title
stringlengths 9
208
| abstract
stringlengths 280
2.36k
| authors
list | published
stringlengths 19
19
| url
stringlengths 33
33
| pdf_url
stringlengths 33
33
| arxiv_id
stringlengths 12
12
|
|---|---|---|---|---|---|---|
Automatic Sensor-free Affect Detection: A Systematic Literature Review
|
Emotions and other affective states play a pivotal role in cognition and,
consequently, the learning process. It is well-established that computer-based
learning environments (CBLEs) that can detect and adapt to students' affective
states can enhance learning outcomes. However, practical constraints often pose
challenges to the deployment of sensor-based affect detection in CBLEs,
particularly for large-scale or long-term applications. As a result,
sensor-free affect detection, which exclusively relies on logs of students'
interactions with CBLEs, emerges as a compelling alternative. This paper
provides a comprehensive literature review on sensor-free affect detection. It
delves into the most frequently identified affective states, the methodologies
and techniques employed for sensor development, the defining attributes of
CBLEs and data samples, as well as key research trends. Despite the field's
evident maturity, demonstrated by the consistent performance of the models and
the application of advanced machine learning techniques, there is ample scope
for future research. Potential areas for further exploration include enhancing
the performance of sensor-free detection models, amassing more samples of
underrepresented emotions, and identifying additional emotions. There is also a
need to refine model development practices and methods. This could involve
comparing the accuracy of various data collection techniques, determining the
optimal granularity of duration, establishing a shared database of action logs
and emotion labels, and making the source code of these models publicly
accessible. Future research should also prioritize the integration of models
into CBLEs for real-time detection, the provision of meaningful interventions
based on detected emotions, and a deeper understanding of the impact of
emotions on learning.
|
[
"Felipe de Morais",
"Diógines Goldoni",
"Tiago Kautzmann",
"Rodrigo da Silva",
"Patricia A. Jaques"
] |
2023-10-11 13:24:27
|
http://arxiv.org/abs/2310.13711v1
|
http://arxiv.org/pdf/2310.13711v1
|
2310.13711v1
|
Deep Learning Predicts Biomarker Status and Discovers Related Histomorphology Characteristics for Low-Grade Glioma
|
Biomarker detection is an indispensable part in the diagnosis and treatment
of low-grade glioma (LGG). However, current LGG biomarker detection methods
rely on expensive and complex molecular genetic testing, for which
professionals are required to analyze the results, and intra-rater variability
is often reported. To overcome these challenges, we propose an interpretable
deep learning pipeline, a Multi-Biomarker Histomorphology Discoverer
(Multi-Beholder) model based on the multiple instance learning (MIL) framework,
to predict the status of five biomarkers in LGG using only hematoxylin and
eosin-stained whole slide images and slide-level biomarker status labels.
Specifically, by incorporating the one-class classification into the MIL
framework, accurate instance pseudo-labeling is realized for instance-level
supervision, which greatly complements the slide-level labels and improves the
biomarker prediction performance. Multi-Beholder demonstrates superior
prediction performance and generalizability for five LGG biomarkers
(AUROC=0.6469-0.9735) in two cohorts (n=607) with diverse races and scanning
protocols. Moreover, the excellent interpretability of Multi-Beholder allows
for discovering the quantitative and qualitative correlations between biomarker
status and histomorphology characteristics. Our pipeline not only provides a
novel approach for biomarker prediction, enhancing the applicability of
molecular treatments for LGG patients but also facilitates the discovery of new
mechanisms in molecular functionality and LGG progression.
|
[
"Zijie Fang",
"Yihan Liu",
"Yifeng Wang",
"Xiangyang Zhang",
"Yang Chen",
"Changjing Cai",
"Yiyang Lin",
"Ying Han",
"Zhi Wang",
"Shan Zeng",
"Hong Shen",
"Jun Tan",
"Yongbing Zhang"
] |
2023-10-11 13:05:33
|
http://arxiv.org/abs/2310.07464v1
|
http://arxiv.org/pdf/2310.07464v1
|
2310.07464v1
|
Uncovering ECG Changes during Healthy Aging using Explainable AI
|
Cardiovascular diseases remain the leading global cause of mortality. This
necessitates a profound understanding of heart aging processes to diagnose
constraints in cardiovascular fitness. Traditionally, most of such insights
have been drawn from the analysis of electrocardiogram (ECG) feature changes of
individuals as they age. However, these features, while informative, may
potentially obscure underlying data relationships. In this paper, we employ a
deep-learning model and a tree-based model to analyze ECG data from a robust
dataset of healthy individuals across varying ages in both raw signals and ECG
feature format. Explainable AI techniques are then used to identify ECG
features or raw signal characteristics are most discriminative for
distinguishing between age groups. Our analysis with tree-based classifiers
reveal age-related declines in inferred breathing rates and identifies notably
high SDANN values as indicative of elderly individuals, distinguishing them
from younger adults. Furthermore, the deep-learning model underscores the
pivotal role of the P-wave in age predictions across all age groups, suggesting
potential changes in the distribution of different P-wave types with age. These
findings shed new light on age-related ECG changes, offering insights that
transcend traditional feature-based approaches.
|
[
"Gabriel Ott",
"Yannik Schaubelt",
"Juan Miguel Lopez Alcaraz",
"Wilhelm Haverkamp",
"Nils Strodthoff"
] |
2023-10-11 13:05:28
|
http://arxiv.org/abs/2310.07463v1
|
http://arxiv.org/pdf/2310.07463v1
|
2310.07463v1
|
Efficient machine-learning surrogates for large-scale geological carbon and energy storage
|
Geological carbon and energy storage are pivotal for achieving net-zero
carbon emissions and addressing climate change. However, they face
uncertainties due to geological factors and operational limitations, resulting
in possibilities of induced seismic events or groundwater contamination. To
overcome these challenges, we propose a specialized machine-learning (ML) model
to manage extensive reservoir models efficiently.
While ML approaches hold promise for geological carbon storage, the
substantial computational resources required for large-scale analysis are the
obstacle. We've developed a method to reduce the training cost for deep neural
operator models, using domain decomposition and a topology embedder to link
spatio-temporal points. This approach allows accurate predictions within the
model's domain, even for untrained data, enhancing ML efficiency for
large-scale geological storage applications.
|
[
"Teeratorn Kadeethum",
"Stephen J. Verzi",
"Hongkyu Yoon"
] |
2023-10-11 13:05:03
|
http://arxiv.org/abs/2310.07461v1
|
http://arxiv.org/pdf/2310.07461v1
|
2310.07461v1
|
ProbTS: A Unified Toolkit to Probe Deep Time-series Forecasting
|
Time-series forecasting serves as a linchpin in a myriad of applications,
spanning various domains. With the growth of deep learning, this arena has
bifurcated into two salient branches: one focuses on crafting specific neural
architectures tailored for time series, and the other harnesses advanced deep
generative models for probabilistic forecasting. While both branches have made
significant progress, their differences across data scenarios, methodological
focuses, and decoding schemes pose profound, yet unexplored, research
questions. To bridge this knowledge chasm, we introduce ProbTS, a pioneering
toolkit developed to synergize and compare these two distinct branches. Endowed
with a unified data module, a modularized model module, and a comprehensive
evaluator module, ProbTS allows us to revisit and benchmark leading methods
from both branches. The scrutiny with ProbTS highlights their distinct
characteristics, relative strengths and weaknesses, and areas that need further
exploration. Our analyses point to new avenues for research, aiming for more
effective time-series forecasting.
|
[
"Jiawen Zhang",
"Xumeng Wen",
"Shun Zheng",
"Jia Li",
"Jiang Bian"
] |
2023-10-11 12:48:45
|
http://arxiv.org/abs/2310.07446v1
|
http://arxiv.org/pdf/2310.07446v1
|
2310.07446v1
|
A Branched Deep Convolutional Network for Forecasting the Occurrence of Hazes in Paris using Meteorological Maps with Different Characteristic Spatial Scales
|
A deep learning platform has been developed to forecast the occurrence of the
low visibility events or hazes. It is trained by using multi-decadal daily
regional maps of various meteorological and hydrological variables as input
features and surface visibility observations as the targets. To better preserve
the characteristic spatial information of different input features for
training, two branched architectures have recently been developed for the case
of Paris hazes. These new architectures have improved the performance of the
network, producing reasonable scores in both validation and a blind forecasting
evaluation using the data of 2021 and 2022 that have not been used in the
training and validation.
|
[
"Chien Wang"
] |
2023-10-11 12:40:07
|
http://arxiv.org/abs/2310.07437v2
|
http://arxiv.org/pdf/2310.07437v2
|
2310.07437v2
|
Generalized Mixture Model for Extreme Events Forecasting in Time Series Data
|
Time Series Forecasting (TSF) is a widely researched topic with broad
applications in weather forecasting, traffic control, and stock price
prediction. Extreme values in time series often significantly impact human and
natural systems, but predicting them is challenging due to their rare
occurrence. Statistical methods based on Extreme Value Theory (EVT) provide a
systematic approach to modeling the distribution of extremes, particularly the
Generalized Pareto (GP) distribution for modeling the distribution of
exceedances beyond a threshold. To overcome the subpar performance of deep
learning in dealing with heavy-tailed data, we propose a novel framework to
enhance the focus on extreme events. Specifically, we propose a Deep Extreme
Mixture Model with Autoencoder (DEMMA) for time series prediction. The model
comprises two main modules: 1) a generalized mixture distribution based on the
Hurdle model and a reparameterized GP distribution form independent of the
extreme threshold, 2) an Autoencoder-based LSTM feature extractor and a
quantile prediction module with a temporal attention mechanism. We demonstrate
the effectiveness of our approach on multiple real-world rainfall datasets.
|
[
"Jincheng Wang",
"Yue Gao"
] |
2023-10-11 12:36:42
|
http://arxiv.org/abs/2310.07435v1
|
http://arxiv.org/pdf/2310.07435v1
|
2310.07435v1
|
HealthWalk: Promoting Health and Mobility through Sensor-Based Rollator Walker Assistance
|
Rollator walkers allow people with physical limitations to increase their
mobility and give them the confidence and independence to participate in
society for longer. However, rollator walker users often have poor posture,
leading to further health problems and, in the worst case, falls. Integrating
sensors into rollator walker designs can help to address this problem and
results in a platform that allows several other interesting use cases. This
paper briefly overviews existing systems and the current research directions
and challenges in this field. We also present our early HealthWalk rollator
walker prototype for data collection with older people, rheumatism, multiple
sclerosis and Parkinson patients, and individuals with visual impairments.
|
[
"Ivanna Kramer",
"Kevin Weirauch",
"Sabine Bauer",
"Mark Oliver Mints",
"Peer Neubert"
] |
2023-10-11 12:36:38
|
http://arxiv.org/abs/2310.07434v1
|
http://arxiv.org/pdf/2310.07434v1
|
2310.07434v1
|
Imitation Learning from Observation with Automatic Discount Scheduling
|
Humans often acquire new skills through observation and imitation. For
robotic agents, learning from the plethora of unlabeled video demonstration
data available on the Internet necessitates imitating the expert without access
to its action, presenting a challenge known as Imitation Learning from
Observations (ILfO). A common approach to tackle ILfO problems is to convert
them into inverse reinforcement learning problems, utilizing a proxy reward
computed from the agent's and the expert's observations. Nonetheless, we
identify that tasks characterized by a progress dependency property pose
significant challenges for such approaches; in these tasks, the agent needs to
initially learn the expert's preceding behaviors before mastering the
subsequent ones. Our investigation reveals that the main cause is that the
reward signals assigned to later steps hinder the learning of initial
behaviors. To address this challenge, we present a novel ILfO framework that
enables the agent to master earlier behaviors before advancing to later ones.
We introduce an Automatic Discount Scheduling (ADS) mechanism that adaptively
alters the discount factor in reinforcement learning during the training phase,
prioritizing earlier rewards initially and gradually engaging later rewards
only when the earlier behaviors have been mastered. Our experiments, conducted
on nine Meta-World tasks, demonstrate that our method significantly outperforms
state-of-the-art methods across all tasks, including those that are unsolvable
by them.
|
[
"Yuyang Liu",
"Weijun Dong",
"Yingdong Hu",
"Chuan Wen",
"Zhao-Heng Yin",
"Chongjie Zhang",
"Yang Gao"
] |
2023-10-11 12:34:39
|
http://arxiv.org/abs/2310.07433v2
|
http://arxiv.org/pdf/2310.07433v2
|
2310.07433v2
|
Non-backtracking Graph Neural Networks
|
The celebrated message-passing updates for graph neural networks allow the
representation of large-scale graphs with local and computationally tractable
updates. However, the local updates suffer from backtracking, i.e., a message
flows through the same edge twice and revisits the previously visited node.
Since the number of message flows increases exponentially with the number of
updates, the redundancy in local updates prevents the graph neural network from
accurately recognizing a particular message flow for downstream tasks. In this
work, we propose to resolve such a redundancy via the non-backtracking graph
neural network (NBA-GNN) that updates a message without incorporating the
message from the previously visited node. We further investigate how NBA-GNN
alleviates the over-squashing of GNNs, and establish a connection between
NBA-GNN and the impressive performance of non-backtracking updates for
stochastic block model recovery. We empirically verify the effectiveness of our
NBA-GNN on long-range graph benchmark and transductive node classification
problems.
|
[
"Seonghyun Park",
"Narae Ryu",
"Gahee Kim",
"Dongyeop Woo",
"Se-Young Yun",
"Sungsoo Ahn"
] |
2023-10-11 12:32:13
|
http://arxiv.org/abs/2310.07430v1
|
http://arxiv.org/pdf/2310.07430v1
|
2310.07430v1
|
Quantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field and CNNs for Stock Return Predictions
|
We propose a time series forecasting method named Quantum Gramian Angular
Field (QGAF). This approach merges the advantages of quantum computing
technology with deep learning, aiming to enhance the precision of time series
classification and forecasting. We successfully transformed stock return time
series data into two-dimensional images suitable for Convolutional Neural
Network (CNN) training by designing specific quantum circuits. Distinct from
the classical Gramian Angular Field (GAF) approach, QGAF's uniqueness lies in
eliminating the need for data normalization and inverse cosine calculations,
simplifying the transformation process from time series data to two-dimensional
images. To validate the effectiveness of this method, we conducted experiments
on datasets from three major stock markets: the China A-share market, the Hong
Kong stock market, and the US stock market. Experimental results revealed that
compared to the classical GAF method, the QGAF approach significantly improved
time series prediction accuracy, reducing prediction errors by an average of
25% for Mean Absolute Error (MAE) and 48% for Mean Squared Error (MSE). This
research confirms the potential and promising prospects of integrating quantum
computing with deep learning techniques in financial time series forecasting.
|
[
"Zhengmeng Xu",
"Hai Lin"
] |
2023-10-11 12:28:52
|
http://arxiv.org/abs/2310.07427v2
|
http://arxiv.org/pdf/2310.07427v2
|
2310.07427v2
|
Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages
|
Plasticity, the ability of a neural network to evolve with new data, is
crucial for high-performance and sample-efficient visual reinforcement learning
(VRL). Although methods like resetting and regularization can potentially
mitigate plasticity loss, the influences of various components within the VRL
framework on the agent's plasticity are still poorly understood. In this work,
we conduct a systematic empirical exploration focusing on three primary
underexplored facets and derive the following insightful conclusions: (1) data
augmentation is essential in maintaining plasticity; (2) the critic's
plasticity loss serves as the principal bottleneck impeding efficient training;
and (3) without timely intervention to recover critic's plasticity in the early
stages, its loss becomes catastrophic. These insights suggest a novel strategy
to address the high replay ratio (RR) dilemma, where exacerbated plasticity
loss hinders the potential improvements of sample efficiency brought by
increased reuse frequency. Rather than setting a static RR for the entire
training process, we propose Adaptive RR, which dynamically adjusts the RR
based on the critic's plasticity level. Extensive evaluations indicate that
Adaptive RR not only avoids catastrophic plasticity loss in the early stages
but also benefits from more frequent reuse in later phases, resulting in
superior sample efficiency.
|
[
"Guozheng Ma",
"Lu Li",
"Sen Zhang",
"Zixuan Liu",
"Zhen Wang",
"Yixin Chen",
"Li Shen",
"Xueqian Wang",
"Dacheng Tao"
] |
2023-10-11 12:05:34
|
http://arxiv.org/abs/2310.07418v1
|
http://arxiv.org/pdf/2310.07418v1
|
2310.07418v1
|
What can knowledge graph alignment gain with Neuro-Symbolic learning approaches?
|
Knowledge Graphs (KG) are the backbone of many data-intensive applications
since they can represent data coupled with its meaning and context. Aligning
KGs across different domains and providers is necessary to afford a fuller and
integrated representation. A severe limitation of current KG alignment (KGA)
algorithms is that they fail to articulate logical thinking and reasoning with
lexical, structural, and semantic data learning. Deep learning models are
increasingly popular for KGA inspired by their good performance in other tasks,
but they suffer from limitations in explainability, reasoning, and data
efficiency. Hybrid neurosymbolic learning models hold the promise of
integrating logical and data perspectives to produce high-quality alignments
that are explainable and support validation through human-centric approaches.
This paper examines the current state of the art in KGA and explores the
potential for neurosymbolic integration, highlighting promising research
directions for combining these fields.
|
[
"Pedro Giesteira Cotovio",
"Ernesto Jimenez-Ruiz",
"Catia Pesquita"
] |
2023-10-11 12:03:19
|
http://arxiv.org/abs/2310.07417v1
|
http://arxiv.org/pdf/2310.07417v1
|
2310.07417v1
|
A Novel Voronoi-based Convolutional Neural Network Framework for Pushing Person Detection in Crowd Videos
|
Analyzing the microscopic dynamics of pushing behavior within crowds can
offer valuable insights into crowd patterns and interactions. By identifying
instances of pushing in crowd videos, a deeper understanding of when, where,
and why such behavior occurs can be achieved. This knowledge is crucial to
creating more effective crowd management strategies, optimizing crowd flow, and
enhancing overall crowd experiences. However, manually identifying pushing
behavior at the microscopic level is challenging, and the existing automatic
approaches cannot detect such microscopic behavior. Thus, this article
introduces a novel automatic framework for identifying pushing in videos of
crowds on a microscopic level. The framework comprises two main components: i)
Feature extraction and ii) Video labeling. In the feature extraction component,
a new Voronoi-based method is developed for determining the local regions
associated with each person in the input video. Subsequently, these regions are
fed into EfficientNetV1B0 Convolutional Neural Network to extract the deep
features of each person over time. In the second component, a combination of a
fully connected layer with a Sigmoid activation function is employed to analyze
these deep features and annotate the individuals involved in pushing within the
video. The framework is trained and evaluated on a new dataset created using
six real-world experiments, including their corresponding ground truths. The
experimental findings indicate that the suggested framework outperforms seven
baseline methods that are employed for comparative analysis purposes.
|
[
"Ahmed Alia",
"Mohammed Maree",
"Mohcine Chraibi",
"Armin Seyfried"
] |
2023-10-11 12:01:52
|
http://arxiv.org/abs/2310.07416v1
|
http://arxiv.org/pdf/2310.07416v1
|
2310.07416v1
|
NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time Series Pretraining
|
Recent research on time-series self-supervised models shows great promise in
learning semantic representations. However, it has been limited to small-scale
datasets, e.g., thousands of temporal sequences. In this work, we make key
technical contributions that are tailored to the numerical properties of
time-series data and allow the model to scale to large datasets, e.g., millions
of temporal sequences. We adopt the Transformer architecture by first
partitioning the input into non-overlapping windows. Each window is then
characterized by its normalized shape and two scalar values denoting the mean
and standard deviation within each window. To embed scalar values that may
possess arbitrary numerical scales to high-dimensional vectors, we propose a
numerically multi-scaled embedding module enumerating all possible scales for
the scalar values. The model undergoes pretraining using the proposed
numerically multi-scaled embedding with a simple contrastive objective on a
large-scale dataset containing over a million sequences. We study its transfer
performance on a number of univariate and multivariate classification
benchmarks. Our method exhibits remarkable improvement against previous
representation learning approaches and establishes the new state of the art,
even compared with domain-specific non-learning-based methods.
|
[
"Chenguo Lin",
"Xumeng Wen",
"Wei Cao",
"Congrui Huang",
"Jiang Bian",
"Stephen Lin",
"Zhirong Wu"
] |
2023-10-11 11:38:18
|
http://arxiv.org/abs/2310.07402v2
|
http://arxiv.org/pdf/2310.07402v2
|
2310.07402v2
|
Deep Kernel and Image Quality Estimators for Optimizing Robotic Ultrasound Controller using Bayesian Optimization
|
Ultrasound is a commonly used medical imaging modality that requires expert
sonographers to manually maneuver the ultrasound probe based on the acquired
image. Autonomous Robotic Ultrasound (A-RUS) is an appealing alternative to
this manual procedure in order to reduce sonographers' workload. The key
challenge to A-RUS is optimizing the ultrasound image quality for the region of
interest across different patients. This requires knowledge of anatomy,
recognition of error sources and precise probe position, orientation and
pressure. Sample efficiency is important while optimizing these parameters
associated with the robotized probe controller. Bayesian Optimization (BO), a
sample-efficient optimization framework, has recently been applied to optimize
the 2D motion of the probe. Nevertheless, further improvements are needed to
improve the sample efficiency for high-dimensional control of the probe. We aim
to overcome this problem by using a neural network to learn a low-dimensional
kernel in BO, termed as Deep Kernel (DK). The neural network of DK is trained
using probe and image data acquired during the procedure. The two image quality
estimators are proposed that use a deep convolution neural network and provide
real-time feedback to the BO. We validated our framework using these two
feedback functions on three urinary bladder phantoms. We obtained over 50%
increase in sample efficiency for 6D control of the robotized probe.
Furthermore, our results indicate that this performance enhancement in BO is
independent of the specific training dataset, demonstrating inter-patient
adaptability.
|
[
"Deepak Raina",
"SH Chandrashekhara",
"Richard Voyles",
"Juan Wachs",
"Subir Kumar Saha"
] |
2023-10-11 11:20:35
|
http://arxiv.org/abs/2310.07392v1
|
http://arxiv.org/pdf/2310.07392v1
|
2310.07392v1
|
Histopathological Image Classification and Vulnerability Analysis using Federated Learning
|
Healthcare is one of the foremost applications of machine learning (ML).
Traditionally, ML models are trained by central servers, which aggregate data
from various distributed devices to forecast the results for newly generated
data. This is a major concern as models can access sensitive user information,
which raises privacy concerns. A federated learning (FL) approach can help
address this issue: A global model sends its copy to all clients who train
these copies, and the clients send the updates (weights) back to it. Over time,
the global model improves and becomes more accurate. Data privacy is protected
during training, as it is conducted locally on the clients' devices.
However, the global model is susceptible to data poisoning. We develop a
privacy-preserving FL technique for a skin cancer dataset and show that the
model is prone to data poisoning attacks. Ten clients train the model, but one
of them intentionally introduces flipped labels as an attack. This reduces the
accuracy of the global model. As the percentage of label flipping increases,
there is a noticeable decrease in accuracy. We use a stochastic gradient
descent optimization algorithm to find the most optimal accuracy for the model.
Although FL can protect user privacy for healthcare diagnostics, it is also
vulnerable to data poisoning, which must be addressed.
|
[
"Sankalp Vyas",
"Amar Nath Patra",
"Raj Mani Shukla"
] |
2023-10-11 10:55:14
|
http://arxiv.org/abs/2310.07380v1
|
http://arxiv.org/pdf/2310.07380v1
|
2310.07380v1
|
Causal Unsupervised Semantic Segmentation
|
Unsupervised semantic segmentation aims to achieve high-quality semantic
grouping without human-labeled annotations. With the advent of self-supervised
pre-training, various frameworks utilize the pre-trained features to train
prediction heads for unsupervised dense prediction. However, a significant
challenge in this unsupervised setup is determining the appropriate level of
clustering required for segmenting concepts. To address it, we propose a novel
framework, CAusal Unsupervised Semantic sEgmentation (CAUSE), which leverages
insights from causal inference. Specifically, we bridge intervention-oriented
approach (i.e., frontdoor adjustment) to define suitable two-step tasks for
unsupervised prediction. The first step involves constructing a concept
clusterbook as a mediator, which represents possible concept prototypes at
different levels of granularity in a discretized form. Then, the mediator
establishes an explicit link to the subsequent concept-wise self-supervised
learning for pixel-level grouping. Through extensive experiments and analyses
on various datasets, we corroborate the effectiveness of CAUSE and achieve
state-of-the-art performance in unsupervised semantic segmentation.
|
[
"Junho Kim",
"Byung-Kwan Lee",
"Yong Man Ro"
] |
2023-10-11 10:54:44
|
http://arxiv.org/abs/2310.07379v1
|
http://arxiv.org/pdf/2310.07379v1
|
2310.07379v1
|
Experimental quantum natural gradient optimization in photonics
|
Variational quantum algorithms (VQAs) combining the advantages of
parameterized quantum circuits and classical optimizers, promise practical
quantum applications in the Noisy Intermediate-Scale Quantum era. The
performance of VQAs heavily depends on the optimization method. Compared with
gradient-free and ordinary gradient descent methods, the quantum natural
gradient (QNG), which mirrors the geometric structure of the parameter space,
can achieve faster convergence and avoid local minima more easily, thereby
reducing the cost of circuit executions. We utilized a fully programmable
photonic chip to experimentally estimate the QNG in photonics for the first
time. We obtained the dissociation curve of the He-H$^+$ cation and achieved
chemical accuracy, verifying the outperformance of QNG optimization on a
photonic device. Our work opens up a vista of utilizing QNG in photonics to
implement practical near-term quantum applications.
|
[
"Yizhi Wang",
"Shichuan Xue",
"Yaxuan Wang",
"Jiangfang Ding",
"Weixu Shi",
"Dongyang Wang",
"Yong Liu",
"Yingwen Liu",
"Xiang Fu",
"Guangyao Huang",
"Anqi Huang",
"Mingtang Deng",
"Junjie Wu"
] |
2023-10-11 10:41:51
|
http://arxiv.org/abs/2310.07371v1
|
http://arxiv.org/pdf/2310.07371v1
|
2310.07371v1
|
Orthogonal Random Features: Explicit Forms and Sharp Inequalities
|
Random features have been introduced to scale up kernel methods via
randomization techniques. In particular, random Fourier features and orthogonal
random features were used to approximate the popular Gaussian kernel. The
former is performed by a random Gaussian matrix and leads exactly to the
Gaussian kernel after averaging. In this work, we analyze the bias and the
variance of the kernel approximation based on orthogonal random features which
makes use of Haar orthogonal matrices. We provide explicit expressions for
these quantities using normalized Bessel functions and derive sharp exponential
bounds supporting the view that orthogonal random features are more informative
than random Fourier features.
|
[
"Nizar Demni",
"Hachem Kadri"
] |
2023-10-11 10:40:43
|
http://arxiv.org/abs/2310.07370v1
|
http://arxiv.org/pdf/2310.07370v1
|
2310.07370v1
|
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model
|
In this paper, we revisit the problem of sparse linear regression in the
local differential privacy (LDP) model. Existing research in the
non-interactive and sequentially local models has focused on obtaining the
lower bounds for the case where the underlying parameter is $1$-sparse, and
extending such bounds to the more general $k$-sparse case has proven to be
challenging. Moreover, it is unclear whether efficient non-interactive LDP
(NLDP) algorithms exist. To address these issues, we first consider the problem
in the $\epsilon$ non-interactive LDP model and provide a lower bound of
$\Omega(\frac{\sqrt{dk\log d}}{\sqrt{n}\epsilon})$ on the $\ell_2$-norm
estimation error for sub-Gaussian data, where $n$ is the sample size and $d$ is
the dimension of the space. We propose an innovative NLDP algorithm, the very
first of its kind for the problem. As a remarkable outcome, this algorithm also
yields a novel and highly efficient estimator as a valuable by-product. Our
algorithm achieves an upper bound of
$\tilde{O}({\frac{d\sqrt{k}}{\sqrt{n}\epsilon}})$ for the estimation error when
the data is sub-Gaussian, which can be further improved by a factor of
$O(\sqrt{d})$ if the server has additional public but unlabeled data. For the
sequentially interactive LDP model, we show a similar lower bound of
$\Omega({\frac{\sqrt{dk}}{\sqrt{n}\epsilon}})$. As for the upper bound, we
rectify a previous method and show that it is possible to achieve a bound of
$\tilde{O}(\frac{k\sqrt{d}}{\sqrt{n}\epsilon})$. Our findings reveal
fundamental differences between the non-private case, central DP model, and
local DP model in the sparse linear regression problem.
|
[
"Liyang Zhu",
"Meng Ding",
"Vaneet Aggarwal",
"Jinhui Xu",
"Di Wang"
] |
2023-10-11 10:34:52
|
http://arxiv.org/abs/2310.07367v1
|
http://arxiv.org/pdf/2310.07367v1
|
2310.07367v1
|
GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning
|
Graph-structured data is ubiquitous in the world which models complex
relationships between objects, enabling various Web applications. Daily
influxes of unlabeled graph data on the Web offer immense potential for these
applications. Graph self-supervised algorithms have achieved significant
success in acquiring generic knowledge from abundant unlabeled graph data.
These pre-trained models can be applied to various downstream Web applications,
saving training time and improving downstream (target) performance. However,
different graphs, even across seemingly similar domains, can differ
significantly in terms of attribute semantics, posing difficulties, if not
infeasibility, for transferring the pre-trained models to downstream tasks.
Concretely speaking, for example, the additional task-specific node information
in downstream tasks (specificity) is usually deliberately omitted so that the
pre-trained representation (transferability) can be leveraged. The trade-off as
such is termed as "transferability-specificity dilemma" in this work. To
address this challenge, we introduce an innovative deployment module coined as
GraphControl, motivated by ControlNet, to realize better graph domain transfer
learning. Specifically, by leveraging universal structural pre-trained models
and GraphControl, we align the input space across various graphs and
incorporate unique characteristics of target data as conditional inputs. These
conditions will be progressively integrated into the model during fine-tuning
or prompt tuning through ControlNet, facilitating personalized deployment.
Extensive experiments show that our method significantly enhances the
adaptability of pre-trained models on target attributed datasets, achieving
1.4-3x performance gain. Furthermore, it outperforms training-from-scratch
methods on target data with a comparable margin and exhibits faster
convergence.
|
[
"Yun Zhu",
"Yaoke Wang",
"Haizhou Shi",
"Zhenshuo Zhang",
"Siliang Tang"
] |
2023-10-11 10:30:49
|
http://arxiv.org/abs/2310.07365v2
|
http://arxiv.org/pdf/2310.07365v2
|
2310.07365v2
|
Diagnosing Bipolar Disorder from 3-D Structural Magnetic Resonance Images Using a Hybrid GAN-CNN Method
|
Bipolar Disorder (BD) is a psychiatric condition diagnosed by repetitive
cycles of hypomania and depression. Since diagnosing BD relies on subjective
behavioral assessments over a long period, a solid diagnosis based on objective
criteria is not straightforward. The current study responded to the described
obstacle by proposing a hybrid GAN-CNN model to diagnose BD from 3-D structural
MRI Images (sMRI). The novelty of this study stems from diagnosing BD from sMRI
samples rather than conventional datasets such as functional MRI (fMRI),
electroencephalography (EEG), and behavioral symptoms while removing the data
insufficiency usually encountered when dealing with sMRI samples. The impact of
various augmentation ratios is also tested using 5-fold cross-validation. Based
on the results, this study obtains an accuracy rate of 75.8%, a sensitivity of
60.3%, and a specificity of 82.5%, which are 3-5% higher than prior work while
utilizing less than 6% sample counts. Next, it is demonstrated that a 2- D
layer-based GAN generator can effectively reproduce complex 3D brain samples, a
more straightforward technique than manual image processing. Lastly, the
optimum augmentation threshold for the current study using 172 sMRI samples is
50%, showing the applicability of the described method for larger sMRI
datasets. In conclusion, it is established that data augmentation using GAN
improves the accuracy of the CNN classifier using sMRI samples, thus developing
more reliable decision support systems to assist practitioners in identifying
BD patients more reliably and in a shorter period
|
[
"Masood Hamed Saghayan",
"Mohammad Hossein Zolfagharnasab",
"Ali Khadem",
"Farzam Matinfar",
"Hassan Rashidi"
] |
2023-10-11 10:17:41
|
http://arxiv.org/abs/2310.07359v1
|
http://arxiv.org/pdf/2310.07359v1
|
2310.07359v1
|
IMITATE: Clinical Prior Guided Hierarchical Vision-Language Pre-training
|
In the field of medical Vision-Language Pre-training (VLP), significant
efforts have been devoted to deriving text and image features from both
clinical reports and associated medical images. However, most existing methods
may have overlooked the opportunity in leveraging the inherent hierarchical
structure of clinical reports, which are generally split into `findings' for
descriptive content and `impressions' for conclusive observation. Instead of
utilizing this rich, structured format, current medical VLP approaches often
simplify the report into either a unified entity or fragmented tokens. In this
work, we propose a novel clinical prior guided VLP framework named IMITATE to
learn the structure information from medical reports with hierarchical
vision-language alignment. The framework derives multi-level visual features
from the chest X-ray (CXR) images and separately aligns these features with the
descriptive and the conclusive text encoded in the hierarchical medical report.
Furthermore, a new clinical-informed contrastive loss is introduced for
cross-modal learning, which accounts for clinical prior knowledge in
formulating sample correlations in contrastive learning. The proposed model,
IMITATE, outperforms baseline VLP methods across six different datasets,
spanning five medical imaging downstream tasks. Comprehensive experimental
results highlight the advantages of integrating the hierarchical structure of
medical reports for vision-language alignment.
|
[
"Che Liu",
"Sibo Cheng",
"Miaojing Shi",
"Anand Shah",
"Wenjia Bai",
"Rossella Arcucci"
] |
2023-10-11 10:12:43
|
http://arxiv.org/abs/2310.07355v1
|
http://arxiv.org/pdf/2310.07355v1
|
2310.07355v1
|
Atom-Motif Contrastive Transformer for Molecular Property Prediction
|
Recently, Graph Transformer (GT) models have been widely used in the task of
Molecular Property Prediction (MPP) due to their high reliability in
characterizing the latent relationship among graph nodes (i.e., the atoms in a
molecule). However, most existing GT-based methods usually explore the basic
interactions between pairwise atoms, and thus they fail to consider the
important interactions among critical motifs (e.g., functional groups consisted
of several atoms) of molecules. As motifs in a molecule are significant
patterns that are of great importance for determining molecular properties
(e.g., toxicity and solubility), overlooking motif interactions inevitably
hinders the effectiveness of MPP. To address this issue, we propose a novel
Atom-Motif Contrastive Transformer (AMCT), which not only explores the
atom-level interactions but also considers the motif-level interactions. Since
the representations of atoms and motifs for a given molecule are actually two
different views of the same instance, they are naturally aligned to generate
the self-supervisory signals for model training. Meanwhile, the same motif can
exist in different molecules, and hence we also employ the contrastive loss to
maximize the representation agreement of identical motifs across different
molecules. Finally, in order to clearly identify the motifs that are critical
in deciding the properties of each molecule, we further construct a
property-aware attention mechanism into our learning framework. Our proposed
AMCT is extensively evaluated on seven popular benchmark datasets, and both
quantitative and qualitative results firmly demonstrate its effectiveness when
compared with the state-of-the-art methods.
|
[
"Wentao Yu",
"Shuo Chen",
"Chen Gong",
"Gang Niu",
"Masashi Sugiyama"
] |
2023-10-11 10:03:10
|
http://arxiv.org/abs/2310.07351v1
|
http://arxiv.org/pdf/2310.07351v1
|
2310.07351v1
|
Fast-ELECTRA for Efficient Pre-training
|
ELECTRA pre-trains language models by detecting tokens in a sequence that
have been replaced by an auxiliary model. Although ELECTRA offers a significant
boost in efficiency, its potential is constrained by the training cost brought
by the auxiliary model. Notably, this model, which is jointly trained with the
main model, only serves to assist the training of the main model and is
discarded post-training. This results in a substantial amount of training cost
being expended in vain. To mitigate this issue, we propose Fast-ELECTRA, which
leverages an existing language model as the auxiliary model. To construct a
learning curriculum for the main model, we smooth its output distribution via
temperature scaling following a descending schedule. Our approach rivals the
performance of state-of-the-art ELECTRA-style pre-training methods, while
significantly eliminating the computation and memory cost brought by the joint
training of the auxiliary model. Our method also reduces the sensitivity to
hyper-parameters and enhances the pre-training stability.
|
[
"Chengyu Dong",
"Liyuan Liu",
"Hao Cheng",
"Jingbo Shang",
"Jianfeng Gao",
"Xiaodong Liu"
] |
2023-10-11 09:55:46
|
http://arxiv.org/abs/2310.07347v1
|
http://arxiv.org/pdf/2310.07347v1
|
2310.07347v1
|
Towards Foundation Models for Learning on Tabular Data
|
Learning on tabular data underpins numerous real-world applications. Despite
considerable efforts in developing effective learning models for tabular data,
current transferable tabular models remain in their infancy, limited by either
the lack of support for direct instruction following in new tasks or the
neglect of acquiring foundational knowledge and capabilities from diverse
tabular datasets. In this paper, we propose Tabular Foundation Models (TabFMs)
to overcome these limitations. TabFMs harness the potential of generative
tabular learning, employing a pre-trained large language model (LLM) as the
base model and fine-tuning it using purpose-designed objectives on an extensive
range of tabular datasets. This approach endows TabFMs with a profound
understanding and universal capabilities essential for learning on tabular
data. Our evaluations underscore TabFM's effectiveness: not only does it
significantly excel in instruction-following tasks like zero-shot and
in-context inference, but it also showcases performance that approaches, and in
instances, even transcends, the renowned yet mysterious closed-source LLMs like
GPT-4. Furthermore, when fine-tuning with scarce data, our model achieves
remarkable efficiency and maintains competitive performance with abundant
training data. Finally, while our results are promising, we also delve into
TabFM's limitations and potential opportunities, aiming to stimulate and
expedite future research on developing more potent TabFMs.
|
[
"Han Zhang",
"Xumeng Wen",
"Shun Zheng",
"Wei Xu",
"Jiang Bian"
] |
2023-10-11 09:37:38
|
http://arxiv.org/abs/2310.07338v2
|
http://arxiv.org/pdf/2310.07338v2
|
2310.07338v2
|
Exploring Social Motion Latent Space and Human Awareness for Effective Robot Navigation in Crowded Environments
|
This work proposes a novel approach to social robot navigation by learning to
generate robot controls from a social motion latent space. By leveraging this
social motion latent space, the proposed method achieves significant
improvements in social navigation metrics such as success rate, navigation
time, and trajectory length while producing smoother (less jerk and angular
deviations) and more anticipatory trajectories. The superiority of the proposed
method is demonstrated through comparison with baseline models in various
scenarios. Additionally, the concept of humans' awareness towards the robot is
introduced into the social robot navigation framework, showing that
incorporating human awareness leads to shorter and smoother trajectories owing
to humans' ability to positively interact with the robot.
|
[
"Junaid Ahmed Ansari",
"Satyajit Tourani",
"Gourav Kumar",
"Brojeshwar Bhowmick"
] |
2023-10-11 09:25:24
|
http://arxiv.org/abs/2310.07335v1
|
http://arxiv.org/pdf/2310.07335v1
|
2310.07335v1
|
An Adversarial Example for Direct Logit Attribution: Memory Management in gelu-4l
|
We provide concrete evidence for memory management in a 4-layer transformer.
Specifically, we identify clean-up behavior, in which model components
consistently remove the output of preceeding components during a forward pass.
Our findings suggest that the interpretability technique Direct Logit
Attribution provides misleading results. We show explicit examples where this
technique is inaccurate, as it does not account for clean-up behavior.
|
[
"James Dao",
"Yeu-Tong Lau",
"Can Rager",
"Jett Janiak"
] |
2023-10-11 09:14:40
|
http://arxiv.org/abs/2310.07325v2
|
http://arxiv.org/pdf/2310.07325v2
|
2310.07325v2
|
Multichannel consecutive data cross-extraction with 1DCNN-attention for diagnosis of power transformer
|
Power transformer plays a critical role in grid infrastructure, and its
diagnosis is paramount for maintaining stable operation. However, the current
methods for transformer diagnosis focus on discrete dissolved gas analysis,
neglecting deep feature extraction of multichannel consecutive data. The
unutilized sequential data contains the significant temporal information
reflecting the transformer condition. In light of this, the structure of
multichannel consecutive data cross-extraction (MCDC) is proposed in this
article in order to comprehensively exploit the intrinsic characteristic and
evaluate the states of transformer. Moreover, for the better accommodation in
scenario of transformer diagnosis, one dimensional convolution neural network
attention (1DCNN-attention) mechanism is introduced and offers a more efficient
solution given the simplified spatial complexity. Finally, the effectiveness of
MCDC and the superior generalization ability, compared with other algorithms,
are validated in experiments conducted on a dataset collected from real
operation cases of power transformer. Additionally, the better stability of
1DCNN-attention has also been certified.
|
[
"Wei Zheng",
"Guogang Zhang",
"Chenchen Zhao",
"Qianqian Zhu"
] |
2023-10-11 09:14:17
|
http://arxiv.org/abs/2310.07323v1
|
http://arxiv.org/pdf/2310.07323v1
|
2310.07323v1
|
On the Impact of Cross-Domain Data on German Language Models
|
Traditionally, large language models have been either trained on general web
crawls or domain-specific data. However, recent successes of generative large
language models, have shed light on the benefits of cross-domain datasets. To
examine the significance of prioritizing data diversity over quality, we
present a German dataset comprising texts from five domains, along with another
dataset aimed at containing high-quality data. Through training a series of
models ranging between 122M and 750M parameters on both datasets, we conduct a
comprehensive benchmark on multiple downstream tasks. Our findings demonstrate
that the models trained on the cross-domain dataset outperform those trained on
quality data alone, leading to improvements up to $4.45\%$ over the previous
state-of-the-art. The models are available at
https://huggingface.co/ikim-uk-essen
|
[
"Amin Dada",
"Aokun Chen",
"Cheng Peng",
"Kaleb E Smith",
"Ahmad Idrissi-Yaghir",
"Constantin Marc Seibold",
"Jianning Li",
"Lars Heiliger",
"Xi Yang",
"Christoph M. Friedrich",
"Daniel Truhn",
"Jan Egger",
"Jiang Bian",
"Jens Kleesiek",
"Yonghui Wu"
] |
2023-10-11 09:09:55
|
http://arxiv.org/abs/2310.07321v2
|
http://arxiv.org/pdf/2310.07321v2
|
2310.07321v2
|
Byzantine-Resilient Decentralized Multi-Armed Bandits
|
In decentralized cooperative multi-armed bandits (MAB), each agent observes a
distinct stream of rewards, and seeks to exchange information with others to
select a sequence of arms so as to minimize its regret. Agents in the
cooperative setting can outperform a single agent running a MAB method such as
Upper-Confidence Bound (UCB) independently. In this work, we study how to
recover such salient behavior when an unknown fraction of the agents can be
Byzantine, that is, communicate arbitrarily wrong information in the form of
reward mean-estimates or confidence sets. This framework can be used to model
attackers in computer networks, instigators of offensive content into
recommender systems, or manipulators of financial markets. Our key contribution
is the development of a fully decentralized resilient upper confidence bound
(UCB) algorithm that fuses an information mixing step among agents with a
truncation of inconsistent and extreme values. This truncation step enables us
to establish that the performance of each normal agent is no worse than the
classic single-agent UCB1 algorithm in terms of regret, and more importantly,
the cumulative regret of all normal agents is strictly better than the
non-cooperative case, provided that each agent has at least 3f+1 neighbors
where f is the maximum possible Byzantine agents in each agent's neighborhood.
Extensions to time-varying neighbor graphs, and minimax lower bounds are
further established on the achievable regret. Experiments corroborate the
merits of this framework in practice.
|
[
"Jingxuan Zhu",
"Alec Koppel",
"Alvaro Velasquez",
"Ji Liu"
] |
2023-10-11 09:09:50
|
http://arxiv.org/abs/2310.07320v1
|
http://arxiv.org/pdf/2310.07320v1
|
2310.07320v1
|
Molecule-Edit Templates for Efficient and Accurate Retrosynthesis Prediction
|
Retrosynthesis involves determining a sequence of reactions to synthesize
complex molecules from simpler precursors. As this poses a challenge in organic
chemistry, machine learning has offered solutions, particularly for predicting
possible reaction substrates for a given target molecule. These solutions
mainly fall into template-based and template-free categories. The former is
efficient but relies on a vast set of predefined reaction patterns, while the
latter, though more flexible, can be computationally intensive and less
interpretable. To address these issues, we introduce METRO (Molecule-Edit
Templates for RetrOsynthesis), a machine-learning model that predicts reactions
using minimal templates - simplified reaction patterns capturing only essential
molecular changes - reducing computational overhead and achieving
state-of-the-art results on standard benchmarks.
|
[
"Mikołaj Sacha",
"Michał Sadowski",
"Piotr Kozakowski",
"Ruard van Workum",
"Stanisław Jastrzębski"
] |
2023-10-11 09:00:02
|
http://arxiv.org/abs/2310.07313v1
|
http://arxiv.org/pdf/2310.07313v1
|
2310.07313v1
|
WiGenAI: The Symphony of Wireless and Generative AI via Diffusion Models
|
Innovative foundation models, such as GPT-3 and stable diffusion models, have
made a paradigm shift in the realm of artificial intelligence (AI) towards
generative AI-based systems. In unison, from data communication and networking
perspective, AI and machine learning (AI/ML) algorithms are envisioned to be
pervasively incorporated into the future generations of wireless communications
systems, highlighting the need for novel AI-native solutions for the emergent
communication scenarios. In this article, we outline the applications of
generative AI in wireless communication systems to lay the foundations for
research in this field. Diffusion-based generative models, as the new
state-of-the-art paradigm of generative models, are introduced, and their
applications in wireless communication systems are discussed. Two case studies
are also presented to showcase how diffusion models can be exploited for the
development of resilient AI-native communication systems. Specifically, we
propose denoising diffusion probabilistic models (DDPM) for a wireless
communication scheme with non-ideal transceivers, where 30% improvement is
achieved in terms of bit error rate. As the second application, DDPMs are
employed at the transmitter to shape the constellation symbols, highlighting a
robust out-of-distribution performance. Finally, future directions and open
issues for the development of generative AI-based wireless systems are
discussed to promote future research endeavors towards wireless generative AI
(WiGenAI).
|
[
"Mehdi Letafati",
"Samad Ali",
"Matti Latva-aho"
] |
2023-10-11 08:57:59
|
http://arxiv.org/abs/2310.07312v2
|
http://arxiv.org/pdf/2310.07312v2
|
2310.07312v2
|
SNOiC: Soft Labeling and Noisy Mixup based Open Intent Classification Model
|
This paper presents a Soft Labeling and Noisy Mixup-based open intent
classification model (SNOiC). Most of the previous works have used
threshold-based methods to identify open intents, which are prone to
overfitting and may produce biased predictions. Additionally, the need for more
available data for an open intent class presents another limitation for these
existing models. SNOiC combines Soft Labeling and Noisy Mixup strategies to
reduce the biasing and generate pseudo-data for open intent class. The
experimental results on four benchmark datasets show that the SNOiC model
achieves a minimum and maximum performance of 68.72\% and 94.71\%,
respectively, in identifying open intents. Moreover, compared to
state-of-the-art models, the SNOiC model improves the performance of
identifying open intents by 0.93\% (minimum) and 12.76\% (maximum). The model's
efficacy is further established by analyzing various parameters used in the
proposed model. An ablation study is also conducted, which involves creating
three model variants to validate the effectiveness of the SNOiC model.
|
[
"Aditi Kanwar",
"Aditi Seetha",
"Satyendra Singh Chouhan",
"Rajdeep Niyogi"
] |
2023-10-11 08:40:06
|
http://arxiv.org/abs/2310.07306v1
|
http://arxiv.org/pdf/2310.07306v1
|
2310.07306v1
|
Beyond Memorization: Violating Privacy Via Inference with Large Language Models
|
Current privacy research on large language models (LLMs) primarily focuses on
the issue of extracting memorized training data. At the same time, models'
inference capabilities have increased drastically. This raises the key question
of whether current LLMs could violate individuals' privacy by inferring
personal attributes from text given at inference time. In this work, we present
the first comprehensive study on the capabilities of pretrained LLMs to infer
personal attributes from text. We construct a dataset consisting of real Reddit
profiles, and show that current LLMs can infer a wide range of personal
attributes (e.g., location, income, sex), achieving up to $85\%$ top-1 and
$95.8\%$ top-3 accuracy at a fraction of the cost ($100\times$) and time
($240\times$) required by humans. As people increasingly interact with
LLM-powered chatbots across all aspects of life, we also explore the emerging
threat of privacy-invasive chatbots trying to extract personal information
through seemingly benign questions. Finally, we show that common mitigations,
i.e., text anonymization and model alignment, are currently ineffective at
protecting user privacy against LLM inference. Our findings highlight that
current LLMs can infer personal data at a previously unattainable scale. In the
absence of working defenses, we advocate for a broader discussion around LLM
privacy implications beyond memorization, striving for a wider privacy
protection.
|
[
"Robin Staab",
"Mark Vero",
"Mislav Balunović",
"Martin Vechev"
] |
2023-10-11 08:32:46
|
http://arxiv.org/abs/2310.07298v1
|
http://arxiv.org/pdf/2310.07298v1
|
2310.07298v1
|
Score Regularized Policy Optimization through Diffusion Behavior
|
Recent developments in offline reinforcement learning have uncovered the
immense potential of diffusion modeling, which excels at representing
heterogeneous behavior policies. However, sampling from diffusion policies is
considerably slow because it necessitates tens to hundreds of iterative
inference steps for one action. To address this issue, we propose to extract an
efficient deterministic inference policy from critic models and pretrained
diffusion behavior models, leveraging the latter to directly regularize the
policy gradient with the behavior distribution's score function during
optimization. Our method enjoys powerful generative capabilities of diffusion
modeling while completely circumventing the computationally intensive and
time-consuming diffusion sampling scheme, both during training and evaluation.
Extensive results on D4RL tasks show that our method boosts action sampling
speed by more than 25 times compared with various leading diffusion-based
methods in locomotion tasks, while still maintaining state-of-the-art
performance.
|
[
"Huayu Chen",
"Cheng Lu",
"Zhengyi Wang",
"Hang Su",
"Jun Zhu"
] |
2023-10-11 08:31:26
|
http://arxiv.org/abs/2310.07297v2
|
http://arxiv.org/pdf/2310.07297v2
|
2310.07297v2
|
BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
|
Recent advancements in biological research leverage the integration of
molecules, proteins, and natural language to enhance drug discovery. However,
current models exhibit several limitations, such as the generation of invalid
molecular SMILES, underutilization of contextual information, and equal
treatment of structured and unstructured knowledge. To address these issues, we
propose $\mathbf{BioT5}$, a comprehensive pre-training framework that enriches
cross-modal integration in biology with chemical knowledge and natural language
associations. $\mathbf{BioT5}$ utilizes SELFIES for $100%$ robust molecular
representations and extracts knowledge from the surrounding context of
bio-entities in unstructured biological literature. Furthermore,
$\mathbf{BioT5}$ distinguishes between structured and unstructured knowledge,
leading to more effective utilization of information. After fine-tuning, BioT5
shows superior performance across a wide range of tasks, demonstrating its
strong capability of capturing underlying relations and properties of
bio-entities. Our code is available at
$\href{https://github.com/QizhiPei/BioT5}{Github}$.
|
[
"Qizhi Pei",
"Wei Zhang",
"Jinhua Zhu",
"Kehan Wu",
"Kaiyuan Gao",
"Lijun Wu",
"Yingce Xia",
"Rui Yan"
] |
2023-10-11 07:57:08
|
http://arxiv.org/abs/2310.07276v2
|
http://arxiv.org/pdf/2310.07276v2
|
2310.07276v2
|
Why Does Sharpness-Aware Minimization Generalize Better Than SGD?
|
The challenge of overfitting, in which the model memorizes the training data
and fails to generalize to test data, has become increasingly significant in
the training of large neural networks. To tackle this challenge,
Sharpness-Aware Minimization (SAM) has emerged as a promising training method,
which can improve the generalization of neural networks even in the presence of
label noise. However, a deep understanding of how SAM works, especially in the
setting of nonlinear neural networks and classification tasks, remains largely
missing. This paper fills this gap by demonstrating why SAM generalizes better
than Stochastic Gradient Descent (SGD) for a certain data model and two-layer
convolutional ReLU networks. The loss landscape of our studied problem is
nonsmooth, thus current explanations for the success of SAM based on the
Hessian information are insufficient. Our result explains the benefits of SAM,
particularly its ability to prevent noise learning in the early stages, thereby
facilitating more effective learning of features. Experiments on both synthetic
and real data corroborate our theory.
|
[
"Zixiang Chen",
"Junkai Zhang",
"Yiwen Kou",
"Xiangning Chen",
"Cho-Jui Hsieh",
"Quanquan Gu"
] |
2023-10-11 07:51:10
|
http://arxiv.org/abs/2310.07269v1
|
http://arxiv.org/pdf/2310.07269v1
|
2310.07269v1
|
RaftFed: A Lightweight Federated Learning Framework for Vehicular Crowd Intelligence
|
Vehicular crowd intelligence (VCI) is an emerging research field. Facilitated
by state-of-the-art vehicular ad-hoc networks and artificial intelligence,
various VCI applications come to place, e.g., collaborative sensing,
positioning, and mapping. The collaborative property of VCI applications
generally requires data to be shared among participants, thus forming
network-wide intelligence. How to fulfill this process without compromising
data privacy remains a challenging issue. Although federated learning (FL) is a
promising tool to solve the problem, adapting conventional FL frameworks to VCI
is nontrivial. First, the centralized model aggregation is unreliable in VCI
because of the existence of stragglers with unfavorable channel conditions.
Second, existing FL schemes are vulnerable to Non-IID data, which is
intensified by the data heterogeneity in VCI. This paper proposes a novel
federated learning framework called RaftFed to facilitate privacy-preserving
VCI. The experimental results show that RaftFed performs better than baselines
regarding communication overhead, model accuracy, and model convergence.
|
[
"Changan Yang",
"Yaxing Chen",
"Yao Zhang",
"Helei Cui",
"Zhiwen Yu",
"Bin Guo",
"Zheng Yan",
"Zijiang Yang"
] |
2023-10-11 07:50:51
|
http://arxiv.org/abs/2310.07268v1
|
http://arxiv.org/pdf/2310.07268v1
|
2310.07268v1
|
Classification of Dysarthria based on the Levels of Severity. A Systematic Review
|
Dysarthria is a neurological speech disorder that can significantly impact
affected individuals' communication abilities and overall quality of life. The
accurate and objective classification of dysarthria and the determination of
its severity are crucial for effective therapeutic intervention. While
traditional assessments by speech-language pathologists (SLPs) are common, they
are often subjective, time-consuming, and can vary between practitioners.
Emerging machine learning-based models have shown the potential to provide a
more objective dysarthria assessment, enhancing diagnostic accuracy and
reliability. This systematic review aims to comprehensively analyze current
methodologies for classifying dysarthria based on severity levels.
Specifically, this review will focus on determining the most effective set and
type of features that can be used for automatic patient classification and
evaluating the best AI techniques for this purpose. We will systematically
review the literature on the automatic classification of dysarthria severity
levels. Sources of information will include electronic databases and grey
literature. Selection criteria will be established based on relevance to the
research questions. Data extraction will include methodologies used, the type
of features extracted for classification, and AI techniques employed. The
findings of this systematic review will contribute to the current understanding
of dysarthria classification, inform future research, and support the
development of improved diagnostic tools. The implications of these findings
could be significant in advancing patient care and improving therapeutic
outcomes for individuals affected by dysarthria.
|
[
"Afnan Al-Ali",
"Somaya Al-Maadeed",
"Moutaz Saleh",
"Rani Chinnappa Naidu",
"Zachariah C Alex",
"Prakash Ramachandran",
"Rajeev Khoodeeram",
"Rajesh Kumar M"
] |
2023-10-11 07:40:46
|
http://arxiv.org/abs/2310.07264v1
|
http://arxiv.org/pdf/2310.07264v1
|
2310.07264v1
|
Deep ReLU networks and high-order finite element methods II: Chebyshev emulation
|
Expression rates and stability in Sobolev norms of deep ReLU neural networks
(NNs) in terms of the number of parameters defining the NN for continuous,
piecewise polynomial functions, on arbitrary, finite partitions $\mathcal{T}$
of a bounded interval $(a,b)$ are addressed. Novel constructions of ReLU NN
surrogates encoding the approximated functions in terms of Chebyshev polynomial
expansion coefficients are developed. Chebyshev coefficients can be computed
easily from the values of the function in the Clenshaw--Curtis points using the
inverse fast Fourier transform. Bounds on expression rates and stability that
are superior to those of constructions based on ReLU NN emulations of monomials
considered in [Opschoor, Petersen, Schwab, 2020] are obtained. All emulation
bounds are explicit in terms of the (arbitrary) partition of the interval, the
target emulation accuracy and the polynomial degree in each element of the
partition. ReLU NN emulation error estimates are provided for various classes
of functions and norms, commonly encountered in numerical analysis. In
particular, we show exponential ReLU emulation rate bounds for analytic
functions with point singularities and develop an interface between Chebfun
approximations and constructive ReLU NN emulations.
|
[
"Joost A. A. Opschoor",
"Christoph Schwab"
] |
2023-10-11 07:38:37
|
http://arxiv.org/abs/2310.07261v1
|
http://arxiv.org/pdf/2310.07261v1
|
2310.07261v1
|
ADMEOOD: Out-of-Distribution Benchmark for Drug Property Prediction
|
Obtaining accurate and valid information for drug molecules is a crucial and
challenging task. However, chemical knowledge and information have been
accumulated over the past 100 years from various regions, laboratories, and
experimental purposes. Little has been explored in terms of the
out-of-distribution (OOD) problem with noise and inconsistency, which may lead
to weak robustness and unsatisfied performance. This study proposes a novel
benchmark ADMEOOD, a systematic OOD dataset curator and benchmark specifically
designed for drug property prediction. ADMEOOD obtained 27 ADME (Absorption,
Distribution, Metabolism, Excretion) drug properties from Chembl and relevant
literature. Additionally, it includes two kinds of OOD data shifts: Noise Shift
and Concept Conflict Drift (CCD). Noise Shift responds to the noise level by
categorizing the environment into different confidence levels. On the other
hand, CCD describes the data which has inconsistent label among the original
data. Finally, it tested on a variety of domain generalization models, and the
experimental results demonstrate the effectiveness of the proposed partition
method in ADMEOOD: ADMEOOD demonstrates a significant difference performance
between in-distribution and out-of-distribution data. Moreover, ERM (Empirical
Risk Minimization) and other models exhibit distinct trends in performance
across different domains and measurement types.
|
[
"Shuoying Wei",
"Xinlong Wen",
"Lida Zhu",
"Songquan Li",
"Rongbo Zhu"
] |
2023-10-11 07:30:18
|
http://arxiv.org/abs/2310.07253v1
|
http://arxiv.org/pdf/2310.07253v1
|
2310.07253v1
|
A Comparative Study of Pre-trained CNNs and GRU-Based Attention for Image Caption Generation
|
Image captioning is a challenging task involving generating a textual
description for an image using computer vision and natural language processing
techniques. This paper proposes a deep neural framework for image caption
generation using a GRU-based attention mechanism. Our approach employs multiple
pre-trained convolutional neural networks as the encoder to extract features
from the image and a GRU-based language model as the decoder to generate
descriptive sentences. To improve performance, we integrate the Bahdanau
attention model with the GRU decoder to enable learning to focus on specific
image parts. We evaluate our approach using the MSCOCO and Flickr30k datasets
and show that it achieves competitive scores compared to state-of-the-art
methods. Our proposed framework can bridge the gap between computer vision and
natural language and can be extended to specific domains.
|
[
"Rashid Khan",
"Bingding Huang",
"Haseeb Hassan",
"Asim Zaman",
"Zhongfu Ye"
] |
2023-10-11 07:30:01
|
http://arxiv.org/abs/2310.07252v1
|
http://arxiv.org/pdf/2310.07252v1
|
2310.07252v1
|
Synthesizing Missing MRI Sequences from Available Modalities using Generative Adversarial Networks in BraTS Dataset
|
Glioblastoma is a highly aggressive and lethal form of brain cancer. Magnetic
resonance imaging (MRI) plays a significant role in the diagnosis, treatment
planning, and follow-up of glioblastoma patients due to its non-invasive and
radiation-free nature. The International Brain Tumor Segmentation (BraTS)
challenge has contributed to generating numerous AI algorithms to accurately
and efficiently segment glioblastoma sub-compartments using four structural
(T1, T1Gd, T2, T2-FLAIR) MRI scans. However, these four MRI sequences may not
always be available. To address this issue, Generative Adversarial Networks
(GANs) can be used to synthesize the missing MRI sequences. In this paper, we
implement and utilize an open-source GAN approach that takes any three MRI
sequences as input to generate the missing fourth structural sequence. Our
proposed approach is contributed to the community-driven generally nuanced deep
learning framework (GaNDLF) and demonstrates promising results in synthesizing
high-quality and realistic MRI sequences, enabling clinicians to improve their
diagnostic capabilities and support the application of AI methods to brain
tumor MRI quantification.
|
[
"Ibrahim Ethem Hamamci"
] |
2023-10-11 07:27:28
|
http://arxiv.org/abs/2310.07250v2
|
http://arxiv.org/pdf/2310.07250v2
|
2310.07250v2
|
Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs
|
Visual crowd counting estimates the density of the crowd using deep learning
models such as convolution neural networks (CNNs). The performance of the model
heavily relies on the quality of the training data that constitutes crowd
images. In harsh weather such as fog, dust, and low light conditions, the
inference performance may severely degrade on the noisy and blur images. In
this paper, we propose the use of Pix2Pix generative adversarial network (GAN)
to first denoise the crowd images prior to passing them to the counting model.
A Pix2Pix network is trained using synthetic noisy images generated from
original crowd images and then the pretrained generator is then used in the
inference engine to estimate the crowd density in unseen, noisy crowd images.
The performance is tested on JHU-Crowd dataset to validate the significance of
the proposed method particularly when high reliability and accuracy are
required.
|
[
"Muhammad Asif Khan",
"Hamid Menouar",
"Ridha Hamila"
] |
2023-10-11 07:22:37
|
http://arxiv.org/abs/2310.07245v1
|
http://arxiv.org/pdf/2310.07245v1
|
2310.07245v1
|
Surrogate modeling for stochastic crack growth processes in structural health monitoring applications
|
Fatigue crack growth is one of the most common types of deterioration in
metal structures with significant implications on their reliability. Recent
advances in Structural Health Monitoring (SHM) have motivated the use of
structural response data to predict future crack growth under uncertainty, in
order to enable a transition towards predictive maintenance. Accurately
representing different sources of uncertainty in stochastic crack growth (SCG)
processes is a non-trivial task. The present work builds on previous research
on physics-based SCG modeling under both material and load-related uncertainty.
The aim here is to construct computationally efficient, probabilistic surrogate
models for SCG processes that successfully encode these different sources of
uncertainty. An approach inspired by latent variable modeling is employed that
utilizes Gaussian Process (GP) regression models to enable the surrogates to be
used to generate prior distributions for different Bayesian SHM tasks as the
application of interest. Implementation is carried out in a numerical setting
and model performance is assessed for two fundamental crack SHM problems;
namely crack length monitoring (damage quantification) and crack growth
monitoring (damage prognosis).
|
[
"Nicholas E. Silionis",
"Konstantinos N. Anyfantis"
] |
2023-10-11 07:13:16
|
http://arxiv.org/abs/2310.07241v1
|
http://arxiv.org/pdf/2310.07241v1
|
2310.07241v1
|
CacheGen: Fast Context Loading for Language Model Applications
|
As large language models (LLMs) take on more complex tasks, their inputs
incorporate longer contexts to respond to questions that require domain
knowledge or user-specific conversational histories. Yet, using long contexts
poses a challenge for responsive LLM systems, as nothing can be generated until
all the contexts are fetched to and processed by the LLM. Existing systems
optimize only the computation delay in context processing (e.g., by caching
intermediate key-value features of the text context) but often cause longer
network delays in context fetching (e.g., key-value features consume orders of
magnitude larger bandwidth than the text context).
This paper presents CacheGen to minimize the delays in fetching and
processing contexts for LLMs. CacheGen reduces the bandwidth needed for
transmitting long contexts' key-value (KV) features through a novel encoder
that compresses KV features into more compact bitstream representations. The
encoder combines adaptive quantization with a tailored arithmetic coder, taking
advantage of the KV features' distributional properties, such as locality
across tokens. Furthermore, CacheGen minimizes the total delay in fetching and
processing a context by using a controller that determines when to load the
context as compressed KV features or raw text and picks the appropriate
compression level if loaded as KV features. We test CacheGen on three models of
various sizes and three datasets of different context lengths. Compared to
recent methods that handle long contexts, CacheGen reduces bandwidth usage by
3.7-4.3x and the total delay in fetching and processing contexts by 2.7-3x
while maintaining similar LLM performance on various tasks as loading the text
contexts.
|
[
"Yuhan Liu",
"Hanchen Li",
"Kuntai Du",
"Jiayi Yao",
"Yihua Cheng",
"Yuyang Huang",
"Shan Lu",
"Michael Maire",
"Henry Hoffmann",
"Ari Holtzman",
"Ganesh Ananthanarayanan",
"Junchen Jiang"
] |
2023-10-11 07:08:20
|
http://arxiv.org/abs/2310.07240v1
|
http://arxiv.org/pdf/2310.07240v1
|
2310.07240v1
|
Are GATs Out of Balance?
|
While the expressive power and computational capabilities of graph neural
networks (GNNs) have been theoretically studied, their optimization and
learning dynamics, in general, remain largely unexplored. Our study undertakes
the Graph Attention Network (GAT), a popular GNN architecture in which a node's
neighborhood aggregation is weighted by parameterized attention coefficients.
We derive a conservation law of GAT gradient flow dynamics, which explains why
a high portion of parameters in GATs with standard initialization struggle to
change during training. This effect is amplified in deeper GATs, which perform
significantly worse than their shallow counterparts. To alleviate this problem,
we devise an initialization scheme that balances the GAT network. Our approach
i) allows more effective propagation of gradients and in turn enables
trainability of deeper networks, and ii) attains a considerable speedup in
training and convergence time in comparison to the standard initialization. Our
main theorem serves as a stepping stone to studying the learning dynamics of
positive homogeneous models with attention mechanisms.
|
[
"Nimrah Mustafa",
"Aleksandar Bojchevski",
"Rebekka Burkholz"
] |
2023-10-11 06:53:05
|
http://arxiv.org/abs/2310.07235v1
|
http://arxiv.org/pdf/2310.07235v1
|
2310.07235v1
|
Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality
|
Prompt-based continual learning is an emerging direction in leveraging
pre-trained knowledge for downstream continual learning, and has almost reached
the performance pinnacle under supervised pre-training. However, our empirical
research reveals that the current strategies fall short of their full potential
under the more realistic self-supervised pre-training, which is essential for
handling vast quantities of unlabeled data in practice. This is largely due to
the difficulty of task-specific knowledge being incorporated into instructed
representations via prompt parameters and predicted by uninstructed
representations at test time. To overcome the exposed sub-optimality, we
conduct a theoretical analysis of the continual learning objective in the
context of pre-training, and decompose it into hierarchical components:
within-task prediction, task-identity inference, and task-adaptive prediction.
Following these empirical and theoretical insights, we propose Hierarchical
Decomposition (HiDe-)Prompt, an innovative approach that explicitly optimizes
the hierarchical components with an ensemble of task-specific prompts and
statistics of both uninstructed and instructed representations, further with
the coordination of a contrastive regularization strategy. Our extensive
experiments demonstrate the superior performance of HiDe-Prompt and its
robustness to pre-training paradigms in continual learning (e.g., up to 15.01%
and 9.61% lead on Split CIFAR-100 and Split ImageNet-R, respectively). Our code
is available at \url{https://github.com/thu-ml/HiDe-Prompt}.
|
[
"Liyuan Wang",
"Jingyi Xie",
"Xingxing Zhang",
"Mingyi Huang",
"Hang Su",
"Jun Zhu"
] |
2023-10-11 06:51:46
|
http://arxiv.org/abs/2310.07234v1
|
http://arxiv.org/pdf/2310.07234v1
|
2310.07234v1
|
Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment
|
Pocket representations play a vital role in various biomedical applications,
such as druggability estimation, ligand affinity prediction, and de novo drug
design. While existing geometric features and pretrained representations have
demonstrated promising results, they usually treat pockets independent of
ligands, neglecting the fundamental interactions between them. However, the
limited pocket-ligand complex structures available in the PDB database (less
than 100 thousand non-redundant pairs) hampers large-scale pretraining
endeavors for interaction modeling. To address this constraint, we propose a
novel pocket pretraining approach that leverages knowledge from high-resolution
atomic protein structures, assisted by highly effective pretrained small
molecule representations. By segmenting protein structures into drug-like
fragments and their corresponding pockets, we obtain a reasonable simulation of
ligand-receptor interactions, resulting in the generation of over 5 million
complexes. Subsequently, the pocket encoder is trained in a contrastive manner
to align with the representation of pseudo-ligand furnished by some pretrained
small molecule encoders. Our method, named ProFSA, achieves state-of-the-art
performance across various tasks, including pocket druggability prediction,
pocket matching, and ligand binding affinity prediction. Notably, ProFSA
surpasses other pretraining methods by a substantial margin. Moreover, our work
opens up a new avenue for mitigating the scarcity of protein-ligand complex
data through the utilization of high-quality and diverse protein structure
databases.
|
[
"Bowen Gao",
"Yinjun Jia",
"Yuanle Mo",
"Yuyan Ni",
"Weiying Ma",
"Zhiming Ma",
"Yanyan Lan"
] |
2023-10-11 06:36:23
|
http://arxiv.org/abs/2310.07229v1
|
http://arxiv.org/pdf/2310.07229v1
|
2310.07229v1
|
Deep Learning for blind spectral unmixing of LULC classes with MODIS multispectral time series and ancillary data
|
Remotely sensed data are dominated by mixed Land Use and Land Cover (LULC)
types. Spectral unmixing is a technique to extract information from mixed
pixels into their constituent LULC types and corresponding abundance fractions.
Traditionally, solving this task has relied on either classical methods that
require prior knowledge of endmembers or machine learning methods that avoid
explicit endmembers calculation, also known as blind spectral unmixing (BSU).
Most BSU studies based on Deep Learning (DL) focus on one time-step
hyperspectral data, yet its acquisition remains quite costly compared with
multispectral data. To our knowledge, here we provide the first study on BSU of
LULC classes using multispectral time series data with DL models. We further
boost the performance of a Long-Short Term Memory (LSTM)-based model by
incorporating geographic plus topographic (geo-topographic) and climatic
ancillary information. Our experiments show that combining spectral-temporal
input data together with geo-topographic and climatic information substantially
improves the abundance estimation of LULC classes in mixed pixels. To carry out
this study, we built a new labeled dataset of the region of Andalusia (Spain)
with monthly multispectral time series of pixels for the year 2013 from MODIS
at 460m resolution, for two hierarchical levels of LULC classes, named
Andalusia MultiSpectral MultiTemporal Unmixing (Andalusia-MSMTU). This dataset
provides, at the pixel level, a multispectral time series plus ancillary
information annotated with the abundance of each LULC class inside each pixel.
The dataset and code are available to the public.
|
[
"José Rodríguez-Ortega",
"Rohaifa Khaldi",
"Domingo Alcaraz-Segura",
"Siham Tabik"
] |
2023-10-11 06:13:50
|
http://arxiv.org/abs/2310.07223v1
|
http://arxiv.org/pdf/2310.07223v1
|
2310.07223v1
|
Using Learnable Physics for Real-Time Exercise Form Recommendations
|
Good posture and form are essential for safe and productive exercising. Even
in gym settings, trainers may not be readily available for feedback.
Rehabilitation therapies and fitness workouts can thus benefit from recommender
systems that provide real-time evaluation. In this paper, we present an
algorithmic pipeline that can diagnose problems in exercise techniques and
offer corrective recommendations, with high sensitivity and specificity in
real-time. We use MediaPipe for pose recognition, count repetitions using
peak-prominence detection, and use a learnable physics simulator to track
motion evolution for each exercise. A test video is diagnosed based on
deviations from the prototypical learned motion using statistical learning. The
system is evaluated on six full and upper body exercises. These real-time
recommendations, counseled via low-cost equipment like smartphones, will allow
exercisers to rectify potential mistakes making self-practice feasible while
reducing the risk of workout injuries.
|
[
"Abhishek Jaiswal",
"Gautam Chauhan",
"Nisheeth Srivastava"
] |
2023-10-11 06:11:11
|
http://arxiv.org/abs/2310.07221v1
|
http://arxiv.org/pdf/2310.07221v1
|
2310.07221v1
|
COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL
|
Dyna-style model-based reinforcement learning contains two phases: model
rollouts to generate sample for policy learning and real environment
exploration using current policy for dynamics model learning. However, due to
the complex real-world environment, it is inevitable to learn an imperfect
dynamics model with model prediction error, which can further mislead policy
learning and result in sub-optimal solutions. In this paper, we propose
$\texttt{COPlanner}$, a planning-driven framework for model-based methods to
address the inaccurately learned dynamics model problem with conservative model
rollouts and optimistic environment exploration. $\texttt{COPlanner}$ leverages
an uncertainty-aware policy-guided model predictive control (UP-MPC) component
to plan for multi-step uncertainty estimation. This estimated uncertainty then
serves as a penalty during model rollouts and as a bonus during real
environment exploration respectively, to choose actions. Consequently,
$\texttt{COPlanner}$ can avoid model uncertain regions through conservative
model rollouts, thereby alleviating the influence of model error.
Simultaneously, it explores high-reward model uncertain regions to reduce model
error actively through optimistic real environment exploration.
$\texttt{COPlanner}$ is a plug-and-play framework that can be applied to any
dyna-style model-based methods. Experimental results on a series of
proprioceptive and visual continuous control tasks demonstrate that both sample
efficiency and asymptotic performance of strong model-based methods are
significantly improved combined with $\texttt{COPlanner}$.
|
[
"Xiyao Wang",
"Ruijie Zheng",
"Yanchao Sun",
"Ruonan Jia",
"Wichayaporn Wongkamjan",
"Huazhe Xu",
"Furong Huang"
] |
2023-10-11 06:10:07
|
http://arxiv.org/abs/2310.07220v1
|
http://arxiv.org/pdf/2310.07220v1
|
2310.07220v1
|
Improved Membership Inference Attacks Against Language Classification Models
|
Artificial intelligence systems are prevalent in everyday life, with use
cases in retail, manufacturing, health, and many other fields. With the rise in
AI adoption, associated risks have been identified, including privacy risks to
the people whose data was used to train models. Assessing the privacy risks of
machine learning models is crucial to enabling knowledgeable decisions on
whether to use, deploy, or share a model. A common approach to privacy risk
assessment is to run one or more known attacks against the model and measure
their success rate. We present a novel framework for running membership
inference attacks against classification models. Our framework takes advantage
of the ensemble method, generating many specialized attack models for different
subsets of the data. We show that this approach achieves higher accuracy than
either a single attack model or an attack model per class label, both on
classical and language classification tasks.
|
[
"Shlomit Shachor",
"Natalia Razinkov",
"Abigail Goldsteen"
] |
2023-10-11 06:09:48
|
http://arxiv.org/abs/2310.07219v1
|
http://arxiv.org/pdf/2310.07219v1
|
2310.07219v1
|
Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devices
|
The rapid proliferation of computing domains relying on Internet of Things
(IoT) devices has created a pressing need for efficient and accurate
deep-learning (DL) models that can run on low-power devices. However,
traditional DL models tend to be too complex and computationally intensive for
typical IoT end-nodes. To address this challenge, Neural Architecture Search
(NAS) has emerged as a popular design automation technique for co-optimizing
the accuracy and complexity of deep neural networks. Nevertheless, existing NAS
techniques require many iterations to produce a network that adheres to
specific hardware constraints, such as the maximum memory available on the
hardware or the maximum latency allowed by the target application. In this
work, we propose a novel approach to incorporate multiple constraints into
so-called Differentiable NAS optimization methods, which allows the generation,
in a single shot, of a model that respects user-defined constraints on both
memory and latency in a time comparable to a single standard training. The
proposed approach is evaluated on five IoT-relevant benchmarks, including the
MLPerf Tiny suite and Tiny ImageNet, demonstrating that, with a single search,
it is possible to reduce memory and latency by 87.4% and 54.2%, respectively
(as defined by our targets), while ensuring non-inferior accuracy on
state-of-the-art hand-tuned deep neural networks for TinyML.
|
[
"Alessio Burrello",
"Matteo Risso",
"Beatrice Alessandra Motetti",
"Enrico Macii",
"Luca Benini",
"Daniele Jahier Pagliari"
] |
2023-10-11 06:09:14
|
http://arxiv.org/abs/2310.07217v1
|
http://arxiv.org/pdf/2310.07217v1
|
2310.07217v1
|
Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processes
|
Learning the distribution of data on Riemannian manifolds is crucial for
modeling data from non-Euclidean space, which is required by many applications
from diverse scientific fields. Yet, existing generative models on manifolds
suffer from expensive divergence computation or rely on approximations of heat
kernel. These limitations restrict their applicability to simple geometries and
hinder scalability to high dimensions. In this work, we introduce the
Riemannian Diffusion Mixture, a principled framework for building a generative
process on manifolds as a mixture of endpoint-conditioned diffusion processes
instead of relying on the denoising approach of previous diffusion models, for
which the generative process is characterized by its drift guiding toward the
most probable endpoint with respect to the geometry of the manifold. We further
propose a simple yet efficient training objective for learning the mixture
process, that is readily applicable to general manifolds. Our method
outperforms previous generative models on various manifolds while scaling to
high dimensions and requires a dramatically reduced number of in-training
simulation steps for general manifolds.
|
[
"Jaehyeong Jo",
"Sung Ju Hwang"
] |
2023-10-11 06:04:40
|
http://arxiv.org/abs/2310.07216v1
|
http://arxiv.org/pdf/2310.07216v1
|
2310.07216v1
|
Bridging the Gap between Newton-Raphson Method and Regularized Policy Iteration
|
Regularization is one of the most important techniques in reinforcement
learning algorithms. The well-known soft actor-critic algorithm is a special
case of regularized policy iteration where the regularizer is chosen as Shannon
entropy. Despite some empirical success of regularized policy iteration, its
theoretical underpinnings remain unclear. This paper proves that regularized
policy iteration is strictly equivalent to the standard Newton-Raphson method
in the condition of smoothing out Bellman equation with strongly convex
functions. This equivalence lays the foundation of a unified analysis for both
global and local convergence behaviors of regularized policy iteration. We
prove that regularized policy iteration has global linear convergence with the
rate being $\gamma$ (discount factor). Furthermore, this algorithm converges
quadratically once it enters a local region around the optimal value. We also
show that a modified version of regularized policy iteration, i.e., with
finite-step policy evaluation, is equivalent to inexact Newton method where the
Newton iteration formula is solved with truncated iterations. We prove that the
associated algorithm achieves an asymptotic linear convergence rate of
$\gamma^M$ in which $M$ denotes the number of steps carried out in policy
evaluation. Our results take a solid step towards a better understanding of the
convergence properties of regularized policy iteration algorithms.
|
[
"Zeyang Li",
"Chuxiong Hu",
"Yunan Wang",
"Guojian Zhan",
"Jie Li",
"Shengbo Eben Li"
] |
2023-10-11 05:55:20
|
http://arxiv.org/abs/2310.07211v1
|
http://arxiv.org/pdf/2310.07211v1
|
2310.07211v1
|
Robust Safe Reinforcement Learning under Adversarial Disturbances
|
Safety is a primary concern when applying reinforcement learning to
real-world control tasks, especially in the presence of external disturbances.
However, existing safe reinforcement learning algorithms rarely account for
external disturbances, limiting their applicability and robustness in practice.
To address this challenge, this paper proposes a robust safe reinforcement
learning framework that tackles worst-case disturbances. First, this paper
presents a policy iteration scheme to solve for the robust invariant set, i.e.,
a subset of the safe set, where persistent safety is only possible for states
within. The key idea is to establish a two-player zero-sum game by leveraging
the safety value function in Hamilton-Jacobi reachability analysis, in which
the protagonist (i.e., control inputs) aims to maintain safety and the
adversary (i.e., external disturbances) tries to break down safety. This paper
proves that the proposed policy iteration algorithm converges monotonically to
the maximal robust invariant set. Second, this paper integrates the proposed
policy iteration scheme into a constrained reinforcement learning algorithm
that simultaneously synthesizes the robust invariant set and uses it for
constrained policy optimization. This algorithm tackles both optimality and
safety, i.e., learning a policy that attains high rewards while maintaining
safety under worst-case disturbances. Experiments on classic control tasks show
that the proposed method achieves zero constraint violation with learned
worst-case adversarial disturbances, while other baseline algorithms violate
the safety constraints substantially. Our proposed method also attains
comparable performance as the baselines even in the absence of the adversary.
|
[
"Zeyang Li",
"Chuxiong Hu",
"Shengbo Eben Li",
"Jia Cheng",
"Yunan Wang"
] |
2023-10-11 05:34:46
|
http://arxiv.org/abs/2310.07207v1
|
http://arxiv.org/pdf/2310.07207v1
|
2310.07207v1
|
State of the Art on Diffusion Models for Visual Computing
|
The field of visual computing is rapidly advancing due to the emergence of
generative artificial intelligence (AI), which unlocks unprecedented
capabilities for the generation, editing, and reconstruction of images, videos,
and 3D scenes. In these domains, diffusion models are the generative AI
architecture of choice. Within the last year alone, the literature on
diffusion-based tools and applications has seen exponential growth and relevant
papers are published across the computer graphics, computer vision, and AI
communities with new works appearing daily on arXiv. This rapid growth of the
field makes it difficult to keep up with all recent developments. The goal of
this state-of-the-art report (STAR) is to introduce the basic mathematical
concepts of diffusion models, implementation details and design choices of the
popular Stable Diffusion model, as well as overview important aspects of these
generative AI tools, including personalization, conditioning, inversion, among
others. Moreover, we give a comprehensive overview of the rapidly growing
literature on diffusion-based generation and editing, categorized by the type
of generated medium, including 2D images, videos, 3D objects, locomotion, and
4D scenes. Finally, we discuss available datasets, metrics, open challenges,
and social implications. This STAR provides an intuitive starting point to
explore this exciting topic for researchers, artists, and practitioners alike.
|
[
"Ryan Po",
"Wang Yifan",
"Vladislav Golyanik",
"Kfir Aberman",
"Jonathan T. Barron",
"Amit H. Bermano",
"Eric Ryan Chan",
"Tali Dekel",
"Aleksander Holynski",
"Angjoo Kanazawa",
"C. Karen Liu",
"Lingjie Liu",
"Ben Mildenhall",
"Matthias Nießner",
"Björn Ommer",
"Christian Theobalt",
"Peter Wonka",
"Gordon Wetzstein"
] |
2023-10-11 05:32:29
|
http://arxiv.org/abs/2310.07204v1
|
http://arxiv.org/pdf/2310.07204v1
|
2310.07204v1
|
Boosting Learning for LDPC Codes to Improve the Error-Floor Performance
|
Low-density parity-check (LDPC) codes have been successfully commercialized
in communication systems due to their strong error correction ability and
simple decoding process. However, the error-floor phenomenon of LDPC codes, in
which the error rate stops decreasing rapidly at a certain level, poses
challenges in achieving extremely low error rates and the application of LDPC
codes in scenarios demanding ultra high reliability. In this work, we propose
training methods to optimize neural min-sum (NMS) decoders that are robust to
the error-floor. Firstly, by leveraging the boosting learning technique of
ensemble networks, we divide the decoding network into two networks and train
the post network to be specialized for uncorrected codewords that failed in the
first network. Secondly, to address the vanishing gradient issue in training,
we introduce a block-wise training schedule that locally trains a block of
weights while retraining the preceding block. Lastly, we show that assigning
different weights to unsatisfied check nodes effectively lowers the error-floor
with a minimal number of weights. By applying these training methods to
standard LDPC codes, we achieve the best error-floor performance compared to
other decoding methods. The proposed NMS decoder, optimized solely through
novel training methods without additional modules, can be implemented into
current LDPC decoders without incurring extra hardware costs. The source code
is available at https://github.com/ghy1228/LDPC_Error_Floor.
|
[
"Hee-Youl Kwak",
"Dae-Young Yun",
"Yongjune Kim",
"Sang-Hyo Kim",
"Jong-Seon No"
] |
2023-10-11 05:05:40
|
http://arxiv.org/abs/2310.07194v1
|
http://arxiv.org/pdf/2310.07194v1
|
2310.07194v1
|
Neural networks: deep, shallow, or in between?
|
We give estimates from below for the error of approximation of a compact
subset from a Banach space by the outputs of feed-forward neural networks with
width W, depth l and Lipschitz activation functions. We show that, modulo
logarithmic factors, rates better that entropy numbers' rates are possibly
attainable only for neural networks for which the depth l goes to infinity, and
that there is no gain if we fix the depth and let the width W go to infinity.
|
[
"Guergana Petrova",
"Przemyslaw Wojtaszczyk"
] |
2023-10-11 04:50:28
|
http://arxiv.org/abs/2310.07190v1
|
http://arxiv.org/pdf/2310.07190v1
|
2310.07190v1
|
Kernel Cox partially linear regression: building predictive models for cancer patients' survival
|
Wide heterogeneity exists in cancer patients' survival, ranging from a few
months to several decades. To accurately predict clinical outcomes, it is vital
to build an accurate predictive model that relates patients' molecular profiles
with patients' survival. With complex relationships between survival and
high-dimensional molecular predictors, it is challenging to conduct
non-parametric modeling and irrelevant predictors removing simultaneously. In
this paper, we build a kernel Cox proportional hazards semi-parametric model
and propose a novel regularized garrotized kernel machine (RegGKM) method to
fit the model. We use the kernel machine method to describe the complex
relationship between survival and predictors, while automatically removing
irrelevant parametric and non-parametric predictors through a LASSO penalty. An
efficient high-dimensional algorithm is developed for the proposed method.
Comparison with other competing methods in simulation shows that the proposed
method always has better predictive accuracy. We apply this method to analyze a
multiple myeloma dataset and predict patients' death burden based on their gene
expressions. Our results can help classify patients into groups with different
death risks, facilitating treatment for better clinical outcomes.
|
[
"Yaohua Rong",
"Sihai Dave Zhao",
"Xia Zheng",
"Yi Li"
] |
2023-10-11 04:27:54
|
http://arxiv.org/abs/2310.07187v1
|
http://arxiv.org/pdf/2310.07187v1
|
2310.07187v1
|
NeuroInspect: Interpretable Neuron-based Debugging Framework through Class-conditional Visualizations
|
Despite deep learning (DL) has achieved remarkable progress in various
domains, the DL models are still prone to making mistakes. This issue
necessitates effective debugging tools for DL practitioners to interpret the
decision-making process within the networks. However, existing debugging
methods often demand extra data or adjustments to the decision process,
limiting their applicability. To tackle this problem, we present NeuroInspect,
an interpretable neuron-based debugging framework with three key stages:
counterfactual explanations, feature visualizations, and false correlation
mitigation. Our debugging framework first pinpoints neurons responsible for
mistakes in the network and then visualizes features embedded in the neurons to
be human-interpretable. To provide these explanations, we introduce
CLIP-Illusion, a novel feature visualization method that generates images
representing features conditioned on classes to examine the connection between
neurons and the decision layer. We alleviate convoluted explanations of the
conventional visualization approach by employing class information, thereby
isolating mixed properties. This process offers more human-interpretable
explanations for model errors without altering the trained network or requiring
additional data. Furthermore, our framework mitigates false correlations
learned from a dataset under a stochastic perspective, modifying decisions for
the neurons considered as the main causes. We validate the effectiveness of our
framework by addressing false correlations and improving inferences for classes
with the worst performance in real-world settings. Moreover, we demonstrate
that NeuroInspect helps debug the mistakes of DL models through evaluation for
human understanding. The code is openly available at
https://github.com/yeongjoonJu/NeuroInspect.
|
[
"Yeong-Joon Ju",
"Ji-Hoon Park",
"Seong-Whan Lee"
] |
2023-10-11 04:20:32
|
http://arxiv.org/abs/2310.07184v2
|
http://arxiv.org/pdf/2310.07184v2
|
2310.07184v2
|
SAM-OCTA: Prompting Segment-Anything for OCTA Image Segmentation
|
In the analysis of optical coherence tomography angiography (OCTA) images,
the operation of segmenting specific targets is necessary. Existing methods
typically train on supervised datasets with limited samples (approximately a
few hundred), which can lead to overfitting. To address this, the low-rank
adaptation technique is adopted for foundation model fine-tuning and proposed
corresponding prompt point generation strategies to process various
segmentation tasks on OCTA datasets. This method is named SAM-OCTA and has been
experimented on the publicly available OCTA-500 and ROSE datasets. This method
achieves or approaches state-of-the-art segmentation performance metrics. The
effect and applicability of prompt points are discussed in detail for the
retinal vessel, foveal avascular zone, capillary, artery, and vein segmentation
tasks. Furthermore, SAM-OCTA accomplishes local vessel segmentation and
effective artery-vein segmentation, which was not well-solved in previous
works. The code is available at https://github.com/ShellRedia/SAM-OCTA.
|
[
"Xinrun Chen",
"Chengliang Wang",
"Haojian Ning",
"Shiying Li"
] |
2023-10-11 04:14:59
|
http://arxiv.org/abs/2310.07183v1
|
http://arxiv.org/pdf/2310.07183v1
|
2310.07183v1
|
Online Speculative Decoding
|
Speculative decoding is a pivotal technique to accelerate the inference of
large language models (LLMs) by employing a smaller draft model to predict the
target model's outputs. However, its efficacy can be limited due to the low
predictive accuracy of the draft model, particularly when faced with diverse
text inputs and a significant capability gap between the draft and target
models. We introduce online speculative decoding (OSD) to address this
challenge. The main idea is to continually update (multiple) draft model(s) on
observed user query data using the abundant excess computational power in an
LLM serving cluster. Given that LLM inference is memory-bounded, the surplus
computational power in a typical LLM serving cluster can be repurposed for
online retraining of draft models, thereby making the training cost-neutral.
Since the query distribution of an LLM service is relatively simple, retraining
on query distribution enables the draft model to more accurately predict the
target model's outputs, particularly on data originating from query
distributions. As the draft model evolves online, it aligns with the query
distribution in real time, mitigating distribution shifts. We develop a
prototype of online speculative decoding based on online knowledge distillation
and evaluate it using both synthetic and real query data on several popular
LLMs. The results show a substantial increase in the token acceptance rate by
0.1 to 0.65, which translates into 1.22x to 3.06x latency reduction.
|
[
"Xiaoxuan Liu",
"Lanxiang Hu",
"Peter Bailis",
"Ion Stoica",
"Zhijie Deng",
"Alvin Cheung",
"Hao Zhang"
] |
2023-10-11 04:03:42
|
http://arxiv.org/abs/2310.07177v2
|
http://arxiv.org/pdf/2310.07177v2
|
2310.07177v2
|
Generalized Neural Sorting Networks with Error-Free Differentiable Swap Functions
|
Sorting is a fundamental operation of all computer systems, having been a
long-standing significant research topic. Beyond the problem formulation of
traditional sorting algorithms, we consider sorting problems for more abstract
yet expressive inputs, e.g., multi-digit images and image fragments, through a
neural sorting network. To learn a mapping from a high-dimensional input to an
ordinal variable, the differentiability of sorting networks needs to be
guaranteed. In this paper we define a softening error by a differentiable swap
function, and develop an error-free swap function that holds non-decreasing and
differentiability conditions. Furthermore, a permutation-equivariant
Transformer network with multi-head attention is adopted to capture dependency
between given inputs and also leverage its model capacity with self-attention.
Experiments on diverse sorting benchmarks show that our methods perform better
than or comparable to baseline methods.
|
[
"Jungtaek Kim",
"Jeongbeen Yoon",
"Minsu Cho"
] |
2023-10-11 03:47:34
|
http://arxiv.org/abs/2310.07174v1
|
http://arxiv.org/pdf/2310.07174v1
|
2310.07174v1
|
Federated Generalization via Information-Theoretic Distribution Diversification
|
Federated Learning (FL) has surged in prominence due to its capability of
collaborative model training without direct data sharing. However, the vast
disparity in local data distributions among clients, often termed the
non-Independent Identically Distributed (non-IID) challenge, poses a
significant hurdle to FL's generalization efficacy. The scenario becomes even
more complex when not all clients participate in the training process, a common
occurrence due to unstable network connections or limited computational
capacities. This can greatly complicate the assessment of the trained models'
generalization abilities. While a plethora of recent studies has centered on
the generalization gap pertaining to unseen data from participating clients
with diverse distributions, the divergence between the training distributions
of participating clients and the testing distributions of non-participating
ones has been largely overlooked. In response, our paper unveils an
information-theoretic generalization framework for FL. Specifically, it
quantifies generalization errors by evaluating the information entropy of local
distributions and discerning discrepancies across these distributions. Inspired
by our deduced generalization bounds, we introduce a weighted aggregation
approach and a duo of client selection strategies. These innovations aim to
bolster FL's generalization prowess by encompassing a more varied set of client
data distributions. Our extensive empirical evaluations reaffirm the potency of
our proposed methods, aligning seamlessly with our theoretical construct.
|
[
"Zheshun Wu",
"Zenglin Xu",
"Dun Zeng",
"Qifan Wang"
] |
2023-10-11 03:39:56
|
http://arxiv.org/abs/2310.07171v3
|
http://arxiv.org/pdf/2310.07171v3
|
2310.07171v3
|
Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent
|
Multi-view clustering has attracted growing attention owing to its
capabilities of aggregating information from various sources and its promising
horizons in public affairs. Up till now, many advanced approaches have been
proposed in recent literature. However, there are several ongoing difficulties
to be tackled. One common dilemma occurs while attempting to align the features
of different views. We dig out as well as deploy the dependency amongst views
through hierarchical feature descent, which leads to a common latent space(
STAGE 1). This latent space, for the first time of its kind, is regarded as a
'resemblance space', as it reveals certain correlations and dependencies of
different views. To be exact, the one-hot encoding of a category can also be
referred to as a resemblance space in its terminal phase. Moreover, due to the
intrinsic fact that most of the existing multi-view clustering algorithms stem
from k-means clustering and spectral clustering, this results in cubic time
complexity w.r.t. the number of the objects. However, we propose Anchor-based
Multi-view Subspace Clustering with Hierarchical Feature Descent(MVSC-HFD) to
further reduce the computing complexity to linear time cost through a unified
sampling strategy in resemblance space( STAGE 2), followed by subspace
clustering to learn the representation collectively( STAGE 3). Extensive
experimental results on public benchmark datasets demonstrate that our proposed
model consistently outperforms the state-of-the-art techniques.
|
[
"Qiyuan Ou",
"Siwei Wang",
"Pei Zhang",
"Sihang Zhou",
"En Zhu"
] |
2023-10-11 03:29:13
|
http://arxiv.org/abs/2310.07166v1
|
http://arxiv.org/pdf/2310.07166v1
|
2310.07166v1
|
LLark: A Multimodal Foundation Model for Music
|
Music has a unique and complex structure which is challenging for both expert
humans and existing AI systems to understand, and presents unique challenges
relative to other forms of audio. We present LLark, an instruction-tuned
multimodal model for music understanding. We detail our process for dataset
creation, which involves augmenting the annotations of diverse open-source
music datasets and converting them to a unified instruction-tuning format. We
propose a multimodal architecture for LLark, integrating a pretrained
generative model for music with a pretrained language model. In evaluations on
three types of tasks (music understanding, captioning, and reasoning), we show
that our model matches or outperforms existing baselines in zero-shot
generalization for music understanding, and that humans show a high degree of
agreement with the model's responses in captioning and reasoning tasks. LLark
is trained entirely from open-source music data and models, and we make our
training code available along with the release of this paper. Additional
results and audio examples are at https://bit.ly/llark, and our source code is
available at https://github.com/spotify-research/llark .
|
[
"Josh Gardner",
"Simon Durand",
"Daniel Stoller",
"Rachel M. Bittner"
] |
2023-10-11 03:12:47
|
http://arxiv.org/abs/2310.07160v1
|
http://arxiv.org/pdf/2310.07160v1
|
2310.07160v1
|
No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML
|
On-device ML introduces new security challenges: DNN models become white-box
accessible to device users. Based on white-box information, adversaries can
conduct effective model stealing (MS) and membership inference attack (MIA).
Using Trusted Execution Environments (TEEs) to shield on-device DNN models aims
to downgrade (easy) white-box attacks to (harder) black-box attacks. However,
one major shortcoming is the sharply increased latency (up to 50X). To
accelerate TEE-shield DNN computation with GPUs, researchers proposed several
model partition techniques. These solutions, referred to as TEE-Shielded DNN
Partition (TSDP), partition a DNN model into two parts, offloading the
privacy-insensitive part to the GPU while shielding the privacy-sensitive part
within the TEE. This paper benchmarks existing TSDP solutions using both MS and
MIA across a variety of DNN models, datasets, and metrics. We show important
findings that existing TSDP solutions are vulnerable to privacy-stealing
attacks and are not as safe as commonly believed. We also unveil the inherent
difficulty in deciding optimal DNN partition configurations (i.e., the highest
security with minimal utility cost) for present TSDP solutions. The experiments
show that such ``sweet spot'' configurations vary across datasets and models.
Based on lessons harvested from the experiments, we present TEESlice, a novel
TSDP method that defends against MS and MIA during DNN inference. TEESlice
follows a partition-before-training strategy, which allows for accurate
separation between privacy-related weights from public weights. TEESlice
delivers the same security protection as shielding the entire DNN model inside
TEE (the ``upper-bound'' security guarantees) with over 10X less overhead (in
both experimental and real-world environments) than prior TSDP solutions and no
accuracy loss.
|
[
"Ziqi Zhang",
"Chen Gong",
"Yifeng Cai",
"Yuanyuan Yuan",
"Bingyan Liu",
"Ding Li",
"Yao Guo",
"Xiangqun Chen"
] |
2023-10-11 02:54:52
|
http://arxiv.org/abs/2310.07152v1
|
http://arxiv.org/pdf/2310.07152v1
|
2310.07152v1
|
QFT: Quantized Full-parameter Tuning of LLMs with Affordable Resources
|
Large Language Models (LLMs) have showcased remarkable impacts across a wide
spectrum of natural language processing tasks. Fine-tuning these pre-trained
models on downstream datasets provides further significant performance gains,
but this process has been challenging due to its extraordinary resource
requirements. To this end, existing efforts focus on parameter-efficient
fine-tuning, which, unfortunately, fail to capitalize on the powerful potential
of full-parameter fine-tuning. In this work, we propose QFT, a novel Quantized
Full-parameter Tuning framework for LLMs that enables memory-efficient
fine-tuning without harming performance. Our framework incorporates two novel
ideas: (i) we adopt the efficient Lion optimizer, which only keeps track of the
momentum and has consistent update magnitudes for each parameter, an inherent
advantage for robust quantization; and (ii) we quantize all model states and
store them as integer values, and present a gradient flow and parameter update
scheme for the quantized weights. As a result, QFT reduces the model state
memory to 21% of the standard solution while achieving comparable performance,
e.g., tuning a LLaMA-7B model requires only <30GB of memory, satisfied by a
single A6000 GPU.
|
[
"Zhikai Li",
"Xiaoxuan Liu",
"Banghua Zhu",
"Zhen Dong",
"Qingyi Gu",
"Kurt Keutzer"
] |
2023-10-11 02:47:40
|
http://arxiv.org/abs/2310.07147v1
|
http://arxiv.org/pdf/2310.07147v1
|
2310.07147v1
|
Imitation Learning from Purified Demonstration
|
Imitation learning has emerged as a promising approach for addressing
sequential decision-making problems, with the assumption that expert
demonstrations are optimal. However, in real-world scenarios, expert
demonstrations are often imperfect, leading to challenges in effectively
applying imitation learning. While existing research has focused on optimizing
with imperfect demonstrations, the training typically requires a certain
proportion of optimal demonstrations to guarantee performance. To tackle these
problems, we propose to purify the potential perturbations in imperfect
demonstrations and subsequently conduct imitation learning from purified
demonstrations. Motivated by the success of diffusion models, we introduce a
two-step purification via the diffusion process. In the first step, we apply a
forward diffusion process to effectively smooth out the potential perturbations
in imperfect demonstrations by introducing additional noise. Subsequently, a
reverse generative process is utilized to recover the optimal expert
demonstrations from the diffused ones. We provide theoretical evidence
supporting our approach, demonstrating that total variance distance between the
purified and optimal demonstration distributions can be upper-bounded. The
evaluation results on MuJoCo demonstrate the effectiveness of our method from
different aspects.
|
[
"Yunke Wang",
"Minjing Dong",
"Bo Du",
"Chang Xu"
] |
2023-10-11 02:36:52
|
http://arxiv.org/abs/2310.07143v1
|
http://arxiv.org/pdf/2310.07143v1
|
2310.07143v1
|
AE-smnsMLC: Multi-Label Classification with Semantic Matching and Negative Label Sampling for Product Attribute Value Extraction
|
Product attribute value extraction plays an important role for many
real-world applications in e-Commerce such as product search and
recommendation. Previous methods treat it as a sequence labeling task that
needs more annotation for position of values in the product text. This limits
their application to real-world scenario in which only attribute values are
weakly-annotated for each product without their position. Moreover, these
methods only use product text (i.e., product title and description) and do not
consider the semantic connection between the multiple attribute values of a
given product and its text, which can help attribute value extraction. In this
paper, we reformulate this task as a multi-label classification task that can
be applied for real-world scenario in which only annotation of attribute values
is available to train models (i.e., annotation of positional information of
attribute values is not available). We propose a classification model with
semantic matching and negative label sampling for attribute value extraction.
Semantic matching aims to capture semantic interactions between attribute
values of a given product and its text. Negative label sampling aims to enhance
the model's ability of distinguishing similar values belonging to the same
attribute. Experimental results on three subsets of a large real-world
e-Commerce dataset demonstrate the effectiveness and superiority of our
proposed model.
|
[
"Zhongfen Deng",
"Wei-Te Chen",
"Lei Chen",
"Philip S. Yu"
] |
2023-10-11 02:22:28
|
http://arxiv.org/abs/2310.07137v1
|
http://arxiv.org/pdf/2310.07137v1
|
2310.07137v1
|
Risk Assessment and Statistical Significance in the Age of Foundation Models
|
We propose a distributional framework for assessing socio-technical risks of
foundation models with quantified statistical significance. Our approach hinges
on a new statistical relative testing based on first and second order
stochastic dominance of real random variables. We show that the second order
statistics in this test are linked to mean-risk models commonly used in
econometrics and mathematical finance to balance risk and utility when choosing
between alternatives. Using this framework, we formally develop a risk-aware
approach for foundation model selection given guardrails quantified by
specified metrics. Inspired by portfolio optimization and selection theory in
mathematical finance, we define a \emph{metrics portfolio} for each model as a
means to aggregate a collection of metrics, and perform model selection based
on the stochastic dominance of these portfolios. The statistical significance
of our tests is backed theoretically by an asymptotic analysis via central
limit theorems instantiated in practice via a bootstrap variance estimate. We
use our framework to compare various large language models regarding risks
related to drifting from instructions and outputting toxic content.
|
[
"Apoorva Nitsure",
"Youssef Mroueh",
"Mattia Rigotti",
"Kristjan Greenewald",
"Brian Belgodere",
"Mikhail Yurochkin",
"Jiri Navratil",
"Igor Melnyk",
"Jerret Ross"
] |
2023-10-11 02:08:37
|
http://arxiv.org/abs/2310.07132v1
|
http://arxiv.org/pdf/2310.07132v1
|
2310.07132v1
|
Off-Policy Evaluation for Human Feedback
|
Off-policy evaluation (OPE) is important for closing the gap between offline
training and evaluation of reinforcement learning (RL), by estimating
performance and/or rank of target (evaluation) policies using offline
trajectories only. It can improve the safety and efficiency of data collection
and policy testing procedures in situations where online deployments are
expensive, such as healthcare. However, existing OPE methods fall short in
estimating human feedback (HF) signals, as HF may be conditioned over multiple
underlying factors and is only sparsely available; as opposed to the
agent-defined environmental rewards (used in policy optimization), which are
usually determined over parametric functions or distributions. Consequently,
the nature of HF signals makes extrapolating accurate OPE estimations to be
challenging. To resolve this, we introduce an OPE for HF (OPEHF) framework that
revives existing OPE methods in order to accurately evaluate the HF signals.
Specifically, we develop an immediate human reward (IHR) reconstruction
approach, regularized by environmental knowledge distilled in a latent space
that captures the underlying dynamics of state transitions as well as issuing
HF signals. Our approach has been tested over two real-world experiments,
adaptive in-vivo neurostimulation and intelligent tutoring, as well as in a
simulation environment (visual Q&A). Results show that our approach
significantly improves the performance toward estimating HF signals accurately,
compared to directly applying (variants of) existing OPE methods.
|
[
"Qitong Gao",
"Ge Gao",
"Juncheng Dong",
"Vahid Tarokh",
"Min Chi",
"Miroslav Pajic"
] |
2023-10-11 01:52:42
|
http://arxiv.org/abs/2310.07123v2
|
http://arxiv.org/pdf/2310.07123v2
|
2310.07123v2
|
The Temporal Structure of Language Processing in the Human Brain Corresponds to The Layered Hierarchy of Deep Language Models
|
Deep Language Models (DLMs) provide a novel computational paradigm for
understanding the mechanisms of natural language processing in the human brain.
Unlike traditional psycholinguistic models, DLMs use layered sequences of
continuous numerical vectors to represent words and context, allowing a
plethora of emerging applications such as human-like text generation. In this
paper we show evidence that the layered hierarchy of DLMs may be used to model
the temporal dynamics of language comprehension in the brain by demonstrating a
strong correlation between DLM layer depth and the time at which layers are
most predictive of the human brain. Our ability to temporally resolve
individual layers benefits from our use of electrocorticography (ECoG) data,
which has a much higher temporal resolution than noninvasive methods like fMRI.
Using ECoG, we record neural activity from participants listening to a
30-minute narrative while also feeding the same narrative to a high-performing
DLM (GPT2-XL). We then extract contextual embeddings from the different layers
of the DLM and use linear encoding models to predict neural activity. We first
focus on the Inferior Frontal Gyrus (IFG, or Broca's area) and then extend our
model to track the increasing temporal receptive window along the linguistic
processing hierarchy from auditory to syntactic and semantic areas. Our results
reveal a connection between human language processing and DLMs, with the DLM's
layer-by-layer accumulation of contextual information mirroring the timing of
neural activity in high-order language areas.
|
[
"Ariel Goldstein",
"Eric Ham",
"Mariano Schain",
"Samuel Nastase",
"Zaid Zada",
"Avigail Dabush",
"Bobbi Aubrey",
"Harshvardhan Gazula",
"Amir Feder",
"Werner K Doyle",
"Sasha Devore",
"Patricia Dugan",
"Daniel Friedman",
"Roi Reichart",
"Michael Brenner",
"Avinatan Hassidim",
"Orrin Devinsky",
"Adeen Flinker",
"Omer Levy",
"Uri Hasson"
] |
2023-10-11 01:03:42
|
http://arxiv.org/abs/2310.07106v1
|
http://arxiv.org/pdf/2310.07106v1
|
2310.07106v1
|
Machine Learning Methods for Background Potential Estimation in 2DEGs
|
In the realm of quantum-effect devices and materials, two-dimensional
electron gases (2DEGs) stand as fundamental structures that promise
transformative technologies. However, the presence of impurities and defects in
2DEGs poses substantial challenges, impacting carrier mobility, conductivity,
and quantum coherence time. To address this, we harness the power of scanning
gate microscopy (SGM) and employ three distinct machine learning techniques to
estimate the background potential of 2DEGs from SGM data: image-to-image
translation using generative adversarial neural networks, cellular neural
network, and evolutionary search. Our findings, despite data constraints,
highlight the effectiveness of an evolutionary search algorithm in this
context, offering a novel approach for defect analysis. This work not only
advances our understanding of 2DEGs but also underscores the potential of
machine learning in probing quantum materials, with implications for quantum
computing and nanoelectronics.
|
[
"Carlo da Cunha",
"Nobuyuki Aoki",
"David Ferry",
"Kevin Vora",
"Yu Zhang"
] |
2023-10-11 00:03:07
|
http://arxiv.org/abs/2310.07089v1
|
http://arxiv.org/pdf/2310.07089v1
|
2310.07089v1
|
Investigating the Adversarial Robustness of Density Estimation Using the Probability Flow ODE
|
Beyond their impressive sampling capabilities, score-based diffusion models
offer a powerful analysis tool in the form of unbiased density estimation of a
query sample under the training data distribution. In this work, we investigate
the robustness of density estimation using the probability flow (PF) neural
ordinary differential equation (ODE) model against gradient-based likelihood
maximization attacks and the relation to sample complexity, where the
compressed size of a sample is used as a measure of its complexity. We
introduce and evaluate six gradient-based log-likelihood maximization attacks,
including a novel reverse integration attack. Our experimental evaluations on
CIFAR-10 show that density estimation using the PF ODE is robust against
high-complexity, high-likelihood attacks, and that in some cases adversarial
samples are semantically meaningful, as expected from a robust estimator.
|
[
"Marius Arvinte",
"Cory Cornelius",
"Jason Martin",
"Nageen Himayat"
] |
2023-10-10 23:58:53
|
http://arxiv.org/abs/2310.07084v1
|
http://arxiv.org/pdf/2310.07084v1
|
2310.07084v1
|
Taking the human out of decomposition-based optimization via artificial intelligence: Part II. Learning to initialize
|
The repeated solution of large-scale optimization problems arises frequently
in process systems engineering tasks. Decomposition-based solution methods have
been widely used to reduce the corresponding computational time, yet their
implementation has multiple steps that are difficult to configure. We propose a
machine learning approach to learn the optimal initialization of such
algorithms which minimizes the computational time. Active and supervised
learning is used to learn a surrogate model that predicts the computational
performance for a given initialization. We apply this approach to the
initialization of Generalized Benders Decomposition for the solution of mixed
integer model predictive control problems. The surrogate models are used to
find the optimal number of initial cuts that should be added in the master
problem. The results show that the proposed approach can lead to a significant
reduction in solution time, and active learning can reduce the data required
for learning.
|
[
"Ilias Mitrai",
"Prodromos Daoutidis"
] |
2023-10-10 23:49:26
|
http://arxiv.org/abs/2310.07082v1
|
http://arxiv.org/pdf/2310.07082v1
|
2310.07082v1
|
Secure Decentralized Learning with Blockchain
|
Federated Learning (FL) is a well-known paradigm of distributed machine
learning on mobile and IoT devices, which preserves data privacy and optimizes
communication efficiency. To avoid the single point of failure problem in FL,
decentralized federated learning (DFL) has been proposed to use peer-to-peer
communication for model aggregation, which has been considered an attractive
solution for machine learning tasks on distributed personal devices. However,
this process is vulnerable to attackers who share false models and data. If
there exists a group of malicious clients, they might harm the performance of
the model by carrying out a poisoning attack. In addition, in DFL, clients
often lack the incentives to contribute their computing powers to do model
training. In this paper, we proposed Blockchain-based Decentralized Federated
Learning (BDFL), which leverages a blockchain for decentralized model
verification and auditing. BDFL includes an auditor committee for model
verification, an incentive mechanism to encourage the participation of clients,
a reputation model to evaluate the trustworthiness of clients, and a protocol
suite for dynamic network updates. Evaluation results show that, with the
reputation mechanism, BDFL achieves fast model convergence and high accuracy on
real datasets even if there exist 30\% malicious clients in the system.
|
[
"Xiaoxue Zhang",
"Yifan Hua",
"Chen Qian"
] |
2023-10-10 23:45:17
|
http://arxiv.org/abs/2310.07079v1
|
http://arxiv.org/pdf/2310.07079v1
|
2310.07079v1
|
Taking the human out of decomposition-based optimization via artificial intelligence: Part I. Learning when to decompose
|
In this paper, we propose a graph classification approach for automatically
determining whether to use a monolithic or a decomposition-based solution
method. In this approach, an optimization problem is represented as a graph
that captures the structural and functional coupling among the variables and
constraints of the problem via an appropriate set of features. Given this
representation, a graph classifier is built to determine the best solution
method for a given problem. The proposed approach is used to develop a
classifier that determines whether a convex Mixed Integer Nonlinear Programming
problem should be solved using branch and bound or the outer approximation
algorithm. Finally, it is shown how the learned classifier can be incorporated
into existing mixed integer optimization solvers.
|
[
"Ilias Mitrai",
"Prodromos Daoutidis"
] |
2023-10-10 23:31:06
|
http://arxiv.org/abs/2310.07068v1
|
http://arxiv.org/pdf/2310.07068v1
|
2310.07068v1
|
Acoustic Model Fusion for End-to-end Speech Recognition
|
Recent advances in deep learning and automatic speech recognition (ASR) have
enabled the end-to-end (E2E) ASR system and boosted the accuracy to a new
level. The E2E systems implicitly model all conventional ASR components, such
as the acoustic model (AM) and the language model (LM), in a single network
trained on audio-text pairs. Despite this simpler system architecture, fusing a
separate LM, trained exclusively on text corpora, into the E2E system has
proven to be beneficial. However, the application of LM fusion presents certain
drawbacks, such as its inability to address the domain mismatch issue inherent
to the internal AM. Drawing inspiration from the concept of LM fusion, we
propose the integration of an external AM into the E2E system to better address
the domain mismatch. By implementing this novel approach, we have achieved a
significant reduction in the word error rate, with an impressive drop of up to
14.3% across varied test sets. We also discovered that this AM fusion approach
is particularly beneficial in enhancing named entity recognition.
|
[
"Zhihong Lei",
"Mingbin Xu",
"Shiyi Han",
"Leo Liu",
"Zhen Huang",
"Tim Ng",
"Yuanyuan Zhang",
"Ernest Pusateri",
"Mirko Hannemann",
"Yaqiao Deng",
"Man-Hung Siu"
] |
2023-10-10 23:00:17
|
http://arxiv.org/abs/2310.07062v1
|
http://arxiv.org/pdf/2310.07062v1
|
2310.07062v1
|
DKEC: Domain Knowledge Enhanced Multi-Label Classification for Electronic Health Records
|
Multi-label text classification (MLTC) tasks in the medical domain often face
long-tail label distribution, where rare classes have fewer training samples
than frequent classes. Although previous works have explored different model
architectures and hierarchical label structures to find important features,
most of them neglect to incorporate the domain knowledge from medical
guidelines. In this paper, we present DKEC, Domain Knowledge Enhanced
Classifier for medical diagnosis prediction with two innovations: (1) a
label-wise attention mechanism that incorporates a heterogeneous graph and
domain ontologies to capture the semantic relationships between medical
entities, (2) a simple yet effective group-wise training method based on
similarity of labels to increase samples of rare classes. We evaluate DKEC on
two real-world medical datasets: the RAA dataset, a collection of 4,417 patient
care reports from emergency medical services (EMS) incidents, and a subset of
53,898 reports from the MIMIC-III dataset. Experimental results show that our
method outperforms the state-of-the-art, particularly for the few-shot (tail)
classes. More importantly, we study the applicability of DKEC to different
language models and show that DKEC can help the smaller language models achieve
comparable performance to large language models.
|
[
"Xueren Ge",
"Ronald Dean Williams",
"John A. Stankovic",
"Homa Alemzadeh"
] |
2023-10-10 22:53:15
|
http://arxiv.org/abs/2310.07059v1
|
http://arxiv.org/pdf/2310.07059v1
|
2310.07059v1
|
Spiral-Elliptical automated galaxy morphology classification from telescope images
|
The classification of galaxy morphologies is an important step in the
investigation of theories of hierarchical structure formation. While human
expert visual classification remains quite effective and accurate, it cannot
keep up with the massive influx of data from emerging sky surveys. A variety of
approaches have been proposed to classify large numbers of galaxies; these
approaches include crowdsourced visual classification, and automated and
computational methods, such as machine learning methods based on designed
morphology statistics and deep learning. In this work, we develop two novel
galaxy morphology statistics, descent average and descent variance, which can
be efficiently extracted from telescope galaxy images. We further propose
simplified versions of the existing image statistics concentration, asymmetry,
and clumpiness, which have been widely used in the literature of galaxy
morphologies. We utilize the galaxy image data from the Sloan Digital Sky
Survey to demonstrate the effective performance of our proposed image
statistics at accurately detecting spiral and elliptical galaxies when used as
features of a random forest classifier.
|
[
"Matthew J. Baumstark",
"Giuseppe Vinci"
] |
2023-10-10 22:36:52
|
http://arxiv.org/abs/2310.07740v1
|
http://arxiv.org/pdf/2310.07740v1
|
2310.07740v1
|
FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication
|
Federated learning (FL) is a distributed machine learning (ML) paradigm that
enables clients to collaborate without accessing, infringing upon, or leaking
original user data by sharing only model parameters. In the Internet of Things
(IoT), edge devices are increasingly leveraging multimodal data compositions
and fusion paradigms to enhance model performance. However, in FL applications,
two main challenges remain open: (i) addressing the issues caused by
heterogeneous clients lacking specific modalities and (ii) devising an optimal
modality upload strategy to minimize communication overhead while maximizing
learning performance. In this paper, we propose Federated Multimodal Fusion
learning with Selective modality communication (FedMFS), a new multimodal
fusion FL methodology that can tackle the above mentioned challenges. The key
idea is to utilize Shapley values to quantify each modality's contribution and
modality model size to gauge communication overhead, so that each client can
selectively upload the modality models to the server for aggregation. This
enables FedMFS to flexibly balance performance against communication costs,
depending on resource constraints and applications. Experiments on real-world
multimodal datasets demonstrate the effectiveness of FedMFS, achieving
comparable accuracy while reducing communication overhead by one twentieth
compared to baselines.
|
[
"Liangqi Yuan",
"Dong-Jun Han",
"Vishnu Pandi Chellapandi",
"Stanislaw H. Żak",
"Christopher G. Brinton"
] |
2023-10-10 22:23:27
|
http://arxiv.org/abs/2310.07048v1
|
http://arxiv.org/pdf/2310.07048v1
|
2310.07048v1
|
A predict-and-optimize approach to profit-driven churn prevention
|
In this paper, we introduce a novel predict-and-optimize method for
profit-driven churn prevention. We frame the task of targeting customers for a
retention campaign as a regret minimization problem. The main objective is to
leverage individual customer lifetime values (CLVs) to ensure that only the
most valuable customers are targeted. In contrast, many profit-driven
strategies focus on churn probabilities while considering average CLVs. This
often results in significant information loss due to data aggregation. Our
proposed model aligns with the guidelines of Predict-and-Optimize (PnO)
frameworks and can be efficiently solved using stochastic gradient descent
methods. Results from 12 churn prediction datasets underscore the effectiveness
of our approach, which achieves the best average performance compared to other
well-established strategies in terms of average profit.
|
[
"Nuria Gómez-Vargas",
"Sebastián Maldonado",
"Carla Vairetti"
] |
2023-10-10 22:21:16
|
http://arxiv.org/abs/2310.07047v1
|
http://arxiv.org/pdf/2310.07047v1
|
2310.07047v1
|
Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Images
|
Recent breakthroughs in self-supervised learning have enabled the use of
large unlabeled datasets to train visual foundation models that can generalize
to a variety of downstream tasks. While this training paradigm is well suited
for the medical domain where annotations are scarce, large-scale pre-training
in the medical domain, and in particular pathology, has not been extensively
studied. Previous work in self-supervised learning in pathology has leveraged
smaller datasets for both pre-training and evaluating downstream performance.
The aim of this project is to train the largest academic foundation model and
benchmark the most prominent self-supervised learning algorithms by
pre-training and evaluating downstream performance on large clinical pathology
datasets. We collected the largest pathology dataset to date, consisting of
over 3 billion images from over 423 thousand microscopy slides. We compared
pre-training of visual transformer models using the masked autoencoder (MAE)
and DINO algorithms. We evaluated performance on six clinically relevant tasks
from three anatomic sites and two institutions: breast cancer detection,
inflammatory bowel disease detection, breast cancer estrogen receptor
prediction, lung adenocarcinoma EGFR mutation prediction, and lung cancer
immunotherapy response prediction. Our results demonstrate that pre-training on
pathology data is beneficial for downstream performance compared to
pre-training on natural images. Additionally, the DINO algorithm achieved
better generalization performance across all tasks tested. The presented
results signify a phase change in computational pathology research, paving the
way into a new era of more performant models based on large-scale, parallel
pre-training at the billion-image scale.
|
[
"Gabriele Campanella",
"Ricky Kwan",
"Eugene Fluder",
"Jennifer Zeng",
"Aryeh Stock",
"Brandon Veremis",
"Alexandros D. Polydorides",
"Cyrus Hedvat",
"Adam Schoenfeld",
"Chad Vanderbilt",
"Patricia Kovatch",
"Carlos Cordon-Cardo",
"Thomas J. Fuchs"
] |
2023-10-10 21:40:19
|
http://arxiv.org/abs/2310.07033v1
|
http://arxiv.org/pdf/2310.07033v1
|
2310.07033v1
|
Neural Harmonium: An Interpretable Deep Structure for Nonlinear Dynamic System Identification with Application to Audio Processing
|
Improving the interpretability of deep neural networks has recently gained
increased attention, especially when the power of deep learning is leveraged to
solve problems in physics. Interpretability helps us understand a model's
ability to generalize and reveal its limitations. In this paper, we introduce a
causal interpretable deep structure for modeling dynamic systems. Our proposed
model makes use of the harmonic analysis by modeling the system in a
time-frequency domain while maintaining high temporal and spectral resolution.
Moreover, the model is built in an order recursive manner which allows for
fast, robust, and exact second order optimization without the need for an
explicit Hessian calculation. To circumvent the resulting high dimensionality
of the building blocks of our system, a neural network is designed to identify
the frequency interdependencies. The proposed model is illustrated and
validated on nonlinear system identification problems as required for audio
signal processing tasks. Crowd-sourced experimentation contrasting the
performance of the proposed approach to other state-of-the-art solutions on an
acoustic echo cancellation scenario confirms the effectiveness of our method
for real-life applications.
|
[
"Karim Helwani",
"Erfan Soltanmohammadi",
"Michael M. Goodwin"
] |
2023-10-10 21:32:15
|
http://arxiv.org/abs/2310.07032v1
|
http://arxiv.org/pdf/2310.07032v1
|
2310.07032v1
|
Facial Forgery-based Deepfake Detection using Fine-Grained Features
|
Facial forgery by deepfakes has caused major security risks and raised severe
societal concerns. As a countermeasure, a number of deepfake detection methods
have been proposed. Most of them model deepfake detection as a binary
classification problem using a backbone convolutional neural network (CNN)
architecture pretrained for the task. These CNN-based methods have demonstrated
very high efficacy in deepfake detection with the Area under the Curve (AUC) as
high as $0.99$. However, the performance of these methods degrades
significantly when evaluated across datasets and deepfake manipulation
techniques. This draws our attention towards learning more subtle, local, and
discriminative features for deepfake detection. In this paper, we formulate
deepfake detection as a fine-grained classification problem and propose a new
fine-grained solution to it. Specifically, our method is based on learning
subtle and generalizable features by effectively suppressing background noise
and learning discriminative features at various scales for deepfake detection.
Through extensive experimental validation, we demonstrate the superiority of
our method over the published research in cross-dataset and cross-manipulation
generalization of deepfake detectors for the majority of the experimental
scenarios.
|
[
"Aakash Varma Nadimpalli",
"Ajita Rattani"
] |
2023-10-10 21:30:05
|
http://arxiv.org/abs/2310.07028v1
|
http://arxiv.org/pdf/2310.07028v1
|
2310.07028v1
|
Utilizing Synthetic Data for Medical Vision-Language Pre-training: Bypassing the Need for Real Images
|
Medical Vision-Language Pre-training (VLP) learns representations jointly
from medical images and paired radiology reports. It typically requires
large-scale paired image-text datasets to achieve effective pre-training for
both the image encoder and text encoder. The advent of text-guided generative
models raises a compelling question: Can VLP be implemented solely with
synthetic images generated from genuine radiology reports, thereby mitigating
the need for extensively pairing and curating image-text datasets? In this
work, we scrutinize this very question by examining the feasibility and
effectiveness of employing synthetic images for medical VLP. We replace real
medical images with their synthetic equivalents, generated from authentic
medical reports. Utilizing three state-of-the-art VLP algorithms, we
exclusively train on these synthetic samples. Our empirical evaluation across
three subsequent tasks, namely image classification, semantic segmentation and
object detection, reveals that the performance achieved through synthetic data
is on par with or even exceeds that obtained with real images. As a pioneering
contribution to this domain, we introduce a large-scale synthetic medical image
dataset, paired with anonymized real radiology reports. This alleviates the
need of sharing medical images, which are not easy to curate and share in
practice. The code and the dataset will be made publicly available upon paper
acceptance.
|
[
"Che Liu",
"Anand Shah",
"Wenjia Bai",
"Rossella Arcucci"
] |
2023-10-10 21:29:41
|
http://arxiv.org/abs/2310.07027v1
|
http://arxiv.org/pdf/2310.07027v1
|
2310.07027v1
|
Automatic Macro Mining from Interaction Traces at Scale
|
Macros are building block tasks of our everyday smartphone activity (e.g.,
"login", or "booking a flight"). Effectively extracting macros is important for
understanding mobile interaction and enabling task automation. These macros are
however difficult to extract at scale as they can be comprised of multiple
steps yet hidden within programmatic components of the app. In this paper, we
introduce a novel approach based on Large Language Models (LLMs) to
automatically extract semantically meaningful macros from both random and
user-curated mobile interaction traces. The macros produced by our approach are
automatically tagged with natural language descriptions and are fully
executable. To examine the quality of extraction, we conduct multiple studies,
including user evaluation, comparative analysis against human-curated tasks,
and automatic execution of these macros. These experiments and analyses show
the effectiveness of our approach and the usefulness of extracted macros in
various downstream applications.
|
[
"Forrest Huang",
"Gang Li",
"Tao Li",
"Yang Li"
] |
2023-10-10 21:23:47
|
http://arxiv.org/abs/2310.07023v1
|
http://arxiv.org/pdf/2310.07023v1
|
2310.07023v1
|
Neural Relational Inference with Fast Modular Meta-learning
|
\textit{Graph neural networks} (GNNs) are effective models for many dynamical
systems consisting of entities and relations. Although most GNN applications
assume a single type of entity and relation, many situations involve multiple
types of interactions. \textit{Relational inference} is the problem of
inferring these interactions and learning the dynamics from observational data.
We frame relational inference as a \textit{modular meta-learning} problem,
where neural modules are trained to be composed in different ways to solve many
tasks. This meta-learning framework allows us to implicitly encode time
invariance and infer relations in context of one another rather than
independently, which increases inference capacity. Framing inference as the
inner-loop optimization of meta-learning leads to a model-based approach that
is more data-efficient and capable of estimating the state of entities that we
do not observe directly, but whose existence can be inferred from their effect
on observed entities. To address the large search space of graph neural network
compositions, we meta-learn a \textit{proposal function} that speeds up the
inner-loop simulated annealing search within the modular meta-learning
algorithm, providing two orders of magnitude increase in the size of problems
that can be addressed.
|
[
"Ferran Alet",
"Erica Weng",
"Tomás Lozano Pérez",
"Leslie Pack Kaelbling"
] |
2023-10-10 21:05:13
|
http://arxiv.org/abs/2310.07015v1
|
http://arxiv.org/pdf/2310.07015v1
|
2310.07015v1
|
Answer Candidate Type Selection: Text-to-Text Language Model for Closed Book Question Answering Meets Knowledge Graphs
|
Pre-trained Text-to-Text Language Models (LMs), such as T5 or BART yield
promising results in the Knowledge Graph Question Answering (KGQA) task.
However, the capacity of the models is limited and the quality decreases for
questions with less popular entities. In this paper, we present a novel
approach which works on top of the pre-trained Text-to-Text QA system to
address this issue. Our simple yet effective method performs filtering and
re-ranking of generated candidates based on their types derived from Wikidata
"instance_of" property.
|
[
"Mikhail Salnikov",
"Maria Lysyuk",
"Pavel Braslavski",
"Anton Razzhigaev",
"Valentin Malykh",
"Alexander Panchenko"
] |
2023-10-10 20:49:43
|
http://arxiv.org/abs/2310.07008v1
|
http://arxiv.org/pdf/2310.07008v1
|
2310.07008v1
|
Sound-skwatter (Did You Mean: Sound-squatter?) AI-powered Generator for Phishing Prevention
|
Sound-squatting is a phishing attack that tricks users into malicious
resources by exploiting similarities in the pronunciation of words. Proactive
defense against sound-squatting candidates is complex, and existing solutions
rely on manually curated lists of homophones. We here introduce Sound-skwatter,
a multi-language AI-based system that generates sound-squatting candidates for
proactive defense. Sound-skwatter relies on an innovative multi-modal
combination of Transformers Networks and acoustic models to learn sound
similarities. We show that Sound-skwatter can automatically list known
homophones and thousands of high-quality candidates. In addition, it covers
cross-language sound-squatting, i.e., when the reader and the listener speak
different languages, supporting any combination of languages. We apply
Sound-skwatter to network-centric phishing via squatted domain names. We find ~
10% of the generated domains exist in the wild, the vast majority unknown to
protection solutions. Next, we show attacks on the PyPI package manager, where
~ 17% of the popular packages have at least one existing candidate. We believe
Sound-skwatter is a crucial asset to mitigate the sound-squatting phenomenon
proactively on the Internet. To increase its impact, we publish an online demo
and release our models and code as open source.
|
[
"Rodolfo Valentim",
"Idilio Drago",
"Marco Mellia",
"Federico Cerutti"
] |
2023-10-10 20:36:39
|
http://arxiv.org/abs/2310.07005v1
|
http://arxiv.org/pdf/2310.07005v1
|
2310.07005v1
|
CarDS-Plus ECG Platform: Development and Feasibility Evaluation of a Multiplatform Artificial Intelligence Toolkit for Portable and Wearable Device Electrocardiograms
|
In the rapidly evolving landscape of modern healthcare, the integration of
wearable & portable technology provides a unique opportunity for personalized
health monitoring in the community. Devices like the Apple Watch, FitBit, and
AliveCor KardiaMobile have revolutionized the acquisition and processing of
intricate health data streams. Amidst the variety of data collected by these
gadgets, single-lead electrocardiogram (ECG) recordings have emerged as a
crucial source of information for monitoring cardiovascular health. There has
been significant advances in artificial intelligence capable of interpreting
these 1-lead ECGs, facilitating clinical diagnosis as well as the detection of
rare cardiac disorders. This design study describes the development of an
innovative multiplatform system aimed at the rapid deployment of AI-based ECG
solutions for clinical investigation & care delivery. The study examines design
considerations, aligning them with specific applications, develops data flows
to maximize efficiency for research & clinical use. This process encompasses
the reception of single-lead ECGs from diverse wearable devices, channeling
this data into a centralized data lake & facilitating real-time inference
through AI models for ECG interpretation. An evaluation of the platform
demonstrates a mean duration from acquisition to reporting of results of 33.0
to 35.7 seconds, after a standard 30 second acquisition. There were no
substantial differences in acquisition to reporting across two commercially
available devices (Apple Watch and KardiaMobile). These results demonstrate the
succcessful translation of design principles into a fully integrated &
efficient strategy for leveraging 1-lead ECGs across platforms & interpretation
by AI-ECG algorithms. Such a platform is critical to translating AI discoveries
for wearable and portable ECG devices to clinical impact through rapid
deployment.
|
[
"Sumukh Vasisht Shankar",
"Evangelos K Oikonomou",
"Rohan Khera"
] |
2023-10-10 20:33:48
|
http://arxiv.org/abs/2310.07000v1
|
http://arxiv.org/pdf/2310.07000v1
|
2310.07000v1
|
Violation of Expectation via Metacognitive Prompting Reduces Theory of Mind Prediction Error in Large Language Models
|
Recent research shows that Large Language Models (LLMs) exhibit a compelling
level of proficiency in Theory of Mind (ToM) tasks. This ability to impute
unobservable mental states to others is vital to human social cognition and may
prove equally important in principal-agent relations between individual humans
and Artificial Intelligences (AIs). In this paper, we explore how a mechanism
studied in developmental psychology known as Violation of Expectation (VoE) can
be implemented to reduce errors in LLM prediction about users by leveraging
emergent ToM affordances. And we introduce a \textit{metacognitive prompting}
framework to apply VoE in the context of an AI tutor. By storing and retrieving
facts derived in cases where LLM expectation about the user was violated, we
find that LLMs are able to learn about users in ways that echo theories of
human learning. Finally, we discuss latent hazards and augmentative
opportunities associated with modeling user psychology and propose ways to
mitigate risk along with possible directions for future inquiry.
|
[
"Courtland Leer",
"Vincent Trost",
"Vineeth Voruganti"
] |
2023-10-10 20:05:13
|
http://arxiv.org/abs/2310.06983v1
|
http://arxiv.org/pdf/2310.06983v1
|
2310.06983v1
|
Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality
|
Dataset distillation aims to minimize the time and memory needed for training
deep networks on large datasets, by creating a small set of synthetic images
that has a similar generalization performance to that of the full dataset.
However, current dataset distillation techniques fall short, showing a notable
performance gap when compared to training on the original data. In this work,
we are the first to argue that using just one synthetic subset for distillation
will not yield optimal generalization performance. This is because the training
dynamics of deep networks drastically change during the training. Hence,
multiple synthetic subsets are required to capture the training dynamics at
different phases of training. To address this issue, we propose Progressive
Dataset Distillation (PDD). PDD synthesizes multiple small sets of synthetic
images, each conditioned on the previous sets, and trains the model on the
cumulative union of these subsets without requiring additional training time.
Our extensive experiments show that PDD can effectively improve the performance
of existing dataset distillation methods by up to 4.3%. In addition, our method
for the first time enable generating considerably larger synthetic datasets.
|
[
"Xuxi Chen",
"Yu Yang",
"Zhangyang Wang",
"Baharan Mirzasoleiman"
] |
2023-10-10 20:04:44
|
http://arxiv.org/abs/2310.06982v1
|
http://arxiv.org/pdf/2310.06982v1
|
2310.06982v1
|
Federated Quantum Machine Learning with Differential Privacy
|
The preservation of privacy is a critical concern in the implementation of
artificial intelligence on sensitive training data. There are several
techniques to preserve data privacy but quantum computations are inherently
more secure due to the no-cloning theorem, resulting in a most desirable
computational platform on top of the potential quantum advantages. There have
been prior works in protecting data privacy by Quantum Federated Learning (QFL)
and Quantum Differential Privacy (QDP) studied independently. However, to the
best of our knowledge, no prior work has addressed both QFL and QDP together
yet. Here, we propose to combine these privacy-preserving methods and implement
them on the quantum platform, so that we can achieve comprehensive protection
against data leakage (QFL) and model inversion attacks (QDP). This
implementation promises more efficient and secure artificial intelligence. In
this paper, we present a successful implementation of these
privacy-preservation methods by performing the binary classification of the
Cats vs Dogs dataset. Using our quantum-classical machine learning model, we
obtained a test accuracy of over 0.98, while maintaining epsilon values less
than 1.3. We show that federated differentially private training is a viable
privacy preservation method for quantum machine learning on Noisy
Intermediate-Scale Quantum (NISQ) devices.
|
[
"Rod Rofougaran",
"Shinjae Yoo",
"Huan-Hsin Tseng",
"Samuel Yen-Chi Chen"
] |
2023-10-10 19:52:37
|
http://arxiv.org/abs/2310.06973v1
|
http://arxiv.org/pdf/2310.06973v1
|
2310.06973v1
|
Flood and Echo: Algorithmic Alignment of GNNs with Distributed Computing
|
Graph Neural Networks are a natural fit for learning algorithms. They can
directly represent tasks through an abstract but versatile graph structure and
handle inputs of different sizes. This opens up the possibility for scaling and
extrapolation to larger graphs, one of the most important advantages of an
algorithm. However, this raises two core questions i) How can we enable nodes
to gather the required information in a given graph ($\textit{information
exchange}$), even if is far away and ii) How can we design an execution
framework which enables this information exchange for extrapolation to larger
graph sizes ($\textit{algorithmic alignment for extrapolation}$). We propose a
new execution framework that is inspired by the design principles of
distributed algorithms: Flood and Echo Net. It propagates messages through the
entire graph in a wave like activation pattern, which naturally generalizes to
larger instances. Through its sparse but parallel activations it is provably
more efficient in terms of message complexity. We study the proposed model and
provide both empirical evidence and theoretical insights in terms of its
expressiveness, efficiency, information exchange and ability to extrapolate.
|
[
"Joël Mathys",
"Florian Grötschla",
"Kalyan Varma Nadimpalli",
"Roger Wattenhofer"
] |
2023-10-10 19:47:58
|
http://arxiv.org/abs/2310.06970v2
|
http://arxiv.org/pdf/2310.06970v2
|
2310.06970v2
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.