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Using fine-tuning and min lookahead beam search to improve Whisper | The performance of Whisper in low-resource languages is still far from
perfect. In addition to a lack of training data on low-resource languages, we
identify some limitations in the beam search algorithm used in Whisper. To
address these issues, we fine-tune Whisper on additional data and propose an
improved decoding algorithm. On the Vietnamese language, fine-tuning
Whisper-Tiny with LoRA leads to an improvement of 38.49 in WER over the
zero-shot Whisper-Tiny setting which is a further reduction of 1.45 compared to
full-parameter fine-tuning. Additionally, by using Filter-Ends and Min
Lookahead decoding algorithms, the WER reduces by 2.26 on average over a range
of languages compared to standard beam search. These results generalise to
larger Whisper model sizes. We also prove a theorem that Min Lookahead
outperforms the standard beam search algorithm used in Whisper. | [
"Andrea Do",
"Oscar Brown",
"Zhengjie Wang",
"Nikhil Mathew",
"Zixin Liu",
"Jawwad Ahmed",
"Cheng Yu"
] | 2023-09-19 04:04:14 | http://arxiv.org/abs/2309.10299v1 | http://arxiv.org/pdf/2309.10299v1 | 2309.10299v1 |
Learning Orbitally Stable Systems for Diagrammatically Teaching | Diagrammatic Teaching is a paradigm for robots to acquire novel skills,
whereby the user provides 2D sketches over images of the scene to shape the
robot's motion. In this work, we tackle the problem of teaching a robot to
approach a surface and then follow cyclic motion on it, where the cycle of the
motion can be arbitrarily specified by a single user-provided sketch over an
image from the robot's camera. Accordingly, we introduce the \emph{Stable
Diffeomorphic Diagrammatic Teaching} (SDDT) framework. SDDT models the robot's
motion as an \emph{Orbitally Asymptotically Stable} (O.A.S.) dynamical system
that learns to follow the user-specified sketch. This is achieved by applying a
\emph{diffeomorphism}, i.e. a differentiable and invertible function, to morph
a known O.A.S. system. The parameterised diffeomorphism is then optimised with
respect to the Hausdorff distance between the limit cycle of our modelled
system and the sketch, to produce the desired robot motion. We provide
theoretical insight into the behaviour of the optimised system and also
empirically evaluate SDDT, both in simulation and on a quadruped with a mounted
6-DOF manipulator. Results show that we can diagrammatically teach complex
cyclic motion patterns with a high degree of accuracy. | [
"Weiming Zhi",
"Kangni Liu",
"Tianyi Zhang",
"Matthew Johnson-Roberson"
] | 2023-09-19 04:03:42 | http://arxiv.org/abs/2309.10298v1 | http://arxiv.org/pdf/2309.10298v1 | 2309.10298v1 |
Koopman Invertible Autoencoder: Leveraging Forward and Backward Dynamics for Temporal Modeling | Accurate long-term predictions are the foundations for many machine learning
applications and decision-making processes. However, building accurate
long-term prediction models remains challenging due to the limitations of
existing temporal models like recurrent neural networks (RNNs), as they capture
only the statistical connections in the training data and may fail to learn the
underlying dynamics of the target system. To tackle this challenge, we propose
a novel machine learning model based on Koopman operator theory, which we call
Koopman Invertible Autoencoders (KIA), that captures the inherent
characteristic of the system by modeling both forward and backward dynamics in
the infinite-dimensional Hilbert space. This enables us to efficiently learn
low-dimensional representations, resulting in more accurate predictions of
long-term system behavior. Moreover, our method's invertibility design
guarantees reversibility and consistency in both forward and inverse
operations. We illustrate the utility of KIA on pendulum and climate datasets,
demonstrating 300% improvements in long-term prediction capability for pendulum
while maintaining robustness against noise. Additionally, our method excels in
long-term climate prediction, further validating our method's effectiveness. | [
"Kshitij Tayal",
"Arvind Renganathan",
"Rahul Ghosh",
"Xiaowei Jia",
"Vipin Kumar"
] | 2023-09-19 03:42:55 | http://arxiv.org/abs/2309.10291v1 | http://arxiv.org/pdf/2309.10291v1 | 2309.10291v1 |
Corporate Credit Rating: A Survey | Corporate credit rating (CCR) plays a very important role in the process of
contemporary economic and social development. How to use credit rating methods
for enterprises has always been a problem worthy of discussion. Through reading
and studying the relevant literature at home and abroad, this paper makes a
systematic survey of CCR. This paper combs the context of the development of
CCR methods from the three levels: statistical models, machine learning models
and neural network models, summarizes the common databases of CCR, and deeply
compares the advantages and disadvantages of the models. Finally, this paper
summarizes the problems existing in the current research and prospects the
future of CCR. Compared with the existing review of CCR, this paper expounds
and analyzes the progress of neural network model in this field in recent
years. | [
"Bojing Feng",
"Xi Cheng",
"Dan Li",
"Zeyu Liu",
"Wenfang Xue"
] | 2023-09-19 03:39:12 | http://arxiv.org/abs/2309.14349v1 | http://arxiv.org/pdf/2309.14349v1 | 2309.14349v1 |
Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity | With the fast growth of parameter size, it becomes increasingly challenging
to deploy large generative models as they typically require large GPU memory
consumption and massive computation. Unstructured model pruning has been a
common approach to reduce both GPU memory footprint and the overall computation
while retaining good model accuracy. However, the existing solutions do not
provide a highly-efficient support for handling unstructured sparsity on modern
GPUs, especially on the highly-structured Tensor Core hardware. Therefore, we
propose Flash-LLM for enabling low-cost and highly-efficient large generative
model inference with the sophisticated support of unstructured sparsity on
high-performance but highly restrictive Tensor Cores. Based on our key
observation that the main bottleneck of generative model inference is the
several skinny matrix multiplications for which Tensor Cores would be
significantly under-utilized due to low computational intensity, we propose a
general Load-as-Sparse and Compute-as-Dense methodology for unstructured sparse
matrix multiplication. The basic insight is to address the significant memory
bandwidth bottleneck while tolerating redundant computations that are not
critical for end-to-end performance on Tensor Cores. Based on this, we design
an effective software framework for Tensor Core based unstructured SpMM,
leveraging on-chip resources for efficient sparse data extraction and
computation/memory-access overlapping. At SpMM kernel level, Flash-LLM
significantly outperforms the state-of-the-art library, i.e., Sputnik and
SparTA by an average of 2.9x and 1.5x, respectively. At end-to-end framework
level on OPT-30B/66B/175B models, for tokens per GPU-second, Flash-LLM achieves
up to 3.8x and 3.6x improvement over DeepSpeed and FasterTransformer,
respectively, with significantly lower inference cost. | [
"Haojun Xia",
"Zhen Zheng",
"Yuchao Li",
"Donglin Zhuang",
"Zhongzhu Zhou",
"Xiafei Qiu",
"Yong Li",
"Wei Lin",
"Shuaiwen Leon Song"
] | 2023-09-19 03:20:02 | http://arxiv.org/abs/2309.10285v1 | http://arxiv.org/pdf/2309.10285v1 | 2309.10285v1 |
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning | Machine Unlearning is an emerging field that addresses data privacy issues by
enabling the removal of private or irrelevant data from the Machine Learning
process. Challenges related to privacy and model efficiency arise from the use
of outdated, private, and irrelevant data. These issues compromise both the
accuracy and the computational efficiency of models in both Machine Learning
and Unlearning. To mitigate these challenges, we introduce a novel framework,
Attention-based Machine Unlearning using Federated Reinforcement Learning
(FRAMU). This framework incorporates adaptive learning mechanisms, privacy
preservation techniques, and optimization strategies, making it a well-rounded
solution for handling various data sources, either single-modality or
multi-modality, while maintaining accuracy and privacy. FRAMU's strength lies
in its adaptability to fluctuating data landscapes, its ability to unlearn
outdated, private, or irrelevant data, and its support for continual model
evolution without compromising privacy. Our experiments, conducted on both
single-modality and multi-modality datasets, revealed that FRAMU significantly
outperformed baseline models. Additional assessments of convergence behavior
and optimization strategies further validate the framework's utility in
federated learning applications. Overall, FRAMU advances Machine Unlearning by
offering a robust, privacy-preserving solution that optimizes model performance
while also addressing key challenges in dynamic data environments. | [
"Thanveer Shaik",
"Xiaohui Tao",
"Lin Li",
"Haoran Xie",
"Taotao Cai",
"Xiaofeng Zhu",
"Qing Li"
] | 2023-09-19 03:13:17 | http://arxiv.org/abs/2309.10283v2 | http://arxiv.org/pdf/2309.10283v2 | 2309.10283v2 |
Crowdotic: A Privacy-Preserving Hospital Waiting Room Crowd Density Estimation with Non-speech Audio | Privacy-preserving crowd density analysis finds application across a wide
range of scenarios, substantially enhancing smart building operation and
management while upholding privacy expectations in various spaces. We propose a
non-speech audio-based approach for crowd analytics, leveraging a
transformer-based model. Our results demonstrate that non-speech audio alone
can be used to conduct such analysis with remarkable accuracy. To the best of
our knowledge, this is the first time when non-speech audio signals are
proposed for predicting occupancy. As far as we know, there has been no other
similar approach of its kind prior to this. To accomplish this, we deployed our
sensor-based platform in the waiting room of a large hospital with IRB approval
over a period of several months to capture non-speech audio and thermal images
for the training and evaluation of our models. The proposed non-speech-based
approach outperformed the thermal camera-based model and all other baselines.
In addition to demonstrating superior performance without utilizing speech
audio, we conduct further analysis using differential privacy techniques to
provide additional privacy guarantees. Overall, our work demonstrates the
viability of employing non-speech audio data for accurate occupancy estimation,
while also ensuring the exclusion of speech-related content and providing
robust privacy protections through differential privacy guarantees. | [
"Forsad Al Hossain",
"Tanjid Hasan Tonmoy",
"Andrew A. Lover",
"George A. Corey",
"Mohammad Arif Ul Alam",
"Tauhidur Rahman"
] | 2023-09-19 03:08:20 | http://arxiv.org/abs/2309.10280v2 | http://arxiv.org/pdf/2309.10280v2 | 2309.10280v2 |
Diffusion Methods for Generating Transition Paths | In this work, we seek to simulate rare transitions between metastable states
using score-based generative models. An efficient method for generating
high-quality transition paths is valuable for the study of molecular systems
since data is often difficult to obtain. We develop two novel methods for path
generation in this paper: a chain-based approach and a midpoint-based approach.
The first biases the original dynamics to facilitate transitions, while the
second mirrors splitting techniques and breaks down the original transition
into smaller transitions. Numerical results of generated transition paths for
the M\"uller potential and for Alanine dipeptide demonstrate the effectiveness
of these approaches in both the data-rich and data-scarce regimes. | [
"Luke Triplett",
"Jianfeng Lu"
] | 2023-09-19 03:03:03 | http://arxiv.org/abs/2309.10276v1 | http://arxiv.org/pdf/2309.10276v1 | 2309.10276v1 |
Crowd-Aware Multi-Agent Pathfinding With Boosted Curriculum Reinforcement Learning | Multi-Agent Path Finding (MAPF) in crowded environments presents a
challenging problem in motion planning, aiming to find collision-free paths for
all agents in the system. MAPF finds a wide range of applications in various
domains, including aerial swarms, autonomous warehouse robotics, and
self-driving vehicles. The current approaches for MAPF can be broadly
categorized into two main categories: centralized and decentralized planning.
Centralized planning suffers from the curse of dimensionality and thus does not
scale well in large and complex environments. On the other hand, decentralized
planning enables agents to engage in real-time path planning within a partially
observable environment, demonstrating implicit coordination. However, they
suffer from slow convergence and performance degradation in dense environments.
In this paper, we introduce CRAMP, a crowd-aware decentralized approach to
address this problem by leveraging reinforcement learning guided by a boosted
curriculum-based training strategy. We test CRAMP on simulated environments and
demonstrate that our method outperforms the state-of-the-art decentralized
methods for MAPF on various metrics. CRAMP improves the solution quality up to
58% measured in makespan and collision count, and up to 5% in success rate in
comparison to previous methods. | [
"Phu Pham",
"Aniket Bera"
] | 2023-09-19 03:02:43 | http://arxiv.org/abs/2309.10275v1 | http://arxiv.org/pdf/2309.10275v1 | 2309.10275v1 |
LLM Platform Security: Applying a Systematic Evaluation Framework to OpenAI's ChatGPT Plugins | Large language model (LLM) platforms, such as ChatGPT, have recently begun
offering a plugin ecosystem to interface with third-party services on the
internet. While these plugins extend the capabilities of LLM platforms, they
are developed by arbitrary third parties and thus cannot be implicitly trusted.
Plugins also interface with LLM platforms and users using natural language,
which can have imprecise interpretations. In this paper, we propose a framework
that lays a foundation for LLM platform designers to analyze and improve the
security, privacy, and safety of current and future plugin-integrated LLM
platforms. Our framework is a formulation of an attack taxonomy that is
developed by iteratively exploring how LLM platform stakeholders could leverage
their capabilities and responsibilities to mount attacks against each other. As
part of our iterative process, we apply our framework in the context of
OpenAI's plugin ecosystem. We uncover plugins that concretely demonstrate the
potential for the types of issues that we outline in our attack taxonomy. We
conclude by discussing novel challenges and by providing recommendations to
improve the security, privacy, and safety of present and future LLM-based
computing platforms. | [
"Umar Iqbal",
"Tadayoshi Kohno",
"Franziska Roesner"
] | 2023-09-19 02:20:10 | http://arxiv.org/abs/2309.10254v1 | http://arxiv.org/pdf/2309.10254v1 | 2309.10254v1 |
What is the Best Automated Metric for Text to Motion Generation? | There is growing interest in generating skeleton-based human motions from
natural language descriptions. While most efforts have focused on developing
better neural architectures for this task, there has been no significant work
on determining the proper evaluation metric. Human evaluation is the ultimate
accuracy measure for this task, and automated metrics should correlate well
with human quality judgments. Since descriptions are compatible with many
motions, determining the right metric is critical for evaluating and designing
effective generative models. This paper systematically studies which metrics
best align with human evaluations and proposes new metrics that align even
better. Our findings indicate that none of the metrics currently used for this
task show even a moderate correlation with human judgments on a sample level.
However, for assessing average model performance, commonly used metrics such as
R-Precision and less-used coordinate errors show strong correlations.
Additionally, several recently developed metrics are not recommended due to
their low correlation compared to alternatives. We also introduce a novel
metric based on a multimodal BERT-like model, MoBERT, which offers strongly
human-correlated sample-level evaluations while maintaining near-perfect
model-level correlation. Our results demonstrate that this new metric exhibits
extensive benefits over all current alternatives. | [
"Jordan Voas",
"Yili Wang",
"Qixing Huang",
"Raymond Mooney"
] | 2023-09-19 01:59:54 | http://arxiv.org/abs/2309.10248v1 | http://arxiv.org/pdf/2309.10248v1 | 2309.10248v1 |
On Explicit Curvature Regularization in Deep Generative Models | We propose a family of curvature-based regularization terms for deep
generative model learning. Explicit coordinate-invariant formulas for both
intrinsic and extrinsic curvature measures are derived for the case of
arbitrary data manifolds embedded in higher-dimensional Euclidean space.
Because computing the curvature is a highly computation-intensive process
involving the evaluation of second-order derivatives, efficient formulas are
derived for approximately evaluating intrinsic and extrinsic curvatures.
Comparative studies are conducted that compare the relative efficacy of
intrinsic versus extrinsic curvature-based regularization measures, as well as
performance comparisons against existing autoencoder training methods.
Experiments involving noisy motion capture data confirm that curvature-based
methods outperform existing autoencoder regularization methods, with intrinsic
curvature measures slightly more effective than extrinsic curvature measures. | [
"Yonghyeon Lee",
"Frank Chongwoo Park"
] | 2023-09-19 01:21:36 | http://arxiv.org/abs/2309.10237v1 | http://arxiv.org/pdf/2309.10237v1 | 2309.10237v1 |
Multi-fidelity climate model parameterization for better generalization and extrapolation | Machine-learning-based parameterizations (i.e. representation of sub-grid
processes) of global climate models or turbulent simulations have recently been
proposed as a powerful alternative to physical, but empirical, representations,
offering a lower computational cost and higher accuracy. Yet, those approaches
still suffer from a lack of generalization and extrapolation beyond the
training data, which is however critical to projecting climate change or
unobserved regimes of turbulence. Here we show that a multi-fidelity approach,
which integrates datasets of different accuracy and abundance, can provide the
best of both worlds: the capacity to extrapolate leveraging the
physically-based parameterization and a higher accuracy using the
machine-learning-based parameterizations. In an application to climate
modeling, the multi-fidelity framework yields more accurate climate projections
without requiring major increase in computational resources. Our multi-fidelity
randomized prior networks (MF-RPNs) combine physical parameterization data as
low-fidelity and storm-resolving historical run's data as high-fidelity. To
extrapolate beyond the training data, the MF-RPNs are tested on high-fidelity
warming scenarios, $+4K$, data. We show the MF-RPN's capacity to return much
more skillful predictions compared to either low- or high-fidelity (historical
data) simulations trained only on one regime while providing trustworthy
uncertainty quantification across a wide range of scenarios. Our approach paves
the way for the use of machine-learning based methods that can optimally
leverage historical observations or high-fidelity simulations and extrapolate
to unseen regimes such as climate change. | [
"Mohamed Aziz Bhouri",
"Liran Peng",
"Michael S. Pritchard",
"Pierre Gentine"
] | 2023-09-19 01:03:39 | http://arxiv.org/abs/2309.10231v1 | http://arxiv.org/pdf/2309.10231v1 | 2309.10231v1 |
Causal Theories and Structural Data Representations for Improving Out-of-Distribution Classification | We consider how human-centered causal theories and tools from the dynamical
systems literature can be deployed to guide the representation of data when
training neural networks for complex classification tasks. Specifically, we use
simulated data to show that training a neural network with a data
representation that makes explicit the invariant structural causal features of
the data generating process of an epidemic system improves out-of-distribution
(OOD) generalization performance on a classification task as compared to a more
naive approach to data representation. We take these results to demonstrate
that using human-generated causal knowledge to reduce the epistemic uncertainty
of ML developers can lead to more well-specified ML pipelines. This, in turn,
points to the utility of a dynamical systems approach to the broader effort
aimed at improving the robustness and safety of machine learning systems via
improved ML system development practices. | [
"Donald Martin, Jr.",
"David Kinney"
] | 2023-09-18 23:49:42 | http://arxiv.org/abs/2309.10211v1 | http://arxiv.org/pdf/2309.10211v1 | 2309.10211v1 |
The Kernel Density Integral Transformation | Feature preprocessing continues to play a critical role when applying machine
learning and statistical methods to tabular data. In this paper, we propose the
use of the kernel density integral transformation as a feature preprocessing
step. Our approach subsumes the two leading feature preprocessing methods as
limiting cases: linear min-max scaling and quantile transformation. We
demonstrate that, without hyperparameter tuning, the kernel density integral
transformation can be used as a simple drop-in replacement for either method,
offering protection from the weaknesses of each. Alternatively, with tuning of
a single continuous hyperparameter, we frequently outperform both of these
methods. Finally, we show that the kernel density transformation can be
profitably applied to statistical data analysis, particularly in correlation
analysis and univariate clustering. | [
"Calvin McCarter"
] | 2023-09-18 22:54:05 | http://arxiv.org/abs/2309.10194v2 | http://arxiv.org/pdf/2309.10194v2 | 2309.10194v2 |
Stochastic Deep Koopman Model for Quality Propagation Analysis in Multistage Manufacturing Systems | The modeling of multistage manufacturing systems (MMSs) has attracted
increased attention from both academia and industry. Recent advancements in
deep learning methods provide an opportunity to accomplish this task with
reduced cost and expertise. This study introduces a stochastic deep Koopman
(SDK) framework to model the complex behavior of MMSs. Specifically, we present
a novel application of Koopman operators to propagate critical quality
information extracted by variational autoencoders. Through this framework, we
can effectively capture the general nonlinear evolution of product quality
using a transferred linear representation, thus enhancing the interpretability
of the data-driven model. To evaluate the performance of the SDK framework, we
carried out a comparative study on an open-source dataset. The main findings of
this paper are as follows. Our results indicate that SDK surpasses other
popular data-driven models in accuracy when predicting stagewise product
quality within the MMS. Furthermore, the unique linear propagation property in
the stochastic latent space of SDK enables traceability for quality evolution
throughout the process, thereby facilitating the design of root cause analysis
schemes. Notably, the proposed framework requires minimal knowledge of the
underlying physics of production lines. It serves as a virtual metrology tool
that can be applied to various MMSs, contributing to the ultimate goal of Zero
Defect Manufacturing. | [
"Zhiyi Chen",
"Harshal Maske",
"Huanyi Shui",
"Devesh Upadhyay",
"Michael Hopka",
"Joseph Cohen",
"Xingjian Lai",
"Xun Huan",
"Jun Ni"
] | 2023-09-18 22:53:17 | http://arxiv.org/abs/2309.10193v1 | http://arxiv.org/pdf/2309.10193v1 | 2309.10193v1 |
Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence | Reinforcement learning is well known for its ability to model sequential
tasks and learn latent data patterns adaptively. Deep learning models have been
widely explored and adopted in regression and classification tasks. However,
deep learning has its limitations such as the assumption of equally spaced and
ordered data, and the lack of ability to incorporate graph structure in terms
of time-series prediction. Graphical neural network (GNN) has the ability to
overcome these challenges and capture the temporal dependencies in time-series
data. In this study, we propose a novel approach for predicting time-series
data using GNN and monitoring with Reinforcement Learning (RL). GNNs are able
to explicitly incorporate the graph structure of the data into the model,
allowing them to capture temporal dependencies in a more natural way. This
approach allows for more accurate predictions in complex temporal structures,
such as those found in healthcare, traffic and weather forecasting. We also
fine-tune our GraphRL model using a Bayesian optimisation technique to further
improve performance. The proposed framework outperforms the baseline models in
time-series forecasting and monitoring. The contributions of this study include
the introduction of a novel GraphRL framework for time-series prediction and
the demonstration of the effectiveness of GNNs in comparison to traditional
deep learning models such as RNNs and LSTMs. Overall, this study demonstrates
the potential of GraphRL in providing accurate and efficient predictions in
dynamic RL environments. | [
"Thanveer Shaik",
"Xiaohui Tao",
"Haoran Xie",
"Lin Li",
"Jianming Yong",
"Yuefeng Li"
] | 2023-09-18 22:25:12 | http://arxiv.org/abs/2309.10186v1 | http://arxiv.org/pdf/2309.10186v1 | 2309.10186v1 |
QoS-Aware Service Prediction and Orchestration in Cloud-Network Integrated Beyond 5G | Novel applications such as the Metaverse have highlighted the potential of
beyond 5G networks, which necessitate ultra-low latency communications and
massive broadband connections. Moreover, the burgeoning demand for such
services with ever-fluctuating users has engendered a need for heightened
service continuity consideration in B5G. To enable these services, the
edge-cloud paradigm is a potential solution to harness cloud capacity and
effectively manage users in real time as they move across the network. However,
edge-cloud networks confront a multitude of limitations, including networking
and computing resources that must be collectively managed to unlock their full
potential. This paper addresses the joint problem of service placement and
resource allocation in a network-cloud integrated environment while considering
capacity constraints, dynamic users, and end-to-end delays. We present a
non-linear programming model that formulates the optimization problem with the
aiming objective of minimizing overall cost while enhancing latency. Next, to
address the problem, we introduce a DDQL-based technique using RNNs to predict
user behavior, empowered by a water-filling-based algorithm for service
placement. The proposed framework adeptly accommodates the dynamic nature of
users, the placement of services that mandate ultra-low latency in B5G, and
service continuity when users migrate from one location to another. Simulation
results show that our solution provides timely responses that optimize the
network's potential, offering a scalable and efficient placement. | [
"Mohammad Farhoudi",
"Masoud Shokrnezhad",
"Tarik Taleb"
] | 2023-09-18 22:24:42 | http://arxiv.org/abs/2309.10185v1 | http://arxiv.org/pdf/2309.10185v1 | 2309.10185v1 |
Double Deep Q-Learning-based Path Selection and Service Placement for Latency-Sensitive Beyond 5G Applications | Nowadays, as the need for capacity continues to grow, entirely novel services
are emerging. A solid cloud-network integrated infrastructure is necessary to
supply these services in a real-time responsive, and scalable way. Due to their
diverse characteristics and limited capacity, communication and computing
resources must be collaboratively managed to unleash their full potential.
Although several innovative methods have been proposed to orchestrate the
resources, most ignored network resources or relaxed the network as a simple
graph, focusing only on cloud resources. This paper fills the gap by studying
the joint problem of communication and computing resource allocation, dubbed
CCRA, including function placement and assignment, traffic prioritization, and
path selection considering capacity constraints and quality requirements, to
minimize total cost. We formulate the problem as a non-linear programming model
and propose two approaches, dubbed B\&B-CCRA and WF-CCRA, based on the Branch
\& Bound and Water-Filling algorithms to solve it when the system is fully
known. Then, for partially known systems, a Double Deep Q-Learning (DDQL)
architecture is designed. Numerical simulations show that B\&B-CCRA optimally
solves the problem, whereas WF-CCRA delivers near-optimal solutions in a
substantially shorter time. Furthermore, it is demonstrated that DDQL-CCRA
obtains near-optimal solutions in the absence of request-specific information. | [
"Masoud Shokrnezhad",
"Tarik Taleb",
"Patrizio Dazzi"
] | 2023-09-18 22:17:23 | http://arxiv.org/abs/2309.10180v1 | http://arxiv.org/pdf/2309.10180v1 | 2309.10180v1 |
Self-Sustaining Multiple Access with Continual Deep Reinforcement Learning for Dynamic Metaverse Applications | The Metaverse is a new paradigm that aims to create a virtual environment
consisting of numerous worlds, each of which will offer a different set of
services. To deal with such a dynamic and complex scenario, considering the
stringent quality of service requirements aimed at the 6th generation of
communication systems (6G), one potential approach is to adopt self-sustaining
strategies, which can be realized by employing Adaptive Artificial Intelligence
(Adaptive AI) where models are continually re-trained with new data and
conditions. One aspect of self-sustainability is the management of multiple
access to the frequency spectrum. Although several innovative methods have been
proposed to address this challenge, mostly using Deep Reinforcement Learning
(DRL), the problem of adapting agents to a non-stationary environment has not
yet been precisely addressed. This paper fills in the gap in the current
literature by investigating the problem of multiple access in multi-channel
environments to maximize the throughput of the intelligent agent when the
number of active User Equipments (UEs) may fluctuate over time. To solve the
problem, a Double Deep Q-Learning (DDQL) technique empowered by Continual
Learning (CL) is proposed to overcome the non-stationary situation, while the
environment is unknown. Numerical simulations demonstrate that, compared to
other well-known methods, the CL-DDQL algorithm achieves significantly higher
throughputs with a considerably shorter convergence time in highly dynamic
scenarios. | [
"Hamidreza Mazandarani",
"Masoud Shokrnezhad",
"Tarik Taleb",
"Richard Li"
] | 2023-09-18 22:02:47 | http://arxiv.org/abs/2309.10177v1 | http://arxiv.org/pdf/2309.10177v1 | 2309.10177v1 |
One ACT Play: Single Demonstration Behavior Cloning with Action Chunking Transformers | Learning from human demonstrations (behavior cloning) is a cornerstone of
robot learning. However, most behavior cloning algorithms require a large
number of demonstrations to learn a task, especially for general tasks that
have a large variety of initial conditions. Humans, however, can learn to
complete tasks, even complex ones, after only seeing one or two demonstrations.
Our work seeks to emulate this ability, using behavior cloning to learn a task
given only a single human demonstration. We achieve this goal by using linear
transforms to augment the single demonstration, generating a set of
trajectories for a wide range of initial conditions. With these demonstrations,
we are able to train a behavior cloning agent to successfully complete three
block manipulation tasks. Additionally, we developed a novel addition to the
temporal ensembling method used by action chunking agents during inference. By
incorporating the standard deviation of the action predictions into the
ensembling method, our approach is more robust to unforeseen changes in the
environment, resulting in significant performance improvements. | [
"Abraham George",
"Amir Barati Farimani"
] | 2023-09-18 21:50:26 | http://arxiv.org/abs/2309.10175v1 | http://arxiv.org/pdf/2309.10175v1 | 2309.10175v1 |
Generative modeling, design and analysis of spider silk protein sequences for enhanced mechanical properties | Spider silks are remarkable materials characterized by superb mechanical
properties such as strength, extensibility and lightweightedness. Yet, to date,
limited models are available to fully explore sequence-property relationships
for analysis and design. Here we propose a custom generative large-language
model to enable design of novel spider silk protein sequences to meet complex
combinations of target mechanical properties. The model, pretrained on a large
set of protein sequences, is fine-tuned on ~1,000 major ampullate spidroin
(MaSp) sequences for which associated fiber-level mechanical properties exist,
to yield an end-to-end forward and inverse generative strategy. Performance is
assessed through: (1), a novelty analysis and protein type classification for
generated spidroin sequences through BLAST searches, (2) property evaluation
and comparison with similar sequences, (3) comparison of molecular structures,
as well as, and (4) a detailed sequence motif analyses. We generate silk
sequences with property combinations that do not exist in nature, and develop a
deep understanding the mechanistic roles of sequence patterns in achieving
overarching key mechanical properties (elastic modulus, strength, toughness,
failure strain). The model provides an efficient approach to expand the silkome
dataset, facilitating further sequence-structure analyses of silks, and
establishes a foundation for synthetic silk design and optimization. | [
"Wei Lu",
"David L. Kaplan",
"Markus J. Buehler"
] | 2023-09-18 21:38:40 | http://arxiv.org/abs/2309.10170v1 | http://arxiv.org/pdf/2309.10170v1 | 2309.10170v1 |
Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter | The CMS detector is a general-purpose apparatus that detects high-energy
collisions produced at the LHC. Online Data Quality Monitoring of the CMS
electromagnetic calorimeter is a vital operational tool that allows detector
experts to quickly identify, localize, and diagnose a broad range of detector
issues that could affect the quality of physics data. A real-time
autoencoder-based anomaly detection system using semi-supervised machine
learning is presented enabling the detection of anomalies in the CMS
electromagnetic calorimeter data. A novel method is introduced which maximizes
the anomaly detection performance by exploiting the time-dependent evolution of
anomalies as well as spatial variations in the detector response. The
autoencoder-based system is able to efficiently detect anomalies, while
maintaining a very low false discovery rate. The performance of the system is
validated with anomalies found in 2018 and 2022 LHC collision data.
Additionally, the first results from deploying the autoencoder-based system in
the CMS online Data Quality Monitoring workflow during the beginning of Run 3
of the LHC are presented, showing its ability to detect issues missed by the
existing system. | [
"The CMS ECAL Collaboration"
] | 2023-09-18 21:11:25 | http://arxiv.org/abs/2309.10157v1 | http://arxiv.org/pdf/2309.10157v1 | 2309.10157v1 |
Preserving Tumor Volumes for Unsupervised Medical Image Registration | Medical image registration is a critical task that estimates the spatial
correspondence between pairs of images. However, current traditional and
deep-learning-based methods rely on similarity measures to generate a deforming
field, which often results in disproportionate volume changes in dissimilar
regions, especially in tumor regions. These changes can significantly alter the
tumor size and underlying anatomy, which limits the practical use of image
registration in clinical diagnosis. To address this issue, we have formulated
image registration with tumors as a constraint problem that preserves tumor
volumes while maximizing image similarity in other normal regions. Our proposed
strategy involves a two-stage process. In the first stage, we use
similarity-based registration to identify potential tumor regions by their
volume change, generating a soft tumor mask accordingly. In the second stage,
we propose a volume-preserving registration with a novel adaptive
volume-preserving loss that penalizes the change in size adaptively based on
the masks calculated from the previous stage. Our approach balances image
similarity and volume preservation in different regions, i.e., normal and tumor
regions, by using soft tumor masks to adjust the imposition of
volume-preserving loss on each one. This ensures that the tumor volume is
preserved during the registration process. We have evaluated our strategy on
various datasets and network architectures, demonstrating that our method
successfully preserves the tumor volume while achieving comparable registration
results with state-of-the-art methods. Our codes is available at:
\url{https://dddraxxx.github.io/Volume-Preserving-Registration/}. | [
"Qihua Dong",
"Hao Du",
"Ying Song",
"Yan Xu",
"Jing Liao"
] | 2023-09-18 21:02:36 | http://arxiv.org/abs/2309.10153v1 | http://arxiv.org/pdf/2309.10153v1 | 2309.10153v1 |
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions | In this work, we present a scalable reinforcement learning method for
training multi-task policies from large offline datasets that can leverage both
human demonstrations and autonomously collected data. Our method uses a
Transformer to provide a scalable representation for Q-functions trained via
offline temporal difference backups. We therefore refer to the method as
Q-Transformer. By discretizing each action dimension and representing the
Q-value of each action dimension as separate tokens, we can apply effective
high-capacity sequence modeling techniques for Q-learning. We present several
design decisions that enable good performance with offline RL training, and
show that Q-Transformer outperforms prior offline RL algorithms and imitation
learning techniques on a large diverse real-world robotic manipulation task
suite. The project's website and videos can be found at
https://qtransformer.github.io | [
"Yevgen Chebotar",
"Quan Vuong",
"Alex Irpan",
"Karol Hausman",
"Fei Xia",
"Yao Lu",
"Aviral Kumar",
"Tianhe Yu",
"Alexander Herzog",
"Karl Pertsch",
"Keerthana Gopalakrishnan",
"Julian Ibarz",
"Ofir Nachum",
"Sumedh Sontakke",
"Grecia Salazar",
"Huong T Tran",
"Jodilyn Peralta",
"Clayton Tan",
"Deeksha Manjunath",
"Jaspiar Singht",
"Brianna Zitkovich",
"Tomas Jackson",
"Kanishka Rao",
"Chelsea Finn",
"Sergey Levine"
] | 2023-09-18 21:00:38 | http://arxiv.org/abs/2309.10150v2 | http://arxiv.org/pdf/2309.10150v2 | 2309.10150v2 |
Analysis of the Memorization and Generalization Capabilities of AI Agents: Are Continual Learners Robust? | In continual learning (CL), an AI agent (e.g., autonomous vehicles or
robotics) learns from non-stationary data streams under dynamic environments.
For the practical deployment of such applications, it is important to guarantee
robustness to unseen environments while maintaining past experiences. In this
paper, a novel CL framework is proposed to achieve robust generalization to
dynamic environments while retaining past knowledge. The considered CL agent
uses a capacity-limited memory to save previously observed environmental
information to mitigate forgetting issues. Then, data points are sampled from
the memory to estimate the distribution of risks over environmental change so
as to obtain predictors that are robust with unseen changes. The generalization
and memorization performance of the proposed framework are theoretically
analyzed. This analysis showcases the tradeoff between memorization and
generalization with the memory size. Experiments show that the proposed
algorithm outperforms memory-based CL baselines across all environments while
significantly improving the generalization performance on unseen target
environments. | [
"Minsu Kim",
"Walid Saad"
] | 2023-09-18 21:00:01 | http://arxiv.org/abs/2309.10149v1 | http://arxiv.org/pdf/2309.10149v1 | 2309.10149v1 |
Realistic Website Fingerprinting By Augmenting Network Trace | Website Fingerprinting (WF) is considered a major threat to the anonymity of
Tor users (and other anonymity systems). While state-of-the-art WF techniques
have claimed high attack accuracies, e.g., by leveraging Deep Neural Networks
(DNN), several recent works have questioned the practicality of such WF attacks
in the real world due to the assumptions made in the design and evaluation of
these attacks. In this work, we argue that such impracticality issues are
mainly due to the attacker's inability in collecting training data in
comprehensive network conditions, e.g., a WF classifier may be trained only on
samples collected on specific high-bandwidth network links but deployed on
connections with different network conditions. We show that augmenting network
traces can enhance the performance of WF classifiers in unobserved network
conditions. Specifically, we introduce NetAugment, an augmentation technique
tailored to the specifications of Tor traces. We instantiate NetAugment through
semi-supervised and self-supervised learning techniques. Our extensive
open-world and close-world experiments demonstrate that under practical
evaluation settings, our WF attacks provide superior performances compared to
the state-of-the-art; this is due to their use of augmented network traces for
training, which allows them to learn the features of target traffic in
unobserved settings. For instance, with a 5-shot learning in a closed-world
scenario, our self-supervised WF attack (named NetCLR) reaches up to 80%
accuracy when the traces for evaluation are collected in a setting unobserved
by the WF adversary. This is compared to an accuracy of 64.4% achieved by the
state-of-the-art Triplet Fingerprinting [35]. We believe that the promising
results of our work can encourage the use of network trace augmentation in
other types of network traffic analysis. | [
"Alireza Bahramali",
"Ardavan Bozorgi",
"Amir Houmansadr"
] | 2023-09-18 20:57:52 | http://arxiv.org/abs/2309.10147v1 | http://arxiv.org/pdf/2309.10147v1 | 2309.10147v1 |
Tree-Based Reconstructive Partitioning: A Novel Low-Data Level Generation Approach | Procedural Content Generation (PCG) is the algorithmic generation of content,
often applied to games. PCG and PCG via Machine Learning (PCGML) have appeared
in published games. However, it can prove difficult to apply these approaches
in the early stages of an in-development game. PCG requires expertise in
representing designer notions of quality in rules or functions, and PCGML
typically requires significant training data, which may not be available early
in development. In this paper, we introduce Tree-based Reconstructive
Partitioning (TRP), a novel PCGML approach aimed to address this problem. Our
results, across two domains, demonstrate that TRP produces levels that are more
playable and coherent, and that the approach is more generalizable with less
training data. We consider TRP to be a promising new approach that can afford
the introduction of PCGML into the early stages of game development without
requiring human expertise or significant training data. | [
"Emily Halina",
"Matthew Guzdial"
] | 2023-09-18 20:39:14 | http://arxiv.org/abs/2309.13071v1 | http://arxiv.org/pdf/2309.13071v1 | 2309.13071v1 |
A Geometric Framework for Neural Feature Learning | We present a novel framework for learning system design based on neural
feature extractors by exploiting geometric structures in feature spaces. First,
we introduce the feature geometry, which unifies statistical dependence and
features in the same functional space with geometric structures. By applying
the feature geometry, we formulate each learning problem as solving the optimal
feature approximation of the dependence component specified by the learning
setting. We propose a nesting technique for designing learning algorithms to
learn the optimal features from data samples, which can be applied to
off-the-shelf network architectures and optimizers. To demonstrate the
application of the nesting technique, we further discuss multivariate learning
problems, including conditioned inference and multimodal learning, where we
present the optimal features and reveal their connections to classical
approaches. | [
"Xiangxiang Xu",
"Lizhong Zheng"
] | 2023-09-18 20:39:12 | http://arxiv.org/abs/2309.10140v1 | http://arxiv.org/pdf/2309.10140v1 | 2309.10140v1 |
Efficient Low-Rank GNN Defense Against Structural Attacks | Graph Neural Networks (GNNs) have been shown to possess strong representation
abilities over graph data. However, GNNs are vulnerable to adversarial attacks,
and even minor perturbations to the graph structure can significantly degrade
their performance. Existing methods either are ineffective against
sophisticated attacks or require the optimization of dense adjacency matrices,
which is time-consuming and prone to local minima. To remedy this problem, we
propose an Efficient Low-Rank Graph Neural Network (ELR-GNN) defense method,
which aims to learn low-rank and sparse graph structures for defending against
adversarial attacks, ensuring effective defense with greater efficiency.
Specifically, ELR-GNN consists of two modules: a Coarse Low-Rank Estimation
Module and a Fine-Grained Estimation Module. The first module adopts the
truncated Singular Value Decomposition (SVD) to initialize the low-rank
adjacency matrix estimation, which serves as a starting point for optimizing
the low-rank matrix. In the second module, the initial estimate is refined by
jointly learning a low-rank sparse graph structure with the GNN model. Sparsity
is incorporated into the learned low-rank adjacency matrix by pruning weak
connections, which can reduce redundant data while maintaining valuable
information. As a result, instead of using the dense adjacency matrix directly,
ELR-GNN can learn a low-rank and sparse estimate of it in a simple, efficient
and easy to optimize manner. The experimental results demonstrate that ELR-GNN
outperforms the state-of-the-art GNN defense methods in the literature, in
addition to being very efficient and easy to train. | [
"Abdullah Alchihabi",
"Qing En",
"Yuhong Guo"
] | 2023-09-18 20:22:27 | http://arxiv.org/abs/2309.10136v1 | http://arxiv.org/pdf/2309.10136v1 | 2309.10136v1 |
GDM: Dual Mixup for Graph Classification with Limited Supervision | Graph Neural Networks (GNNs) require a large number of labeled graph samples
to obtain good performance on the graph classification task. The performance of
GNNs degrades significantly as the number of labeled graph samples decreases.
To reduce the annotation cost, it is therefore important to develop graph
augmentation methods that can generate new graph instances to increase the size
and diversity of the limited set of available labeled graph samples. In this
work, we propose a novel mixup-based graph augmentation method, Graph Dual
Mixup (GDM), that leverages both functional and structural information of the
graph instances to generate new labeled graph samples. GDM employs a graph
structural auto-encoder to learn structural embeddings of the graph samples,
and then applies mixup to the structural information of the graphs in the
learned structural embedding space and generates new graph structures from the
mixup structural embeddings. As for the functional information, GDM applies
mixup directly to the input node features of the graph samples to generate
functional node feature information for new mixup graph instances. Jointly, the
generated input node features and graph structures yield new graph samples
which can supplement the set of original labeled graphs. Furthermore, we
propose two novel Balanced Graph Sampling methods to enhance the balanced
difficulty and diversity for the generated graph samples. Experimental results
on the benchmark datasets demonstrate that our proposed method substantially
outperforms the state-of-the-art graph augmentation methods when the labeled
graphs are scarce. | [
"Abdullah Alchihabi",
"Yuhong Guo"
] | 2023-09-18 20:17:10 | http://arxiv.org/abs/2309.10134v1 | http://arxiv.org/pdf/2309.10134v1 | 2309.10134v1 |
Deep Prompt Tuning for Graph Transformers | Graph transformers have gained popularity in various graph-based tasks by
addressing challenges faced by traditional Graph Neural Networks. However, the
quadratic complexity of self-attention operations and the extensive layering in
graph transformer architectures present challenges when applying them to graph
based prediction tasks. Fine-tuning, a common approach, is resource-intensive
and requires storing multiple copies of large models. We propose a novel
approach called deep graph prompt tuning as an alternative to fine-tuning for
leveraging large graph transformer models in downstream graph based prediction
tasks. Our method introduces trainable feature nodes to the graph and pre-pends
task-specific tokens to the graph transformer, enhancing the model's expressive
power. By freezing the pre-trained parameters and only updating the added
tokens, our approach reduces the number of free parameters and eliminates the
need for multiple model copies, making it suitable for small datasets and
scalable to large graphs. Through extensive experiments on various-sized
datasets, we demonstrate that deep graph prompt tuning achieves comparable or
even superior performance to fine-tuning, despite utilizing significantly fewer
task-specific parameters. Our contributions include the introduction of prompt
tuning for graph transformers, its application to both graph transformers and
message passing graph neural networks, improved efficiency and resource
utilization, and compelling experimental results. This work brings attention to
a promising approach to leverage pre-trained models in graph based prediction
tasks and offers new opportunities for exploring and advancing graph
representation learning. | [
"Reza Shirkavand",
"Heng Huang"
] | 2023-09-18 20:12:17 | http://arxiv.org/abs/2309.10131v1 | http://arxiv.org/pdf/2309.10131v1 | 2309.10131v1 |
Deep smoothness WENO scheme for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators | In this paper, we introduce an improved version of the fifth-order weighted
essentially non-oscillatory (WENO) shock-capturing scheme by incorporating deep
learning techniques. The established WENO algorithm is improved by training a
compact neural network to adjust the smoothness indicators within the WENO
scheme. This modification enhances the accuracy of the numerical results,
particularly near abrupt shocks. Unlike previous deep learning-based methods,
no additional post-processing steps are necessary for maintaining consistency.
We demonstrate the superiority of our new approach using several examples from
the literature for the two-dimensional Euler equations of gas dynamics. Through
intensive study of these test problems, which involve various shocks and
rarefaction waves, the new technique is shown to outperform traditional
fifth-order WENO schemes, especially in cases where the numerical solutions
exhibit excessive diffusion or overshoot around shocks. | [
"Tatiana Kossaczká",
"Ameya D. Jagtap",
"Matthias Ehrhardt"
] | 2023-09-18 19:42:35 | http://arxiv.org/abs/2309.10117v1 | http://arxiv.org/pdf/2309.10117v1 | 2309.10117v1 |
AR-TTA: A Simple Method for Real-World Continual Test-Time Adaptation | Test-time adaptation is a promising research direction that allows the source
model to adapt itself to changes in data distribution without any supervision.
Yet, current methods are usually evaluated on benchmarks that are only a
simplification of real-world scenarios. Hence, we propose to validate test-time
adaptation methods using the recently introduced datasets for autonomous
driving, namely CLAD-C and SHIFT. We observe that current test-time adaptation
methods struggle to effectively handle varying degrees of domain shift, often
resulting in degraded performance that falls below that of the source model. We
noticed that the root of the problem lies in the inability to preserve the
knowledge of the source model and adapt to dynamically changing, temporally
correlated data streams. Therefore, we enhance well-established self-training
framework by incorporating a small memory buffer to increase model stability
and at the same time perform dynamic adaptation based on the intensity of
domain shift. The proposed method, named AR-TTA, outperforms existing
approaches on both synthetic and more real-world benchmarks and shows
robustness across a variety of TTA scenarios. | [
"Damian Sójka",
"Sebastian Cygert",
"Bartłomiej Twardowski",
"Tomasz Trzciński"
] | 2023-09-18 19:34:23 | http://arxiv.org/abs/2309.10109v1 | http://arxiv.org/pdf/2309.10109v1 | 2309.10109v1 |
Understanding Catastrophic Forgetting in Language Models via Implicit Inference | Fine-tuning (via methods such as instruction-tuning or reinforcement learning
from human feedback) is a crucial step in training language models to robustly
carry out tasks of interest. However, we lack a systematic understanding of the
effects of fine-tuning, particularly on tasks outside the narrow fine-tuning
distribution. In a simplified scenario, we demonstrate that improving
performance on tasks within the fine-tuning data distribution comes at the
expense of suppressing model capabilities on other tasks. This degradation is
especially pronounced for tasks "closest" to the fine-tuning distribution. We
hypothesize that language models implicitly infer the task of the prompt
corresponds, and the fine-tuning process predominantly skews this task
inference towards tasks in the fine-tuning distribution. To test this
hypothesis, we propose Conjugate Prompting to see if we can recover pretrained
capabilities. Conjugate prompting artificially makes the task look farther from
the fine-tuning distribution while requiring the same capability. We find that
conjugate prompting systematically recovers some of the pretraining
capabilities on our synthetic setup. We then apply conjugate prompting to
real-world LLMs using the observation that fine-tuning distributions are
typically heavily skewed towards English. We find that simply translating the
prompts to different languages can cause the fine-tuned models to respond like
their pretrained counterparts instead. This allows us to recover the in-context
learning abilities lost via instruction tuning, and more concerningly, to
recover harmful content generation suppressed by safety fine-tuning in chatbots
like ChatGPT. | [
"Suhas Kotha",
"Jacob Mitchell Springer",
"Aditi Raghunathan"
] | 2023-09-18 19:28:48 | http://arxiv.org/abs/2309.10105v1 | http://arxiv.org/pdf/2309.10105v1 | 2309.10105v1 |
Machine Learning Technique Based Fake News Detection | False news has received attention from both the general public and the
scholarly world. Such false information has the ability to affect public
perception, giving nefarious groups the chance to influence the results of
public events like elections. Anyone can share fake news or facts about anyone
or anything for their personal gain or to cause someone trouble. Also,
information varies depending on the part of the world it is shared on. Thus, in
this paper, we have trained a model to classify fake and true news by utilizing
the 1876 news data from our collected dataset. We have preprocessed the data to
get clean and filtered texts by following the Natural Language Processing
approaches. Our research conducts 3 popular Machine Learning (Stochastic
gradient descent, Na\"ive Bayes, Logistic Regression,) and 2 Deep Learning
(Long-Short Term Memory, ASGD Weight-Dropped LSTM, or AWD-LSTM) algorithms.
After we have found our best Naive Bayes classifier with 56% accuracy and an
F1-macro score of an average of 32%. | [
"Biplob Kumar Sutradhar",
"Md. Zonaid",
"Nushrat Jahan Ria",
"Sheak Rashed Haider Noori"
] | 2023-09-18 19:26:54 | http://arxiv.org/abs/2309.13069v1 | http://arxiv.org/pdf/2309.13069v1 | 2309.13069v1 |
A Semi-Supervised Approach for Power System Event Identification | Event identification is increasingly recognized as crucial for enhancing the
reliability, security, and stability of the electric power system. With the
growing deployment of Phasor Measurement Units (PMUs) and advancements in data
science, there are promising opportunities to explore data-driven event
identification via machine learning classification techniques. However,
obtaining accurately-labeled eventful PMU data samples remains challenging due
to its labor-intensive nature and uncertainty about the event type (class) in
real-time. Thus, it is natural to use semi-supervised learning techniques,
which make use of both labeled and unlabeled samples. %We propose a novel
semi-supervised framework to assess the effectiveness of incorporating
unlabeled eventful samples to enhance existing event identification
methodologies. We evaluate three categories of classical semi-supervised
approaches: (i) self-training, (ii) transductive support vector machines
(TSVM), and (iii) graph-based label spreading (LS) method. Our approach
characterizes events using physically interpretable features extracted from
modal analysis of synthetic eventful PMU data. In particular, we focus on the
identification of four event classes whose identification is crucial for grid
operations. We have developed and publicly shared a comprehensive Event
Identification package which consists of three aspects: data generation,
feature extraction, and event identification with limited labels using
semi-supervised methodologies. Using this package, we generate and evaluate
eventful PMU data for the South Carolina synthetic network. Our evaluation
consistently demonstrates that graph-based LS outperforms the other two
semi-supervised methods that we consider, and can noticeably improve event
identification performance relative to the setting with only a small number of
labeled samples. | [
"Nima Taghipourbazargani",
"Lalitha Sankar",
"Oliver Kosut"
] | 2023-09-18 19:07:41 | http://arxiv.org/abs/2309.10095v1 | http://arxiv.org/pdf/2309.10095v1 | 2309.10095v1 |
Unified Coarse-to-Fine Alignment for Video-Text Retrieval | The canonical approach to video-text retrieval leverages a coarse-grained or
fine-grained alignment between visual and textual information. However,
retrieving the correct video according to the text query is often challenging
as it requires the ability to reason about both high-level (scene) and
low-level (object) visual clues and how they relate to the text query. To this
end, we propose a Unified Coarse-to-fine Alignment model, dubbed UCoFiA.
Specifically, our model captures the cross-modal similarity information at
different granularity levels. To alleviate the effect of irrelevant visual
clues, we also apply an Interactive Similarity Aggregation module (ISA) to
consider the importance of different visual features while aggregating the
cross-modal similarity to obtain a similarity score for each granularity.
Finally, we apply the Sinkhorn-Knopp algorithm to normalize the similarities of
each level before summing them, alleviating over- and under-representation
issues at different levels. By jointly considering the crossmodal similarity of
different granularity, UCoFiA allows the effective unification of multi-grained
alignments. Empirically, UCoFiA outperforms previous state-of-the-art
CLIP-based methods on multiple video-text retrieval benchmarks, achieving 2.4%,
1.4% and 1.3% improvements in text-to-video retrieval R@1 on MSR-VTT,
Activity-Net, and DiDeMo, respectively. Our code is publicly available at
https://github.com/Ziyang412/UCoFiA. | [
"Ziyang Wang",
"Yi-Lin Sung",
"Feng Cheng",
"Gedas Bertasius",
"Mohit Bansal"
] | 2023-09-18 19:04:37 | http://arxiv.org/abs/2309.10091v1 | http://arxiv.org/pdf/2309.10091v1 | 2309.10091v1 |
HTEC: Human Transcription Error Correction | High-quality human transcription is essential for training and improving
Automatic Speech Recognition (ASR) models. Recent study~\cite{libricrowd} has
found that every 1% worse transcription Word Error Rate (WER) increases
approximately 2% ASR WER by using the transcriptions to train ASR models.
Transcription errors are inevitable for even highly-trained annotators.
However, few studies have explored human transcription correction. Error
correction methods for other problems, such as ASR error correction and
grammatical error correction, do not perform sufficiently for this problem.
Therefore, we propose HTEC for Human Transcription Error Correction. HTEC
consists of two stages: Trans-Checker, an error detection model that predicts
and masks erroneous words, and Trans-Filler, a sequence-to-sequence generative
model that fills masked positions. We propose a holistic list of correction
operations, including four novel operations handling deletion errors. We
further propose a variant of embeddings that incorporates phoneme information
into the input of the transformer. HTEC outperforms other methods by a large
margin and surpasses human annotators by 2.2% to 4.5% in WER. Finally, we
deployed HTEC to assist human annotators and showed HTEC is particularly
effective as a co-pilot, which improves transcription quality by 15.1% without
sacrificing transcription velocity. | [
"Hanbo Sun",
"Jian Gao",
"Xiaomin Wu",
"Anjie Fang",
"Cheng Cao",
"Zheng Du"
] | 2023-09-18 19:03:21 | http://arxiv.org/abs/2309.10089v1 | http://arxiv.org/pdf/2309.10089v1 | 2309.10089v1 |
Invariant Probabilistic Prediction | In recent years, there has been a growing interest in statistical methods
that exhibit robust performance under distribution changes between training and
test data. While most of the related research focuses on point predictions with
the squared error loss, this article turns the focus towards probabilistic
predictions, which aim to comprehensively quantify the uncertainty of an
outcome variable given covariates. Within a causality-inspired framework, we
investigate the invariance and robustness of probabilistic predictions with
respect to proper scoring rules. We show that arbitrary distribution shifts do
not, in general, admit invariant and robust probabilistic predictions, in
contrast to the setting of point prediction. We illustrate how to choose
evaluation metrics and restrict the class of distribution shifts to allow for
identifiability and invariance in the prototypical Gaussian heteroscedastic
linear model. Motivated by these findings, we propose a method to yield
invariant probabilistic predictions, called IPP, and study the consistency of
the underlying parameters. Finally, we demonstrate the empirical performance of
our proposed procedure on simulated as well as on single-cell data. | [
"Alexander Henzi",
"Xinwei Shen",
"Michael Law",
"Peter Bühlmann"
] | 2023-09-18 18:50:24 | http://arxiv.org/abs/2309.10083v1 | http://arxiv.org/pdf/2309.10083v1 | 2309.10083v1 |
GAME: Generalized deep learning model towards multimodal data integration for early screening of adolescent mental disorders | The timely identification of mental disorders in adolescents is a global
public health challenge.Single factor is difficult to detect the abnormality
due to its complex and subtle nature. Additionally, the generalized multimodal
Computer-Aided Screening (CAS) systems with interactive robots for adolescent
mental disorders are not available. Here, we design an android application with
mini-games and chat recording deployed in a portable robot to screen 3,783
middle school students and construct the multimodal screening dataset,
including facial images, physiological signs, voice recordings, and textual
transcripts.We develop a model called GAME (Generalized Model with Attention
and Multimodal EmbraceNet) with novel attention mechanism that integrates
cross-modal features into the model. GAME evaluates adolescent mental
conditions with high accuracy (73.34%-92.77%) and F1-Score (71.32%-91.06%).We
find each modality contributes dynamically to the mental disorders screening
and comorbidities among various mental disorders, indicating the feasibility of
explainable model. This study provides a system capable of acquiring multimodal
information and constructs a generalized multimodal integration algorithm with
novel attention mechanisms for the early screening of adolescent mental
disorders. | [
"Zhicheng Du",
"Chenyao Jiang",
"Xi Yuan",
"Shiyao Zhai",
"Zhengyang Lei",
"Shuyue Ma",
"Yang Liu",
"Qihui Ye",
"Chufan Xiao",
"Qiming Huang",
"Ming Xu",
"Dongmei Yu",
"Peiwu Qin"
] | 2023-09-18 18:46:00 | http://arxiv.org/abs/2309.10077v1 | http://arxiv.org/pdf/2309.10077v1 | 2309.10077v1 |
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes | The Gaussian process (GP) is a popular statistical technique for stochastic
function approximation and uncertainty quantification from data. GPs have been
adopted into the realm of machine learning in the last two decades because of
their superior prediction abilities, especially in data-sparse scenarios, and
their inherent ability to provide robust uncertainty estimates. Even so, their
performance highly depends on intricate customizations of the core methodology,
which often leads to dissatisfaction among practitioners when standard setups
and off-the-shelf software tools are being deployed. Arguably the most
important building block of a GP is the kernel function which assumes the role
of a covariance operator. Stationary kernels of the Mat\'ern class are used in
the vast majority of applied studies; poor prediction performance and
unrealistic uncertainty quantification are often the consequences.
Non-stationary kernels show improved performance but are rarely used due to
their more complicated functional form and the associated effort and expertise
needed to define and tune them optimally. In this perspective, we want to help
ML practitioners make sense of some of the most common forms of
non-stationarity for Gaussian processes. We show a variety of kernels in action
using representative datasets, carefully study their properties, and compare
their performances. Based on our findings, we propose a new kernel that
combines some of the identified advantages of existing kernels. | [
"Marcus M. Noack",
"Hengrui Luo",
"Mark D. Risser"
] | 2023-09-18 18:34:51 | http://arxiv.org/abs/2309.10068v1 | http://arxiv.org/pdf/2309.10068v1 | 2309.10068v1 |
Dual Student Networks for Data-Free Model Stealing | Existing data-free model stealing methods use a generator to produce samples
in order to train a student model to match the target model outputs. To this
end, the two main challenges are estimating gradients of the target model
without access to its parameters, and generating a diverse set of training
samples that thoroughly explores the input space. We propose a Dual Student
method where two students are symmetrically trained in order to provide the
generator a criterion to generate samples that the two students disagree on. On
one hand, disagreement on a sample implies at least one student has classified
the sample incorrectly when compared to the target model. This incentive
towards disagreement implicitly encourages the generator to explore more
diverse regions of the input space. On the other hand, our method utilizes
gradients of student models to indirectly estimate gradients of the target
model. We show that this novel training objective for the generator network is
equivalent to optimizing a lower bound on the generator's loss if we had access
to the target model gradients. We show that our new optimization framework
provides more accurate gradient estimation of the target model and better
accuracies on benchmark classification datasets. Additionally, our approach
balances improved query efficiency with training computation cost. Finally, we
demonstrate that our method serves as a better proxy model for transfer-based
adversarial attacks than existing data-free model stealing methods. | [
"James Beetham",
"Navid Kardan",
"Ajmal Mian",
"Mubarak Shah"
] | 2023-09-18 18:11:31 | http://arxiv.org/abs/2309.10058v1 | http://arxiv.org/pdf/2309.10058v1 | 2309.10058v1 |
Actively Learning Reinforcement Learning: A Stochastic Optimal Control Approach | In this paper we provide a framework to cope with two problems: (i) the
fragility of reinforcement learning due to modeling uncertainties because of
the mismatch between controlled laboratory/simulation and real-world conditions
and (ii) the prohibitive computational cost of stochastic optimal control. We
approach both problems by using reinforcement learning to solve the stochastic
dynamic programming equation. The resulting reinforcement learning controller
is safe with respect to several types of constraints and it can actively learn
about the modeling uncertainties. Unlike exploration and exploitation, probing
and safety are employed automatically by the controller itself, resulting
real-time learning. A simulation example demonstrates the efficacy of the
proposed approach. | [
"Mohammad S. Ramadan",
"Mahmoud A. Hayajnh",
"Michael T. Tolley",
"Kyriakos G. Vamvoudakis"
] | 2023-09-18 18:05:35 | http://arxiv.org/abs/2309.10831v2 | http://arxiv.org/pdf/2309.10831v2 | 2309.10831v2 |
A Modular Spatial Clustering Algorithm with Noise Specification | Clustering techniques have been the key drivers of data mining, machine
learning and pattern recognition for decades. One of the most popular
clustering algorithms is DBSCAN due to its high accuracy and noise tolerance.
Many superior algorithms such as DBSCAN have input parameters that are hard to
estimate. Therefore, finding those parameters is a time consuming process. In
this paper, we propose a novel clustering algorithm Bacteria-Farm, which
balances the performance and ease of finding the optimal parameters for
clustering. Bacteria- Farm algorithm is inspired by the growth of bacteria in
closed experimental farms - their ability to consume food and grow - which
closely represents the ideal cluster growth desired in clustering algorithms.
In addition, the algorithm features a modular design to allow the creation of
versions of the algorithm for specific tasks / distributions of data. In
contrast with other clustering algorithms, our algorithm also has a provision
to specify the amount of noise to be excluded during clustering. | [
"Akhil K",
"Srikanth H R"
] | 2023-09-18 18:05:06 | http://arxiv.org/abs/2309.10047v1 | http://arxiv.org/pdf/2309.10047v1 | 2309.10047v1 |
General In-Hand Object Rotation with Vision and Touch | We introduce RotateIt, a system that enables fingertip-based object rotation
along multiple axes by leveraging multimodal sensory inputs. Our system is
trained in simulation, where it has access to ground-truth object shapes and
physical properties. Then we distill it to operate on realistic yet noisy
simulated visuotactile and proprioceptive sensory inputs. These multimodal
inputs are fused via a visuotactile transformer, enabling online inference of
object shapes and physical properties during deployment. We show significant
performance improvements over prior methods and the importance of visual and
tactile sensing. | [
"Haozhi Qi",
"Brent Yi",
"Sudharshan Suresh",
"Mike Lambeta",
"Yi Ma",
"Roberto Calandra",
"Jitendra Malik"
] | 2023-09-18 17:59:25 | http://arxiv.org/abs/2309.09979v2 | http://arxiv.org/pdf/2309.09979v2 | 2309.09979v2 |
A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning | Communication efficiency is a major challenge in federated learning (FL). In
client-server schemes, the server constitutes a bottleneck, and while
decentralized setups spread communications, they do not necessarily reduce them
due to slower convergence. We propose Multi-Token Coordinate Descent (MTCD), a
communication-efficient algorithm for semi-decentralized vertical federated
learning, exploiting both client-server and client-client communications when
each client holds a small subset of features. Our multi-token method can be
seen as a parallel Markov chain (block) coordinate descent algorithm and it
subsumes the client-server and decentralized setups as special cases. We obtain
a convergence rate of $\mathcal{O}(1/T)$ for nonconvex objectives when tokens
roam over disjoint subsets of clients and for convex objectives when they roam
over possibly overlapping subsets. Numerical results show that MTCD improves
the state-of-the-art communication efficiency and allows for a tunable amount
of parallel communications. | [
"Pedro Valdeira",
"Yuejie Chi",
"Cláudia Soares",
"João Xavier"
] | 2023-09-18 17:59:01 | http://arxiv.org/abs/2309.09977v1 | http://arxiv.org/pdf/2309.09977v1 | 2309.09977v1 |
Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification | Decision trees are widely used in machine learning due to their simplicity in
construction and interpretability. However, as data sizes grow, traditional
methods for constructing and retraining decision trees become increasingly
slow, scaling polynomially with the number of training examples. In this work,
we introduce a novel quantum algorithm, named Des-q, for constructing and
retraining decision trees in regression and binary classification tasks.
Assuming the data stream produces small increments of new training examples, we
demonstrate that our Des-q algorithm significantly reduces the time required
for tree retraining, achieving a poly-logarithmic time complexity in the number
of training examples, even accounting for the time needed to load the new
examples into quantum-accessible memory. Our approach involves building a
decision tree algorithm to perform k-piecewise linear tree splits at each
internal node. These splits simultaneously generate multiple hyperplanes,
dividing the feature space into k distinct regions. To determine the k suitable
anchor points for these splits, we develop an efficient quantum-supervised
clustering method, building upon the q-means algorithm of Kerenidis et al.
Des-q first efficiently estimates each feature weight using a novel quantum
technique to estimate the Pearson correlation. Subsequently, we employ weighted
distance estimation to cluster the training examples in k disjoint regions and
then proceed to expand the tree using the same procedure. We benchmark the
performance of the simulated version of our algorithm against the
state-of-the-art classical decision tree for regression and binary
classification on multiple data sets with numerical features. Further, we
showcase that the proposed algorithm exhibits similar performance to the
state-of-the-art decision tree while significantly speeding up the periodic
tree retraining. | [
"Niraj Kumar",
"Romina Yalovetzky",
"Changhao Li",
"Pierre Minssen",
"Marco Pistoia"
] | 2023-09-18 17:56:08 | http://arxiv.org/abs/2309.09976v3 | http://arxiv.org/pdf/2309.09976v3 | 2309.09976v3 |
Empirical Study of Mix-based Data Augmentation Methods in Physiological Time Series Data | Data augmentation is a common practice to help generalization in the
procedure of deep model training. In the context of physiological time series
classification, previous research has primarily focused on label-invariant data
augmentation methods. However, another class of augmentation techniques
(\textit{i.e., Mixup}) that emerged in the computer vision field has yet to be
fully explored in the time series domain. In this study, we systematically
review the mix-based augmentations, including mixup, cutmix, and manifold
mixup, on six physiological datasets, evaluating their performance across
different sensory data and classification tasks. Our results demonstrate that
the three mix-based augmentations can consistently improve the performance on
the six datasets. More importantly, the improvement does not rely on expert
knowledge or extensive parameter tuning. Lastly, we provide an overview of the
unique properties of the mix-based augmentation methods and highlight the
potential benefits of using the mix-based augmentation in physiological time
series data. | [
"Peikun Guo",
"Huiyuan Yang",
"Akane Sano"
] | 2023-09-18 17:51:47 | http://arxiv.org/abs/2309.09970v1 | http://arxiv.org/pdf/2309.09970v1 | 2309.09970v1 |
Parameter-Efficient Long-Tailed Recognition | The "pre-training and fine-tuning" paradigm in addressing long-tailed
recognition tasks has sparked significant interest since the emergence of large
vision-language models like the contrastive language-image pre-training (CLIP).
While previous studies have shown promise in adapting pre-trained models for
these tasks, they often undesirably require extensive training epochs or
additional training data to maintain good performance. In this paper, we
propose PEL, a fine-tuning method that can effectively adapt pre-trained models
to long-tailed recognition tasks in fewer than 20 epochs without the need for
extra data. We first empirically find that commonly used fine-tuning methods,
such as full fine-tuning and classifier fine-tuning, suffer from overfitting,
resulting in performance deterioration on tail classes. To mitigate this issue,
PEL introduces a small number of task-specific parameters by adopting the
design of any existing parameter-efficient fine-tuning method. Additionally, to
expedite convergence, PEL presents a novel semantic-aware classifier
initialization technique derived from the CLIP textual encoder without adding
any computational overhead. Our experimental results on four long-tailed
datasets demonstrate that PEL consistently outperforms previous
state-of-the-art approaches. The source code is available at
https://github.com/shijxcs/PEL. | [
"Jiang-Xin Shi",
"Tong Wei",
"Zhi Zhou",
"Xin-Yan Han",
"Jie-Jing Shao",
"Yu-Feng Li"
] | 2023-09-18 17:50:56 | http://arxiv.org/abs/2309.10019v1 | http://arxiv.org/pdf/2309.10019v1 | 2309.10019v1 |
Prompt a Robot to Walk with Large Language Models | Large language models (LLMs) pre-trained on vast internet-scale data have
showcased remarkable capabilities across diverse domains. Recently, there has
been escalating interest in deploying LLMs for robotics, aiming to harness the
power of foundation models in real-world settings. However, this approach faces
significant challenges, particularly in grounding these models in the physical
world and in generating dynamic robot motions. To address these issues, we
introduce a novel paradigm in which we use few-shot prompts collected from the
physical environment, enabling the LLM to autoregressively generate low-level
control commands for robots without task-specific fine-tuning. Experiments
across various robots and environments validate that our method can effectively
prompt a robot to walk. We thus illustrate how LLMs can proficiently function
as low-level feedback controllers for dynamic motion control even in
high-dimensional robotic systems. The project website and source code can be
found at: https://prompt2walk.github.io/ . | [
"Yen-Jen Wang",
"Bike Zhang",
"Jianyu Chen",
"Koushil Sreenath"
] | 2023-09-18 17:50:17 | http://arxiv.org/abs/2309.09969v1 | http://arxiv.org/pdf/2309.09969v1 | 2309.09969v1 |
Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees | Tabular data is hard to acquire and is subject to missing values. This paper
proposes a novel approach to generate and impute mixed-type (continuous and
categorical) tabular data using score-based diffusion and conditional flow
matching. Contrary to previous work that relies on neural networks as function
approximators, we instead utilize XGBoost, a popular Gradient-Boosted Tree
(GBT) method. In addition to being elegant, we empirically show on various
datasets that our method i) generates highly realistic synthetic data when the
training dataset is either clean or tainted by missing data and ii) generates
diverse plausible data imputations. Our method often outperforms deep-learning
generation methods and can trained in parallel using CPUs without the need for
a GPU. To make it easily accessible, we release our code through a Python
library on PyPI and an R package on CRAN. | [
"Alexia Jolicoeur-Martineau",
"Kilian Fatras",
"Tal Kachman"
] | 2023-09-18 17:49:09 | http://arxiv.org/abs/2309.09968v1 | http://arxiv.org/pdf/2309.09968v1 | 2309.09968v1 |
What is a Fair Diffusion Model? Designing Generative Text-To-Image Models to Incorporate Various Worldviews | Generative text-to-image (GTI) models produce high-quality images from short
textual descriptions and are widely used in academic and creative domains.
However, GTI models frequently amplify biases from their training data, often
producing prejudiced or stereotypical images. Yet, current bias mitigation
strategies are limited and primarily focus on enforcing gender parity across
occupations. To enhance GTI bias mitigation, we introduce DiffusionWorldViewer,
a tool to analyze and manipulate GTI models' attitudes, values, stories, and
expectations of the world that impact its generated images. Through an
interactive interface deployed as a web-based GUI and Jupyter Notebook plugin,
DiffusionWorldViewer categorizes existing demographics of GTI-generated images
and provides interactive methods to align image demographics with user
worldviews. In a study with 13 GTI users, we find that DiffusionWorldViewer
allows users to represent their varied viewpoints about what GTI outputs are
fair and, in doing so, challenges current notions of fairness that assume a
universal worldview. | [
"Zoe De Simone",
"Angie Boggust",
"Arvind Satyanarayan",
"Ashia Wilson"
] | 2023-09-18 17:04:04 | http://arxiv.org/abs/2309.09944v1 | http://arxiv.org/pdf/2309.09944v1 | 2309.09944v1 |
Hierarchical Attention and Graph Neural Networks: Toward Drift-Free Pose Estimation | The most commonly used method for addressing 3D geometric registration is the
iterative closet-point algorithm, this approach is incremental and prone to
drift over multiple consecutive frames. The Common strategy to address the
drift is the pose graph optimization subsequent to frame-to-frame registration,
incorporating a loop closure process that identifies previously visited places.
In this paper, we explore a framework that replaces traditional geometric
registration and pose graph optimization with a learned model utilizing
hierarchical attention mechanisms and graph neural networks. We propose a
strategy to condense the data flow, preserving essential information required
for the precise estimation of rigid poses. Our results, derived from tests on
the KITTI Odometry dataset, demonstrate a significant improvement in pose
estimation accuracy. This improvement is especially notable in determining
rotational components when compared with results obtained through conventional
multi-way registration via pose graph optimization. The code will be made
available upon completion of the review process. | [
"Kathia Melbouci",
"Fawzi Nashashibi"
] | 2023-09-18 16:51:56 | http://arxiv.org/abs/2309.09934v1 | http://arxiv.org/pdf/2309.09934v1 | 2309.09934v1 |
Evaluating Adversarial Robustness with Expected Viable Performance | We introduce a metric for evaluating the robustness of a classifier, with
particular attention to adversarial perturbations, in terms of expected
functionality with respect to possible adversarial perturbations. A classifier
is assumed to be non-functional (that is, has a functionality of zero) with
respect to a perturbation bound if a conventional measure of performance, such
as classification accuracy, is less than a minimally viable threshold when the
classifier is tested on examples from that perturbation bound. Defining
robustness in terms of an expected value is motivated by a domain general
approach to robustness quantification. | [
"Ryan McCoppin",
"Colin Dawson",
"Sean M. Kennedy",
"Leslie M. Blaha"
] | 2023-09-18 16:47:24 | http://arxiv.org/abs/2309.09928v1 | http://arxiv.org/pdf/2309.09928v1 | 2309.09928v1 |
Graph topological property recovery with heat and wave dynamics-based features on graphs | In this paper, we propose Graph Differential Equation Network (GDeNet), an
approach that harnesses the expressive power of solutions to PDEs on a graph to
obtain continuous node- and graph-level representations for various downstream
tasks. We derive theoretical results connecting the dynamics of heat and wave
equations to the spectral properties of the graph and to the behavior of
continuous-time random walks on graphs. We demonstrate experimentally that
these dynamics are able to capture salient aspects of graph geometry and
topology by recovering generating parameters of random graphs, Ricci curvature,
and persistent homology. Furthermore, we demonstrate the superior performance
of GDeNet on real-world datasets including citation graphs, drug-like
molecules, and proteins. | [
"Dhananjay Bhaskar",
"Yanlei Zhang",
"Charles Xu",
"Xingzhi Sun",
"Oluwadamilola Fasina",
"Guy Wolf",
"Maximilian Nickel",
"Michael Perlmutter",
"Smita Krishnaswamy"
] | 2023-09-18 16:39:51 | http://arxiv.org/abs/2309.09924v2 | http://arxiv.org/pdf/2309.09924v2 | 2309.09924v2 |
A Heterogeneous Graph-Based Multi-Task Learning for Fault Event Diagnosis in Smart Grid | Precise and timely fault diagnosis is a prerequisite for a distribution
system to ensure minimum downtime and maintain reliable operation. This
necessitates access to a comprehensive procedure that can provide the grid
operators with insightful information in the case of a fault event. In this
paper, we propose a heterogeneous multi-task learning graph neural network
(MTL-GNN) capable of detecting, locating and classifying faults in addition to
providing an estimate of the fault resistance and current. Using a graph neural
network (GNN) allows for learning the topological representation of the
distribution system as well as feature learning through a message-passing
scheme. We investigate the robustness of our proposed model using the IEEE-123
test feeder system. This work also proposes a novel GNN-based explainability
method to identify key nodes in the distribution system which then facilitates
informed sparse measurements. Numerical tests validate the performance of the
model across all tasks. | [
"Dibaloke Chanda",
"Nasim Yahya Soltani"
] | 2023-09-18 16:35:30 | http://arxiv.org/abs/2309.09921v1 | http://arxiv.org/pdf/2309.09921v1 | 2309.09921v1 |
Distilling HuBERT with LSTMs via Decoupled Knowledge Distillation | Much research effort is being applied to the task of compressing the
knowledge of self-supervised models, which are powerful, yet large and memory
consuming. In this work, we show that the original method of knowledge
distillation (and its more recently proposed extension, decoupled knowledge
distillation) can be applied to the task of distilling HuBERT. In contrast to
methods that focus on distilling internal features, this allows for more
freedom in the network architecture of the compressed model. We thus propose to
distill HuBERT's Transformer layers into an LSTM-based distilled model that
reduces the number of parameters even below DistilHuBERT and at the same time
shows improved performance in automatic speech recognition. | [
"Danilo de Oliveira",
"Timo Gerkmann"
] | 2023-09-18 16:34:40 | http://arxiv.org/abs/2309.09920v1 | http://arxiv.org/pdf/2309.09920v1 | 2309.09920v1 |
Evaluation of Human-Understandability of Global Model Explanations using Decision Tree | In explainable artificial intelligence (XAI) research, the predominant focus
has been on interpreting models for experts and practitioners. Model agnostic
and local explanation approaches are deemed interpretable and sufficient in
many applications. However, in domains like healthcare, where end users are
patients without AI or domain expertise, there is an urgent need for model
explanations that are more comprehensible and instil trust in the model's
operations. We hypothesise that generating model explanations that are
narrative, patient-specific and global(holistic of the model) would enable
better understandability and enable decision-making. We test this using a
decision tree model to generate both local and global explanations for patients
identified as having a high risk of coronary heart disease. These explanations
are presented to non-expert users. We find a strong individual preference for a
specific type of explanation. The majority of participants prefer global
explanations, while a smaller group prefers local explanations. A task based
evaluation of mental models of these participants provide valuable feedback to
enhance narrative global explanations. This, in turn, guides the design of
health informatics systems that are both trustworthy and actionable. | [
"Adarsa Sivaprasad",
"Ehud Reiter",
"Nava Tintarev",
"Nir Oren"
] | 2023-09-18 16:30:14 | http://arxiv.org/abs/2309.09917v1 | http://arxiv.org/pdf/2309.09917v1 | 2309.09917v1 |
Learning Nonparametric High-Dimensional Generative Models: The Empirical-Beta-Copula Autoencoder | By sampling from the latent space of an autoencoder and decoding the latent
space samples to the original data space, any autoencoder can simply be turned
into a generative model. For this to work, it is necessary to model the
autoencoder's latent space with a distribution from which samples can be
obtained. Several simple possibilities (kernel density estimates, Gaussian
distribution) and more sophisticated ones (Gaussian mixture models, copula
models, normalization flows) can be thought of and have been tried recently.
This study aims to discuss, assess, and compare various techniques that can be
used to capture the latent space so that an autoencoder can become a generative
model while striving for simplicity. Among them, a new copula-based method, the
Empirical Beta Copula Autoencoder, is considered. Furthermore, we provide
insights into further aspects of these methods, such as targeted sampling or
synthesizing new data with specific features. | [
"Maximilian Coblenz",
"Oliver Grothe",
"Fabian Kächele"
] | 2023-09-18 16:29:36 | http://arxiv.org/abs/2309.09916v1 | http://arxiv.org/pdf/2309.09916v1 | 2309.09916v1 |
Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for Preference Aligned Path Planning | Autonomous mobility tasks such as lastmile delivery require reasoning about
operator indicated preferences over terrains on which the robot should navigate
to ensure both robot safety and mission success. However, coping with out of
distribution data from novel terrains or appearance changes due to lighting
variations remains a fundamental problem in visual terrain adaptive navigation.
Existing solutions either require labor intensive manual data recollection and
labeling or use handcoded reward functions that may not align with operator
preferences. In this work, we posit that operator preferences for visually
novel terrains, which the robot should adhere to, can often be extrapolated
from established terrain references within the inertial, proprioceptive, and
tactile domain. Leveraging this insight, we introduce Preference extrApolation
for Terrain awarE Robot Navigation, PATERN, a novel framework for extrapolating
operator terrain preferences for visual navigation. PATERN learns to map
inertial, proprioceptive, tactile measurements from the robots observations to
a representation space and performs nearest neighbor search in this space to
estimate operator preferences over novel terrains. Through physical robot
experiments in outdoor environments, we assess PATERNs capability to
extrapolate preferences and generalize to novel terrains and challenging
lighting conditions. Compared to baseline approaches, our findings indicate
that PATERN robustly generalizes to diverse terrains and varied lighting
conditions, while navigating in a preference aligned manner. | [
"Haresh Karnan",
"Elvin Yang",
"Garrett Warnell",
"Joydeep Biswas",
"Peter Stone"
] | 2023-09-18 16:24:26 | http://arxiv.org/abs/2309.09912v1 | http://arxiv.org/pdf/2309.09912v1 | 2309.09912v1 |
Evaluation of GPT-3 for Anti-Cancer Drug Sensitivity Prediction | In this study, we investigated the potential of GPT-3 for the anti-cancer
drug sensitivity prediction task using structured pharmacogenomics data across
five tissue types and evaluated its performance with zero-shot prompting and
fine-tuning paradigms. The drug's smile representation and cell line's genomic
mutation features were predictive of the drug response. The results from this
study have the potential to pave the way for designing more efficient treatment
protocols in precision oncology. | [
"Shaika Chowdhury",
"Sivaraman Rajaganapathy",
"Lichao Sun",
"James Cerhan",
"Nansu Zong"
] | 2023-09-18 16:17:44 | http://arxiv.org/abs/2309.10016v1 | http://arxiv.org/pdf/2309.10016v1 | 2309.10016v1 |
Learning to Generate Lumped Hydrological Models | In a lumped hydrological model structure, the hydrological function of a
catchment is characterized by only a few parameters. Given a set of parameter
values, a numerical function useful for hydrological prediction is generated.
Thus, this study assumes that the hydrological function of a catchment can be
sufficiently well characterized by a small number of latent variables. By
specifying the variable values, a numerical function resembling the
hydrological function of a real-world catchment can be generated using a
generative model. In this study, a deep learning method is used to learn both
the generative model and the latent variable values of different catchments
directly from their climate forcing and runoff data, without using catchment
attributes. The generative models can be used similarly to a lumped model
structure, i.e., by estimating the optimal parameter or latent variable values
using a generic model calibration algorithm, an optimal numerical model can be
derived. In this study, generative models using eight latent variables were
learned from data from over 3,000 catchments worldwide, and the learned
generative models were applied to model over 700 different catchments using a
generic calibration algorithm. The quality of the resulting optimal models was
generally comparable to or better than that obtained using 36 different types
of lump model structures or using non-generative deep learning methods. In
summary, this study presents a data-driven approach for representing the
hydrological function of a catchment in low-dimensional space and a method for
reconstructing specific hydrological functions from the representations. | [
"Yang Yang",
"Ting Fong May Chui"
] | 2023-09-18 16:07:41 | http://arxiv.org/abs/2309.09904v1 | http://arxiv.org/pdf/2309.09904v1 | 2309.09904v1 |
Context is Environment | Two lines of work are taking the central stage in AI research. On the one
hand, the community is making increasing efforts to build models that discard
spurious correlations and generalize better in novel test environments.
Unfortunately, the bitter lesson so far is that no proposal convincingly
outperforms a simple empirical risk minimization baseline. On the other hand,
large language models (LLMs) have erupted as algorithms able to learn
in-context, generalizing on-the-fly to eclectic contextual circumstances that
users enforce by means of prompting. In this paper, we argue that context is
environment, and posit that in-context learning holds the key to better domain
generalization. Via extensive theory and experiments, we show that paying
attention to context$\unicode{x2013}\unicode{x2013}$unlabeled examples as they
arrive$\unicode{x2013}\unicode{x2013}$allows our proposed In-Context Risk
Minimization (ICRM) algorithm to zoom-in on the test environment risk
minimizer, leading to significant out-of-distribution performance improvements.
From all of this, two messages are worth taking home. Researchers in domain
generalization should consider environment as context, and harness the adaptive
power of in-context learning. Researchers in LLMs should consider context as
environment, to better structure data towards generalization. | [
"Sharut Gupta",
"Stefanie Jegelka",
"David Lopez-Paz",
"Kartik Ahuja"
] | 2023-09-18 15:51:27 | http://arxiv.org/abs/2309.09888v2 | http://arxiv.org/pdf/2309.09888v2 | 2309.09888v2 |
On Model Explanations with Transferable Neural Pathways | Neural pathways as model explanations consist of a sparse set of neurons that
provide the same level of prediction performance as the whole model. Existing
methods primarily focus on accuracy and sparsity but the generated pathways may
offer limited interpretability thus fall short in explaining the model
behavior. In this paper, we suggest two interpretability criteria of neural
pathways: (i) same-class neural pathways should primarily consist of
class-relevant neurons; (ii) each instance's neural pathway sparsity should be
optimally determined. To this end, we propose a Generative Class-relevant
Neural Pathway (GEN-CNP) model that learns to predict the neural pathways from
the target model's feature maps. We propose to learn class-relevant information
from features of deep and shallow layers such that same-class neural pathways
exhibit high similarity. We further impose a faithfulness criterion for GEN-CNP
to generate pathways with instance-specific sparsity. We propose to transfer
the class-relevant neural pathways to explain samples of the same class and
show experimentally and qualitatively their faithfulness and interpretability. | [
"Xinmiao Lin",
"Wentao Bao",
"Qi Yu",
"Yu Kong"
] | 2023-09-18 15:50:38 | http://arxiv.org/abs/2309.09887v1 | http://arxiv.org/pdf/2309.09887v1 | 2309.09887v1 |
Deep Reinforcement Learning for the Joint Control of Traffic Light Signaling and Vehicle Speed Advice | Traffic congestion in dense urban centers presents an economical and
environmental burden. In recent years, the availability of vehicle-to-anything
communication allows for the transmission of detailed vehicle states to the
infrastructure that can be used for intelligent traffic light control. The
other way around, the infrastructure can provide vehicles with advice on
driving behavior, such as appropriate velocities, which can improve the
efficacy of the traffic system. Several research works applied deep
reinforcement learning to either traffic light control or vehicle speed advice.
In this work, we propose a first attempt to jointly learn the control of both.
We show this to improve the efficacy of traffic systems. In our experiments,
the joint control approach reduces average vehicle trip delays, w.r.t.
controlling only traffic lights, in eight out of eleven benchmark scenarios.
Analyzing the qualitative behavior of the vehicle speed advice policy, we
observe that this is achieved by smoothing out the velocity profile of vehicles
nearby a traffic light. Learning joint control of traffic signaling and speed
advice in the real world could help to reduce congestion and mitigate the
economical and environmental repercussions of today's traffic systems. | [
"Johannes V. S. Busch",
"Robert Voelckner",
"Peter Sossalla",
"Christian L. Vielhaus",
"Roberto Calandra",
"Frank H. P. Fitzek"
] | 2023-09-18 15:45:22 | http://arxiv.org/abs/2309.09881v1 | http://arxiv.org/pdf/2309.09881v1 | 2309.09881v1 |
Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models | While Automatic Speech Recognition (ASR) systems are widely used in many
real-world applications, they often do not generalize well to new domains and
need to be finetuned on data from these domains. However, target-domain data
usually are not readily available in many scenarios. In this paper, we propose
a new strategy for adapting ASR models to new target domains without any text
or speech from those domains. To accomplish this, we propose a novel data
synthesis pipeline that uses a Large Language Model (LLM) to generate a target
domain text corpus, and a state-of-the-art controllable speech synthesis model
to generate the corresponding speech. We propose a simple yet effective
in-context instruction finetuning strategy to increase the effectiveness of LLM
in generating text corpora for new domains. Experiments on the SLURP dataset
show that the proposed method achieves an average relative word error rate
improvement of $28\%$ on unseen target domains without any performance drop in
source domains. | [
"Hsuan Su",
"Ting-Yao Hu",
"Hema Swetha Koppula",
"Raviteja Vemulapalli",
"Jen-Hao Rick Chang",
"Karren Yang",
"Gautam Varma Mantena",
"Oncel Tuzel"
] | 2023-09-18 15:43:08 | http://arxiv.org/abs/2309.10707v1 | http://arxiv.org/pdf/2309.10707v1 | 2309.10707v1 |
Error Reduction from Stacked Regressions | Stacking regressions is an ensemble technique that forms linear combinations
of different regression estimators to enhance predictive accuracy. The
conventional approach uses cross-validation data to generate predictions from
the constituent estimators, and least-squares with nonnegativity constraints to
learn the combination weights. In this paper, we learn these weights
analogously by minimizing an estimate of the population risk subject to a
nonnegativity constraint. When the constituent estimators are linear
least-squares projections onto nested subspaces separated by at least three
dimensions, we show that thanks to a shrinkage effect, the resulting stacked
estimator has strictly smaller population risk than best single estimator among
them. Here "best" refers to an estimator that minimizes a model selection
criterion such as AIC or BIC. In other words, in this setting, the best single
estimator is inadmissible. Because the optimization problem can be reformulated
as isotonic regression, the stacked estimator requires the same order of
computation as the best single estimator, making it an attractive alternative
in terms of both performance and implementation. | [
"Xin Chen",
"Jason M. Klusowski",
"Yan Shuo Tan"
] | 2023-09-18 15:42:12 | http://arxiv.org/abs/2309.09880v2 | http://arxiv.org/pdf/2309.09880v2 | 2309.09880v2 |
Domain Generalization with Fourier Transform and Soft Thresholding | Domain generalization aims to train models on multiple source domains so that
they can generalize well to unseen target domains. Among many domain
generalization methods, Fourier-transform-based domain generalization methods
have gained popularity primarily because they exploit the power of Fourier
transformation to capture essential patterns and regularities in the data,
making the model more robust to domain shifts. The mainstream
Fourier-transform-based domain generalization swaps the Fourier amplitude
spectrum while preserving the phase spectrum between the source and the target
images. However, it neglects background interference in the amplitude spectrum.
To overcome this limitation, we introduce a soft-thresholding function in the
Fourier domain. We apply this newly designed algorithm to retinal fundus image
segmentation, which is important for diagnosing ocular diseases but the neural
network's performance can degrade across different sources due to domain
shifts. The proposed technique basically enhances fundus image augmentation by
eliminating small values in the Fourier domain and providing better
generalization. The innovative nature of the soft thresholding fused with
Fourier-transform-based domain generalization improves neural network models'
performance by reducing the target images' background interference
significantly. Experiments on public data validate our approach's effectiveness
over conventional and state-of-the-art methods with superior segmentation
metrics. | [
"Hongyi Pan",
"Bin Wang",
"Zheyuan Zhang",
"Xin Zhu",
"Debesh Jha",
"Ahmet Enis Cetin",
"Concetto Spampinato",
"Ulas Bagci"
] | 2023-09-18 15:28:09 | http://arxiv.org/abs/2309.09866v2 | http://arxiv.org/pdf/2309.09866v2 | 2309.09866v2 |
SYNDICOM: Improving Conversational Commonsense with Error-Injection and Natural Language Feedback | Commonsense reasoning is a critical aspect of human communication. Despite
recent advances in conversational AI driven by large language models,
commonsense reasoning remains a challenging task. In this work, we introduce
SYNDICOM - a method for improving commonsense in dialogue response generation.
SYNDICOM consists of two components. The first component is a dataset composed
of commonsense dialogues created from a knowledge graph and synthesized into
natural language. This dataset includes both valid and invalid responses to
dialogue contexts, along with natural language feedback (NLF) for the invalid
responses. The second contribution is a two-step procedure: training a model to
predict natural language feedback (NLF) for invalid responses, and then
training a response generation model conditioned on the predicted NLF, the
invalid response, and the dialogue. SYNDICOM is scalable and does not require
reinforcement learning. Empirical results on three tasks are evaluated using a
broad range of metrics. SYNDICOM achieves a relative improvement of 53% over
ChatGPT on ROUGE1, and human evaluators prefer SYNDICOM over ChatGPT 57% of the
time. We will publicly release the code and the full dataset. | [
"Christopher Richardson",
"Anirudh Sundar",
"Larry Heck"
] | 2023-09-18 15:08:48 | http://arxiv.org/abs/2309.10015v1 | http://arxiv.org/pdf/2309.10015v1 | 2309.10015v1 |
Prognosis of Multivariate Battery State of Performance and Health via Transformers | Batteries are an essential component in a deeply decarbonized future.
Understanding battery performance and "useful life" as a function of design and
use is of paramount importance to accelerating adoption. Historically, battery
state of health (SOH) was summarized by a single parameter, the fraction of a
battery's capacity relative to its initial state. A more useful approach,
however, is a comprehensive characterization of its state and complexities,
using an interrelated set of descriptors including capacity, energy, ionic and
electronic impedances, open circuit voltages, and microstructure metrics.
Indeed, predicting across an extensive suite of properties as a function of
battery use is a "holy grail" of battery science; it can provide unprecedented
insights toward the design of better batteries with reduced experimental
effort, and de-risking energy storage investments that are necessary to meet
CO2 reduction targets. In this work, we present a first step in that direction
via deep transformer networks for the prediction of 28 battery state of health
descriptors using two cycling datasets representing six lithium-ion cathode
chemistries (LFP, NMC111, NMC532, NMC622, HE5050, and 5Vspinel), multiple
electrolyte/anode compositions, and different charge-discharge scenarios. The
accuracy of these predictions versus battery life (with an unprecedented mean
absolute error of 19 cycles in predicting end of life for an LFP fast-charging
dataset) illustrates the promise of deep learning towards providing deeper
understanding and control of battery health. | [
"Noah H. Paulson",
"Joseph J. Kubal",
"Susan J. Babinec"
] | 2023-09-18 15:04:40 | http://arxiv.org/abs/2309.10014v1 | http://arxiv.org/pdf/2309.10014v1 | 2309.10014v1 |
CC-SGG: Corner Case Scenario Generation using Learned Scene Graphs | Corner case scenarios are an essential tool for testing and validating the
safety of autonomous vehicles (AVs). As these scenarios are often
insufficiently present in naturalistic driving datasets, augmenting the data
with synthetic corner cases greatly enhances the safe operation of AVs in
unique situations. However, the generation of synthetic, yet realistic, corner
cases poses a significant challenge. In this work, we introduce a novel
approach based on Heterogeneous Graph Neural Networks (HGNNs) to transform
regular driving scenarios into corner cases. To achieve this, we first generate
concise representations of regular driving scenes as scene graphs, minimally
manipulating their structure and properties. Our model then learns to perturb
those graphs to generate corner cases using attention and triple embeddings.
The input and perturbed graphs are then imported back into the simulation to
generate corner case scenarios. Our model successfully learned to produce
corner cases from input scene graphs, achieving 89.9% prediction accuracy on
our testing dataset. We further validate the generated scenarios on baseline
autonomous driving methods, demonstrating our model's ability to effectively
create critical situations for the baselines. | [
"George Drayson",
"Efimia Panagiotaki",
"Daniel Omeiza",
"Lars Kunze"
] | 2023-09-18 14:59:11 | http://arxiv.org/abs/2309.09844v1 | http://arxiv.org/pdf/2309.09844v1 | 2309.09844v1 |
Instruction-Following Speech Recognition | Conventional end-to-end Automatic Speech Recognition (ASR) models primarily
focus on exact transcription tasks, lacking flexibility for nuanced user
interactions. With the advent of Large Language Models (LLMs) in speech
processing, more organic, text-prompt-based interactions have become possible.
However, the mechanisms behind these models' speech understanding and
"reasoning" capabilities remain underexplored. To study this question from the
data perspective, we introduce instruction-following speech recognition,
training a Listen-Attend-Spell model to understand and execute a diverse set of
free-form text instructions. This enables a multitude of speech recognition
tasks -- ranging from transcript manipulation to summarization -- without
relying on predefined command sets. Remarkably, our model, trained from scratch
on Librispeech, interprets and executes simple instructions without requiring
LLMs or pre-trained speech modules. It also offers selective transcription
options based on instructions like "transcribe first half and then turn off
listening," providing an additional layer of privacy and safety compared to
existing LLMs. Our findings highlight the significant potential of
instruction-following training to advance speech foundation models. | [
"Cheng-I Jeff Lai",
"Zhiyun Lu",
"Liangliang Cao",
"Ruoming Pang"
] | 2023-09-18 14:59:10 | http://arxiv.org/abs/2309.09843v1 | http://arxiv.org/pdf/2309.09843v1 | 2309.09843v1 |
UNICON: A unified framework for behavior-based consumer segmentation in e-commerce | Data-driven personalization is a key practice in fashion e-commerce,
improving the way businesses serve their consumers needs with more relevant
content. While hyper-personalization offers highly targeted experiences to each
consumer, it requires a significant amount of private data to create an
individualized journey. To alleviate this, group-based personalization provides
a moderate level of personalization built on broader common preferences of a
consumer segment, while still being able to personalize the results. We
introduce UNICON, a unified deep learning consumer segmentation framework that
leverages rich consumer behavior data to learn long-term latent representations
and utilizes them to extract two pivotal types of segmentation catering various
personalization use-cases: lookalike, expanding a predefined target seed
segment with consumers of similar behavior, and data-driven, revealing
non-obvious consumer segments with similar affinities. We demonstrate through
extensive experimentation our framework effectiveness in fashion to identify
lookalike Designer audience and data-driven style segments. Furthermore, we
present experiments that showcase how segment information can be incorporated
in a hybrid recommender system combining hyper and group-based personalization
to exploit the advantages of both alternatives and provide improvements on
consumer experience. | [
"Manuel Dibak",
"Vladimir Vlasov",
"Nour Karessli",
"Darya Dedik",
"Egor Malykh",
"Jacek Wasilewski",
"Ton Torres",
"Ana Peleteiro Ramallo"
] | 2023-09-18 14:58:13 | http://arxiv.org/abs/2309.13068v1 | http://arxiv.org/pdf/2309.13068v1 | 2309.13068v1 |
Hyperbolic vs Euclidean Embeddings in Few-Shot Learning: Two Sides of the Same Coin | Recent research in representation learning has shown that hierarchical data
lends itself to low-dimensional and highly informative representations in
hyperbolic space. However, even if hyperbolic embeddings have gathered
attention in image recognition, their optimization is prone to numerical
hurdles. Further, it remains unclear which applications stand to benefit the
most from the implicit bias imposed by hyperbolicity, when compared to
traditional Euclidean features. In this paper, we focus on prototypical
hyperbolic neural networks. In particular, the tendency of hyperbolic
embeddings to converge to the boundary of the Poincar\'e ball in high
dimensions and the effect this has on few-shot classification. We show that the
best few-shot results are attained for hyperbolic embeddings at a common
hyperbolic radius. In contrast to prior benchmark results, we demonstrate that
better performance can be achieved by a fixed-radius encoder equipped with the
Euclidean metric, regardless of the embedding dimension. | [
"Gabriel Moreira",
"Manuel Marques",
"João Paulo Costeira",
"Alexander Hauptmann"
] | 2023-09-18 14:51:46 | http://arxiv.org/abs/2309.10013v1 | http://arxiv.org/pdf/2309.10013v1 | 2309.10013v1 |
Clustering of Urban Traffic Patterns by K-Means and Dynamic Time Warping: Case Study | Clustering of urban traffic patterns is an essential task in many different
areas of traffic management and planning. In this paper, two significant
applications in the clustering of urban traffic patterns are described. The
first application estimates the missing speed values using the speed of road
segments with similar traffic patterns to colorify map tiles. The second one is
the estimation of essential road segments for generating addresses for a local
point on the map, using the similarity patterns of different road segments. The
speed time series extracts the traffic pattern in different road segments. In
this paper, we proposed the time series clustering algorithm based on K-Means
and Dynamic Time Warping. The case study of our proposed algorithm is based on
the Snapp application's driver speed time series data. The results of the two
applications illustrate that the proposed method can extract similar urban
traffic patterns. | [
"Sadegh Etemad",
"Raziyeh Mosayebi",
"Tadeh Alexani Khodavirdian",
"Elahe Dastan",
"Amir Salari Telmadarreh",
"Mohammadreza Jafari",
"Sepehr Rafiei"
] | 2023-09-18 14:50:46 | http://arxiv.org/abs/2309.09830v1 | http://arxiv.org/pdf/2309.09830v1 | 2309.09830v1 |
Convolutional Deep Kernel Machines | Deep kernel machines (DKMs) are a recently introduced kernel method with the
flexibility of other deep models including deep NNs and deep Gaussian
processes. DKMs work purely with kernels, never with features, and are
therefore different from other methods ranging from NNs to deep kernel learning
and even deep Gaussian processes, which all use features as a fundamental
component. Here, we introduce convolutional DKMs, along with an efficient
inter-domain inducing point approximation scheme. Further, we develop and
experimentally assess a number of model variants, including 9 different types
of normalisation designed for the convolutional DKMs, two likelihoods, and two
different types of top-layer. The resulting models achieve around 99% test
accuracy on MNIST, 92% on CIFAR-10 and 71% on CIFAR-100, despite training in
only around 28 GPU hours, 1-2 orders of magnitude faster than full NNGP / NTK /
Myrtle kernels, whilst achieving comparable performance. | [
"Edward Milsom",
"Ben Anson",
"Laurence Aitchison"
] | 2023-09-18 14:36:17 | http://arxiv.org/abs/2309.09814v1 | http://arxiv.org/pdf/2309.09814v1 | 2309.09814v1 |
Learning Optimal Contracts: How to Exploit Small Action Spaces | We study principal-agent problems in which a principal commits to an
outcome-dependent payment scheme -- called contract -- in order to induce an
agent to take a costly, unobservable action leading to favorable outcomes. We
consider a generalization of the classical (single-round) version of the
problem in which the principal interacts with the agent by committing to
contracts over multiple rounds. The principal has no information about the
agent, and they have to learn an optimal contract by only observing the outcome
realized at each round. We focus on settings in which the size of the agent's
action space is small. We design an algorithm that learns an
approximately-optimal contract with high probability in a number of rounds
polynomial in the size of the outcome space, when the number of actions is
constant. Our algorithm solves an open problem by Zhu et al.[2022]. Moreover,
it can also be employed to provide a $\tilde{\mathcal{O}}(T^{4/5})$ regret
bound in the related online learning setting in which the principal aims at
maximizing their cumulative utility, thus considerably improving
previously-known regret bounds. | [
"Francesco Bacchiocchi",
"Matteo Castiglioni",
"Alberto Marchesi",
"Nicola Gatti"
] | 2023-09-18 14:18:35 | http://arxiv.org/abs/2309.09801v1 | http://arxiv.org/pdf/2309.09801v1 | 2309.09801v1 |
Harnessing Collective Intelligence Under a Lack of Cultural Consensus | Harnessing collective intelligence to drive effective decision-making and
collaboration benefits from the ability to detect and characterize
heterogeneity in consensus beliefs. This is particularly true in domains such
as technology acceptance or leadership perception, where a consensus defines an
intersubjective truth, leading to the possibility of multiple "ground truths"
when subsets of respondents sustain mutually incompatible consensuses. Cultural
Consensus Theory (CCT) provides a statistical framework for detecting and
characterizing these divergent consensus beliefs. However, it is unworkable in
modern applications because it lacks the ability to generalize across even
highly similar beliefs, is ineffective with sparse data, and can leverage
neither external knowledge bases nor learned machine representations. Here, we
overcome these limitations through Infinite Deep Latent Construct Cultural
Consensus Theory (iDLC-CCT), a nonparametric Bayesian model that extends CCT
with a latent construct that maps between pretrained deep neural network
embeddings of entities and the consensus beliefs regarding those entities among
one or more subsets of respondents. We validate the method across domains
including perceptions of risk sources, food healthiness, leadership, first
impressions, and humor. We find that iDLC-CCT better predicts the degree of
consensus, generalizes well to out-of-sample entities, and is effective even
with sparse data. To improve scalability, we introduce an efficient
hard-clustering variant of the iDLC-CCT using an algorithm derived from a
small-variance asymptotic analysis of the model. The iDLC-CCT, therefore,
provides a workable computational foundation for harnessing collective
intelligence under a lack of cultural consensus and may potentially form the
basis of consensus-aware information technologies. | [
"Necdet Gürkan",
"Jordan W. Suchow"
] | 2023-09-18 14:05:04 | http://arxiv.org/abs/2309.09787v2 | http://arxiv.org/pdf/2309.09787v2 | 2309.09787v2 |
Towards Self-Adaptive Pseudo-Label Filtering for Semi-Supervised Learning | Recent semi-supervised learning (SSL) methods typically include a filtering
strategy to improve the quality of pseudo labels. However, these filtering
strategies are usually hand-crafted and do not change as the model is updated,
resulting in a lot of correct pseudo labels being discarded and incorrect
pseudo labels being selected during the training process. In this work, we
observe that the distribution gap between the confidence values of correct and
incorrect pseudo labels emerges at the very beginning of the training, which
can be utilized to filter pseudo labels. Based on this observation, we propose
a Self-Adaptive Pseudo-Label Filter (SPF), which automatically filters noise in
pseudo labels in accordance with model evolvement by modeling the confidence
distribution throughout the training process. Specifically, with an online
mixture model, we weight each pseudo-labeled sample by the posterior of it
being correct, which takes into consideration the confidence distribution at
that time. Unlike previous handcrafted filters, our SPF evolves together with
the deep neural network without manual tuning. Extensive experiments
demonstrate that incorporating SPF into the existing SSL methods can help
improve the performance of SSL, especially when the labeled data is extremely
scarce. | [
"Lei Zhu",
"Zhanghan Ke",
"Rynson Lau"
] | 2023-09-18 13:57:16 | http://arxiv.org/abs/2309.09774v1 | http://arxiv.org/pdf/2309.09774v1 | 2309.09774v1 |
Looking through the past: better knowledge retention for generative replay in continual learning | In this work, we improve the generative replay in a continual learning
setting to perform well on challenging scenarios. Current generative rehearsal
methods are usually benchmarked on small and simple datasets as they are not
powerful enough to generate more complex data with a greater number of classes.
We notice that in VAE-based generative replay, this could be attributed to the
fact that the generated features are far from the original ones when mapped to
the latent space. Therefore, we propose three modifications that allow the
model to learn and generate complex data. More specifically, we incorporate the
distillation in latent space between the current and previous models to reduce
feature drift. Additionally, a latent matching for the reconstruction and
original data is proposed to improve generated features alignment. Further,
based on the observation that the reconstructions are better for preserving
knowledge, we add the cycling of generations through the previously trained
model to make them closer to the original data. Our method outperforms other
generative replay methods in various scenarios. Code available at
https://github.com/valeriya-khan/looking-through-the-past. | [
"Valeriya Khan",
"Sebastian Cygert",
"Kamil Deja",
"Tomasz Trzciński",
"Bartłomiej Twardowski"
] | 2023-09-18 13:45:49 | http://arxiv.org/abs/2309.10012v1 | http://arxiv.org/pdf/2309.10012v1 | 2309.10012v1 |
Application-driven Validation of Posteriors in Inverse Problems | Current deep learning-based solutions for image analysis tasks are commonly
incapable of handling problems to which multiple different plausible solutions
exist. In response, posterior-based methods such as conditional Diffusion
Models and Invertible Neural Networks have emerged; however, their translation
is hampered by a lack of research on adequate validation. In other words, the
way progress is measured often does not reflect the needs of the driving
practical application. Closing this gap in the literature, we present the first
systematic framework for the application-driven validation of posterior-based
methods in inverse problems. As a methodological novelty, it adopts key
principles from the field of object detection validation, which has a long
history of addressing the question of how to locate and match multiple object
instances in an image. Treating modes as instances enables us to perform
mode-centric validation, using well-interpretable metrics from the application
perspective. We demonstrate the value of our framework through instantiations
for a synthetic toy example and two medical vision use cases: pose estimation
in surgery and imaging-based quantification of functional tissue parameters for
diagnostics. Our framework offers key advantages over common approaches to
posterior validation in all three examples and could thus revolutionize
performance assessment in inverse problems. | [
"Tim J. Adler",
"Jan-Hinrich Nölke",
"Annika Reinke",
"Minu Dietlinde Tizabi",
"Sebastian Gruber",
"Dasha Trofimova",
"Lynton Ardizzone",
"Paul F. Jaeger",
"Florian Buettner",
"Ullrich Köthe",
"Lena Maier-Hein"
] | 2023-09-18 13:44:36 | http://arxiv.org/abs/2309.09764v1 | http://arxiv.org/pdf/2309.09764v1 | 2309.09764v1 |
Contrastive Initial State Buffer for Reinforcement Learning | In Reinforcement Learning, the trade-off between exploration and exploitation
poses a complex challenge for achieving efficient learning from limited
samples. While recent works have been effective in leveraging past experiences
for policy updates, they often overlook the potential of reusing past
experiences for data collection. Independent of the underlying RL algorithm, we
introduce the concept of a Contrastive Initial State Buffer, which
strategically selects states from past experiences and uses them to initialize
the agent in the environment in order to guide it toward more informative
states. We validate our approach on two complex robotic tasks without relying
on any prior information about the environment: (i) locomotion of a quadruped
robot traversing challenging terrains and (ii) a quadcopter drone racing
through a track. The experimental results show that our initial state buffer
achieves higher task performance than the nominal baseline while also speeding
up training convergence. | [
"Nico Messikommer",
"Yunlong Song",
"Davide Scaramuzza"
] | 2023-09-18 13:26:40 | http://arxiv.org/abs/2309.09752v2 | http://arxiv.org/pdf/2309.09752v2 | 2309.09752v2 |
Towards Better Modeling with Missing Data: A Contrastive Learning-based Visual Analytics Perspective | Missing data can pose a challenge for machine learning (ML) modeling. To
address this, current approaches are categorized into feature imputation and
label prediction and are primarily focused on handling missing data to enhance
ML performance. These approaches rely on the observed data to estimate the
missing values and therefore encounter three main shortcomings in imputation,
including the need for different imputation methods for various missing data
mechanisms, heavy dependence on the assumption of data distribution, and
potential introduction of bias. This study proposes a Contrastive Learning (CL)
framework to model observed data with missing values, where the ML model learns
the similarity between an incomplete sample and its complete counterpart and
the dissimilarity between other samples. Our proposed approach demonstrates the
advantages of CL without requiring any imputation. To enhance interpretability,
we introduce CIVis, a visual analytics system that incorporates interpretable
techniques to visualize the learning process and diagnose the model status.
Users can leverage their domain knowledge through interactive sampling to
identify negative and positive pairs in CL. The output of CIVis is an optimized
model that takes specified features and predicts downstream tasks. We provide
two usage scenarios in regression and classification tasks and conduct
quantitative experiments, expert interviews, and a qualitative user study to
demonstrate the effectiveness of our approach. In short, this study offers a
valuable contribution to addressing the challenges associated with ML modeling
in the presence of missing data by providing a practical solution that achieves
high predictive accuracy and model interpretability. | [
"Laixin Xie",
"Yang Ouyang",
"Longfei Chen",
"Ziming Wu",
"Quan Li"
] | 2023-09-18 13:16:24 | http://arxiv.org/abs/2309.09744v1 | http://arxiv.org/pdf/2309.09744v1 | 2309.09744v1 |
The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flows | We propose the NFLikelihood, an unsupervised version, based on Normalizing
Flows, of the DNNLikelihood proposed in Ref.[1]. We show, through realistic
examples, how Autoregressive Flows, based on affine and rational quadratic
spline bijectors, are able to learn complicated high-dimensional Likelihoods
arising in High Energy Physics (HEP) analyses. We focus on a toy LHC analysis
example already considered in the literature and on two Effective Field Theory
fits of flavor and electroweak observables, whose samples have been obtained
throught the HEPFit code. We discuss advantages and disadvantages of the
unsupervised approach with respect to the supervised one and discuss possible
interplays of the two. | [
"Humberto Reyes-Gonzalez",
"Riccardo Torre"
] | 2023-09-18 13:13:47 | http://arxiv.org/abs/2309.09743v1 | http://arxiv.org/pdf/2309.09743v1 | 2309.09743v1 |
Moving Object Detection and Tracking with 4D Radar Point Cloud | Mobile autonomy relies on the precise perception of dynamic environments.
Robustly tracking moving objects in 3D world thus plays a pivotal role for
applications like trajectory prediction, obstacle avoidance, and path planning.
While most current methods utilize LiDARs or cameras for Multiple Object
Tracking (MOT), the capabilities of 4D imaging radars remain largely
unexplored. Recognizing the challenges posed by radar noise and point sparsity
in 4D radar data, we introduce RaTrack, an innovative solution tailored for
radar-based tracking. Bypassing the typical reliance on specific object types
and 3D bounding boxes, our method focuses on motion segmentation and
clustering, enriched by a motion estimation module. Evaluated on the
View-of-Delft dataset, RaTrack showcases superior tracking precision of moving
objects, largely surpassing the performance of the state of the art. | [
"Zhijun Pan",
"Fangqiang Ding",
"Hantao Zhong",
"Chris Xiaoxuan Lu"
] | 2023-09-18 13:02:29 | http://arxiv.org/abs/2309.09737v1 | http://arxiv.org/pdf/2309.09737v1 | 2309.09737v1 |
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input Representation | Over the last years we witnessed a renewed interest toward Traffic
Classification (TC) captivated by the rise of Deep Learning (DL). Yet, the vast
majority of TC literature lacks code artifacts, performance assessments across
datasets and reference comparisons against Machine Learning (ML) methods. Among
those works, a recent study from IMC22 [16] is worth of attention since it
adopts recent DL methodologies (namely, few-shot learning, self-supervision via
contrastive learning and data augmentation) appealing for networking as they
enable to learn from a few samples and transfer across datasets. The main
result of [16] on the UCDAVIS19, ISCX-VPN and ISCX-Tor datasets is that, with
such DL methodologies, 100 input samples are enough to achieve very high
accuracy using an input representation called "flowpic" (i.e., a per-flow 2d
histograms of the packets size evolution over time). In this paper (i) we
reproduce [16] on the same datasets and (ii) we replicate its most salient
aspect (the importance of data augmentation) on three additional public
datasets (MIRAGE19, MIRAGE22 and UTMOBILENET21). While we confirm most of the
original results, we also found a 20% accuracy drop on some of the investigated
scenarios due to a data shift in the original dataset that we uncovered.
Additionally, our study validates that the data augmentation strategies studied
in [16] perform well on other datasets too. In the spirit of reproducibility
and replicability we make all artifacts (code and data) available to the
research community at https://tcbenchstack.github.io/tcbench/ | [
"Alessandro Finamore",
"Chao Wang",
"Jonatan Krolikowski",
"Jose M. Navarro",
"Fuxing Chen",
"Dario Rossi"
] | 2023-09-18 12:55:09 | http://arxiv.org/abs/2309.09733v2 | http://arxiv.org/pdf/2309.09733v2 | 2309.09733v2 |
Neural Collapse for Unconstrained Feature Model under Cross-entropy Loss with Imbalanced Data | Recent years have witnessed the huge success of deep neural networks (DNNs)
in various tasks of computer vision and text processing. Interestingly, these
DNNs with massive number of parameters share similar structural properties on
their feature representation and last-layer classifier at terminal phase of
training (TPT). Specifically, if the training data are balanced (each class
shares the same number of samples), it is observed that the feature vectors of
samples from the same class converge to their corresponding in-class mean
features and their pairwise angles are the same. This fascinating phenomenon is
known as Neural Collapse (N C), first termed by Papyan, Han, and Donoho in
2019. Many recent works manage to theoretically explain this phenomenon by
adopting so-called unconstrained feature model (UFM). In this paper, we study
the extension of N C phenomenon to the imbalanced data under cross-entropy loss
function in the context of unconstrained feature model. Our contribution is
multi-fold compared with the state-of-the-art results: (a) we show that the
feature vectors exhibit collapse phenomenon, i.e., the features within the same
class collapse to the same mean vector; (b) the mean feature vectors no longer
form an equiangular tight frame. Instead, their pairwise angles depend on the
sample size; (c) we also precisely characterize the sharp threshold on which
the minority collapse (the feature vectors of the minority groups collapse to
one single vector) will take place; (d) finally, we argue that the effect of
the imbalance in datasize diminishes as the sample size grows. Our results
provide a complete picture of the N C under the cross-entropy loss for the
imbalanced data. Numerical experiments confirm our theoretical analysis. | [
"Wanli Hong",
"Shuyang Ling"
] | 2023-09-18 12:45:08 | http://arxiv.org/abs/2309.09725v1 | http://arxiv.org/pdf/2309.09725v1 | 2309.09725v1 |
Traffic Scene Similarity: a Graph-based Contrastive Learning Approach | Ensuring validation for highly automated driving poses significant obstacles
to the widespread adoption of highly automated vehicles. Scenario-based testing
offers a potential solution by reducing the homologation effort required for
these systems. However, a crucial prerequisite, yet unresolved, is the
definition and reduction of the test space to a finite number of scenarios. To
tackle this challenge, we propose an extension to a contrastive learning
approach utilizing graphs to construct a meaningful embedding space. Our
approach demonstrates the continuous mapping of scenes using scene-specific
features and the formation of thematically similar clusters based on the
resulting embeddings. Based on the found clusters, similar scenes could be
identified in the subsequent test process, which can lead to a reduction in
redundant test runs. | [
"Maximilian Zipfl",
"Moritz Jarosch",
"J. Marius Zöllner"
] | 2023-09-18 12:35:08 | http://arxiv.org/abs/2309.09720v1 | http://arxiv.org/pdf/2309.09720v1 | 2309.09720v1 |
FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data | Federated learning is an emerging distributed machine learning method,
enables a large number of clients to train a model without exchanging their
local data. The time cost of communication is an essential bottleneck in
federated learning, especially for training large-scale deep neural networks.
Some communication-efficient federated learning methods, such as FedAvg and
FedAdam, share the same learning rate across different clients. But they are
not efficient when data is heterogeneous. To maximize the performance of
optimization methods, the main challenge is how to adjust the learning rate
without hurting the convergence. In this paper, we propose a heterogeneous
local variant of AMSGrad, named FedLALR, in which each client adjusts its
learning rate based on local historical gradient squares and synchronized
learning rates. Theoretical analysis shows that our client-specified auto-tuned
learning rate scheduling can converge and achieve linear speedup with respect
to the number of clients, which enables promising scalability in federated
optimization. We also empirically compare our method with several
communication-efficient federated optimization methods. Extensive experimental
results on Computer Vision (CV) tasks and Natural Language Processing (NLP)
task show the efficacy of our proposed FedLALR method and also coincides with
our theoretical findings. | [
"Hao Sun",
"Li Shen",
"Shixiang Chen",
"Jingwei Sun",
"Jing Li",
"Guangzhong Sun",
"Dacheng Tao"
] | 2023-09-18 12:35:05 | http://arxiv.org/abs/2309.09719v1 | http://arxiv.org/pdf/2309.09719v1 | 2309.09719v1 |
Multi-Dictionary Tensor Decomposition | Tensor decomposition methods are popular tools for analysis of multi-way
datasets from social media, healthcare, spatio-temporal domains, and others.
Widely adopted models such as Tucker and canonical polyadic decomposition (CPD)
follow a data-driven philosophy: they decompose a tensor into factors that
approximate the observed data well. In some cases side information is available
about the tensor modes. For example, in a temporal user-item purchases tensor a
user influence graph, an item similarity graph, and knowledge about seasonality
or trends in the temporal mode may be available. Such side information may
enable more succinct and interpretable tensor decomposition models and improved
quality in downstream tasks.
We propose a framework for Multi-Dictionary Tensor Decomposition (MDTD) which
takes advantage of prior structural information about tensor modes in the form
of coding dictionaries to obtain sparsely encoded tensor factors. We derive a
general optimization algorithm for MDTD that handles both complete input and
input with missing values. Our framework handles large sparse tensors typical
to many real-world application domains. We demonstrate MDTD's utility via
experiments with both synthetic and real-world datasets. It learns more concise
models than dictionary-free counterparts and improves (i) reconstruction
quality ($60\%$ fewer non-zero coefficients coupled with smaller error); (ii)
missing values imputation quality (two-fold MSE reduction with up to orders of
magnitude time savings) and (iii) the estimation of the tensor rank. MDTD's
quality improvements do not come with a running time premium: it can decompose
$19GB$ datasets in less than a minute. It can also impute missing values in
sparse billion-entry tensors more accurately and scalably than state-of-the-art
competitors. | [
"Maxwell McNeil",
"Petko Bogdanov"
] | 2023-09-18 12:31:56 | http://arxiv.org/abs/2309.09717v1 | http://arxiv.org/pdf/2309.09717v1 | 2309.09717v1 |
Dealing with negative samples with multi-task learning on span-based joint entity-relation extraction | Recent span-based joint extraction models have demonstrated significant
advantages in both entity recognition and relation extraction. These models
treat text spans as candidate entities, and span pairs as candidate
relationship tuples, achieving state-of-the-art results on datasets like ADE.
However, these models encounter a significant number of non-entity spans or
irrelevant span pairs during the tasks, impairing model performance
significantly. To address this issue, this paper introduces a span-based
multitask entity-relation joint extraction model. This approach employs the
multitask learning to alleviate the impact of negative samples on entity and
relation classifiers. Additionally, we leverage the Intersection over
Union(IoU) concept to introduce the positional information into the entity
classifier, achieving a span boundary detection. Furthermore, by incorporating
the entity Logits predicted by the entity classifier into the embedded
representation of entity pairs, the semantic input for the relation classifier
is enriched. Experimental results demonstrate that our proposed SpERT.MT model
can effectively mitigate the adverse effects of excessive negative samples on
the model performance. Furthermore, the model demonstrated commendable F1
scores of 73.61\%, 53.72\%, and 83.72\% on three widely employed public
datasets, namely CoNLL04, SciERC, and ADE, respectively. | [
"Chenguang Xue",
"Jiamin Lu"
] | 2023-09-18 12:28:46 | http://arxiv.org/abs/2309.09713v1 | http://arxiv.org/pdf/2309.09713v1 | 2309.09713v1 |
Information based explanation methods for deep learning agents -- with applications on large open-source chess models | With large chess-playing neural network models like AlphaZero contesting the
state of the art within the world of computerised chess, two challenges present
themselves: The question of how to explain the domain knowledge internalised by
such models, and the problem that such models are not made openly available.
This work presents the re-implementation of the concept detection methodology
applied to AlphaZero in McGrath et al. (2022), by using large, open-source
chess models with comparable performance. We obtain results similar to those
achieved on AlphaZero, while relying solely on open-source resources. We also
present a novel explainable AI (XAI) method, which is guaranteed to highlight
exhaustively and exclusively the information used by the explained model. This
method generates visual explanations tailored to domains characterised by
discrete input spaces, as is the case for chess. Our presented method has the
desirable property of controlling the information flow between any input vector
and the given model, which in turn provides strict guarantees regarding what
information is used by the trained model during inference. We demonstrate the
viability of our method by applying it to standard 8x8 chess, using large
open-source chess models. | [
"Patrik Hammersborg",
"Inga Strümke"
] | 2023-09-18 12:08:14 | http://arxiv.org/abs/2309.09702v1 | http://arxiv.org/pdf/2309.09702v1 | 2309.09702v1 |
A Study of Data-driven Methods for Adaptive Forecasting of COVID-19 Cases | Severe acute respiratory disease SARS-CoV-2 has had a found impact on public
health systems and healthcare emergency response especially with respect to
making decisions on the most effective measures to be taken at any given time.
As demonstrated throughout the last three years with COVID-19, the prediction
of the number of positive cases can be an effective way to facilitate
decision-making. However, the limited availability of data and the highly
dynamic and uncertain nature of the virus transmissibility makes this task very
challenging. Aiming at investigating these challenges and in order to address
this problem, this work studies data-driven (learning, statistical) methods for
incrementally training models to adapt to these nonstationary conditions. An
extensive empirical study is conducted to examine various characteristics, such
as, performance analysis on a per virus wave basis, feature extraction,
"lookback" window size, memory size, all for next-, 7-, and 14-day forecasting
tasks. We demonstrate that the incremental learning framework can successfully
address the aforementioned challenges and perform well during outbreaks,
providing accurate predictions. | [
"Charithea Stylianides",
"Kleanthis Malialis",
"Panayiotis Kolios"
] | 2023-09-18 12:03:01 | http://arxiv.org/abs/2309.09698v1 | http://arxiv.org/pdf/2309.09698v1 | 2309.09698v1 |
Noise-Augmented Boruta: The Neural Network Perturbation Infusion with Boruta Feature Selection | With the surge in data generation, both vertically (i.e., volume of data) and
horizontally (i.e., dimensionality), the burden of the curse of dimensionality
has become increasingly palpable. Feature selection, a key facet of
dimensionality reduction techniques, has advanced considerably to address this
challenge. One such advancement is the Boruta feature selection algorithm,
which successfully discerns meaningful features by contrasting them to their
permutated counterparts known as shadow features. However, the significance of
a feature is shaped more by the data's overall traits than by its intrinsic
value, a sentiment echoed in the conventional Boruta algorithm where shadow
features closely mimic the characteristics of the original ones. Building on
this premise, this paper introduces an innovative approach to the Boruta
feature selection algorithm by incorporating noise into the shadow variables.
Drawing parallels from the perturbation analysis framework of artificial neural
networks, this evolved version of the Boruta method is presented. Rigorous
testing on four publicly available benchmark datasets revealed that this
proposed technique outperforms the classic Boruta algorithm, underscoring its
potential for enhanced, accurate feature selection. | [
"Hassan Gharoun",
"Navid Yazdanjoe",
"Mohammad Sadegh Khorshidi",
"Amir H. Gandomi"
] | 2023-09-18 11:59:06 | http://arxiv.org/abs/2309.09694v1 | http://arxiv.org/pdf/2309.09694v1 | 2309.09694v1 |
Single and Few-step Diffusion for Generative Speech Enhancement | Diffusion models have shown promising results in speech enhancement, using a
task-adapted diffusion process for the conditional generation of clean speech
given a noisy mixture. However, at test time, the neural network used for score
estimation is called multiple times to solve the iterative reverse process.
This results in a slow inference process and causes discretization errors that
accumulate over the sampling trajectory. In this paper, we address these
limitations through a two-stage training approach. In the first stage, we train
the diffusion model the usual way using the generative denoising score matching
loss. In the second stage, we compute the enhanced signal by solving the
reverse process and compare the resulting estimate to the clean speech target
using a predictive loss. We show that using this second training stage enables
achieving the same performance as the baseline model using only 5 function
evaluations instead of 60 function evaluations. While the performance of usual
generative diffusion algorithms drops dramatically when lowering the number of
function evaluations (NFEs) to obtain single-step diffusion, we show that our
proposed method keeps a steady performance and therefore largely outperforms
the diffusion baseline in this setting and also generalizes better than its
predictive counterpart. | [
"Bunlong Lay",
"Jean-Marie Lemercier",
"Julius Richter",
"Timo Gerkmann"
] | 2023-09-18 11:30:58 | http://arxiv.org/abs/2309.09677v1 | http://arxiv.org/pdf/2309.09677v1 | 2309.09677v1 |
VULNERLIZER: Cross-analysis Between Vulnerabilities and Software Libraries | The identification of vulnerabilities is a continuous challenge in software
projects. This is due to the evolution of methods that attackers employ as well
as the constant updates to the software, which reveal additional issues. As a
result, new and innovative approaches for the identification of vulnerable
software are needed. In this paper, we present VULNERLIZER, which is a novel
framework for cross-analysis between vulnerabilities and software libraries. It
uses CVE and software library data together with clustering algorithms to
generate links between vulnerabilities and libraries. In addition, the training
of the model is conducted in order to reevaluate the generated associations.
This is achieved by updating the assigned weights. Finally, the approach is
then evaluated by making the predictions using the CVE data from the test set.
The results show that the VULNERLIZER has a great potential in being able to
predict future vulnerable libraries based on an initial input CVE entry or a
software library. The trained model reaches a prediction accuracy of 75% or
higher. | [
"Irdin Pekaric",
"Michael Felderer",
"Philipp Steinmüller"
] | 2023-09-18 10:34:47 | http://arxiv.org/abs/2309.09649v1 | http://arxiv.org/pdf/2309.09649v1 | 2309.09649v1 |
Causal Discovery and Counterfactual Explanations for Personalized Student Learning | The paper focuses on identifying the causes of student performance to provide
personalized recommendations for improving pass rates. We introduce the need to
move beyond predictive models and instead identify causal relationships. We
propose using causal discovery techniques to achieve this. The study's main
contributions include using causal discovery to identify causal predictors of
student performance and applying counterfactual analysis to provide
personalized recommendations. The paper describes the application of causal
discovery methods, specifically the PC algorithm, to real-life student
performance data. It addresses challenges such as sample size limitations and
emphasizes the role of domain knowledge in causal discovery. The results reveal
the identified causal relationships, such as the influence of earlier test
grades and mathematical ability on final student performance. Limitations of
this study include the reliance on domain expertise for accurate causal
discovery, and the necessity of larger sample sizes for reliable results. The
potential for incorrect causal structure estimations is acknowledged. A major
challenge remains, which is the real-time implementation and validation of
counterfactual recommendations. In conclusion, the paper demonstrates the value
of causal discovery for understanding student performance and providing
personalized recommendations. It highlights the challenges, benefits, and
limitations of using causal inference in an educational context, setting the
stage for future studies to further explore and refine these methods. | [
"Bevan I. Smith"
] | 2023-09-18 10:32:47 | http://arxiv.org/abs/2309.13066v1 | http://arxiv.org/pdf/2309.13066v1 | 2309.13066v1 |
Neural Network-Based Rule Models With Truth Tables | Understanding the decision-making process of a machine/deep learning model is
crucial, particularly in security-sensitive applications. In this study, we
introduce a neural network framework that combines the global and exact
interpretability properties of rule-based models with the high performance of
deep neural networks.
Our proposed framework, called $\textit{Truth Table rules}$ (TT-rules), is
built upon $\textit{Truth Table nets}$ (TTnets), a family of deep neural
networks initially developed for formal verification. By extracting the set of
necessary and sufficient rules $\mathcal{R}$ from the trained TTnet model
(global interpretability), yielding the same output as the TTnet (exact
interpretability), TT-rules effectively transforms the neural network into a
rule-based model. This rule-based model supports binary classification,
multi-label classification, and regression tasks for tabular datasets.
Furthermore, our TT-rules framework optimizes the rule set $\mathcal{R}$ into
$\mathcal{R}_{opt}$ by reducing the number and size of the rules. To enhance
model interpretation, we leverage Reduced Ordered Binary Decision Diagrams
(ROBDDs) to visualize these rules effectively.
After outlining the framework, we evaluate the performance of TT-rules on
seven tabular datasets from finance, healthcare, and justice domains. We also
compare the TT-rules framework to state-of-the-art rule-based methods. Our
results demonstrate that TT-rules achieves equal or higher performance compared
to other interpretable methods while maintaining a balance between performance
and complexity. Notably, TT-rules presents the first accurate rule-based model
capable of fitting large tabular datasets, including two real-life DNA datasets
with over 20K features. Finally, we extensively investigate a rule-based model
derived from TT-rules using the Adult dataset. | [
"Adrien Benamira",
"Tristan Guérand",
"Thomas Peyrin",
"Hans Soegeng"
] | 2023-09-18 10:13:59 | http://arxiv.org/abs/2309.09638v1 | http://arxiv.org/pdf/2309.09638v1 | 2309.09638v1 |
Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulation | Identification of cracks is essential to assess the structural integrity of
concrete infrastructure. However, robust crack segmentation remains a
challenging task for computer vision systems due to the diverse appearance of
concrete surfaces, variable lighting and weather conditions, and the
overlapping of different defects. In particular recent data-driven methods
struggle with the limited availability of data, the fine-grained and
time-consuming nature of crack annotation, and face subsequent difficulty in
generalizing to out-of-distribution samples. In this work, we move past these
challenges in a two-fold way. We introduce a high-fidelity crack graphics
simulator based on fractals and a corresponding fully-annotated crack dataset.
We then complement the latter with a system that learns generalizable
representations from simulation, by leveraging both a pointwise mutual
information estimate along with adaptive instance normalization as inductive
biases. Finally, we empirically highlight how different design choices are
symbiotic in bridging the simulation to real gap, and ultimately demonstrate
that our introduced system can effectively handle real-world crack
segmentation. | [
"Achref Jaziri",
"Martin Mundt",
"Andres Fernandez Rodriguez",
"Visvanathan Ramesh"
] | 2023-09-18 10:13:03 | http://arxiv.org/abs/2309.09637v1 | http://arxiv.org/pdf/2309.09637v1 | 2309.09637v1 |
Subsets and Splits