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Constructing Synthetic Treatment Groups without the Mean Exchangeability Assumption | The purpose of this work is to transport the information from multiple
randomized controlled trials to the target population where we only have the
control group data. Previous works rely critically on the mean exchangeability
assumption. However, as pointed out by many current studies, the mean
exchangeability assumption might be violated. Motivated by the synthetic
control method, we construct a synthetic treatment group for the target
population by a weighted mixture of treatment groups of source populations. We
estimate the weights by minimizing the conditional maximum mean discrepancy
between the weighted control groups of source populations and the target
population. We establish the asymptotic normality of the synthetic treatment
group estimator based on the sieve semiparametric theory. Our method can serve
as a novel complementary approach when the mean exchangeability assumption is
violated. Experiments are conducted on synthetic and real-world datasets to
demonstrate the effectiveness of our methods. | [
"Yuhang Zhang",
"Yue Liu",
"Zhihua Zhang"
] | 2023-09-28 13:00:56 | http://arxiv.org/abs/2309.16409v1 | http://arxiv.org/pdf/2309.16409v1 | 2309.16409v1 |
VAE-based latent-space classification of RNO-G data | The Radio Neutrino Observatory in Greenland (RNO-G) is a radio-based
ultra-high energy neutrino detector located at Summit Station, Greenland. It is
still being constructed, with 7 stations currently operational. Neutrino
detection works by measuring Askaryan radiation produced by neutrino-nucleon
interactions. A neutrino candidate must be found amidst other backgrounds which
are recorded at much higher rates -- including cosmic-rays and anthropogenic
noise -- the origins of which are sometimes unknown. Here we describe a method
to classify different noise classes using the latent space of a variational
autoencoder. The latent space forms a compact representation that makes
classification tractable. We analyze data from a noisy and a silent station.
The method automatically detects and allows us to qualitatively separate
multiple event classes, including physical wind-induced signals, for both the
noisy and the quiet station. | [
"Thorsten Glüsenkamp"
] | 2023-09-28 12:47:38 | http://arxiv.org/abs/2309.16401v1 | http://arxiv.org/pdf/2309.16401v1 | 2309.16401v1 |
Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey | Differential Privacy has become a widely popular method for data protection
in machine learning, especially since it allows formulating strict mathematical
privacy guarantees. This survey provides an overview of the state-of-the-art of
differentially private centralized deep learning, thorough analyses of recent
advances and open problems, as well as a discussion of potential future
developments in the field. Based on a systematic literature review, the
following topics are addressed: auditing and evaluation methods for private
models, improvements of privacy-utility trade-offs, protection against a broad
range of threats and attacks, differentially private generative models, and
emerging application domains. | [
"Lea Demelius",
"Roman Kern",
"Andreas Trügler"
] | 2023-09-28 12:44:59 | http://arxiv.org/abs/2309.16398v1 | http://arxiv.org/pdf/2309.16398v1 | 2309.16398v1 |
Uncertainty-Aware Decision Transformer for Stochastic Driving Environments | Offline Reinforcement Learning (RL) has emerged as a promising framework for
learning policies without active interactions, making it especially appealing
for autonomous driving tasks. Recent successes of Transformers inspire casting
offline RL as sequence modeling, which performs well in long-horizon tasks.
However, they are overly optimistic in stochastic environments with incorrect
assumptions that the same goal can be consistently achieved by identical
actions. In this paper, we introduce an UNcertainty-awaRE deciSion Transformer
(UNREST) for planning in stochastic driving environments without introducing
additional transition or complex generative models. Specifically, UNREST
estimates state uncertainties by the conditional mutual information between
transitions and returns, and segments sequences accordingly. Discovering the
`uncertainty accumulation' and `temporal locality' properties of driving
environments, UNREST replaces the global returns in decision transformers with
less uncertain truncated returns, to learn from true outcomes of agent actions
rather than environment transitions. We also dynamically evaluate environmental
uncertainty during inference for cautious planning. Extensive experimental
results demonstrate UNREST's superior performance in various driving scenarios
and the power of our uncertainty estimation strategy. | [
"Zenan Li",
"Fan Nie",
"Qiao Sun",
"Fang Da",
"Hang Zhao"
] | 2023-09-28 12:44:51 | http://arxiv.org/abs/2309.16397v1 | http://arxiv.org/pdf/2309.16397v1 | 2309.16397v1 |
Differential 2D Copula Approximating Transforms via Sobolev Training: 2-Cats Networks | Copulas are a powerful statistical tool that captures dependencies across
data dimensions. When applying Copulas, we can estimate multivariate
distribution functions by initially estimating independent marginals, an easy
task, and then a single copulating function, $C$, to connect the marginals, a
hard task. For two-dimensional data, a copula is a two-increasing function of
the form $C: (u,v)\in \mathbf{I}^2 \rightarrow \mathbf{I}$, where $\mathbf{I} =
[0, 1]$. In this paper, we show how Neural Networks (NNs) can approximate any
two-dimensional copula non-parametrically. Our approach, denoted as 2-Cats, is
inspired by the Physics-Informed Neural Networks and Sobolev Training
literature. Not only do we show that we can estimate the output of a 2d Copula
better than the state-of-the-art, our approach is non-parametric and respects
the mathematical properties of a Copula $C$. | [
"Flavio Figueiredo",
"José Geraldo Fernandes",
"Jackson Silva",
"Renato M. Assunção"
] | 2023-09-28 12:38:47 | http://arxiv.org/abs/2309.16391v1 | http://arxiv.org/pdf/2309.16391v1 | 2309.16391v1 |
Multi-Swap $k$-Means++ | The $k$-means++ algorithm of Arthur and Vassilvitskii (SODA 2007) is often
the practitioners' choice algorithm for optimizing the popular $k$-means
clustering objective and is known to give an $O(\log k)$-approximation in
expectation. To obtain higher quality solutions, Lattanzi and Sohler (ICML
2019) proposed augmenting $k$-means++ with $O(k \log \log k)$ local search
steps obtained through the $k$-means++ sampling distribution to yield a
$c$-approximation to the $k$-means clustering problem, where $c$ is a large
absolute constant. Here we generalize and extend their local search algorithm
by considering larger and more sophisticated local search neighborhoods hence
allowing to swap multiple centers at the same time. Our algorithm achieves a $9
+ \varepsilon$ approximation ratio, which is the best possible for local
search. Importantly we show that our approach yields substantial practical
improvements, we show significant quality improvements over the approach of
Lattanzi and Sohler (ICML 2019) on several datasets. | [
"Lorenzo Beretta",
"Vincent Cohen-Addad",
"Silvio Lattanzi",
"Nikos Parotsidis"
] | 2023-09-28 12:31:35 | http://arxiv.org/abs/2309.16384v1 | http://arxiv.org/pdf/2309.16384v1 | 2309.16384v1 |
RLLTE: Long-Term Evolution Project of Reinforcement Learning | We present RLLTE: a long-term evolution, extremely modular, and open-source
framework for reinforcement learning (RL) research and application. Beyond
delivering top-notch algorithm implementations, RLLTE also serves as a toolkit
for developing algorithms. More specifically, RLLTE decouples the RL algorithms
completely from the exploitation-exploration perspective, providing a large
number of components to accelerate algorithm development and evolution. In
particular, RLLTE is the first RL framework to build a complete and luxuriant
ecosystem, which includes model training, evaluation, deployment, benchmark
hub, and large language model (LLM)-empowered copilot. RLLTE is expected to set
standards for RL engineering practice and be highly stimulative for industry
and academia. | [
"Mingqi Yuan",
"Zequn Zhang",
"Yang Xu",
"Shihao Luo",
"Bo Li",
"Xin Jin",
"Wenjun Zeng"
] | 2023-09-28 12:30:37 | http://arxiv.org/abs/2309.16382v1 | http://arxiv.org/pdf/2309.16382v1 | 2309.16382v1 |
A Comprehensive Review on Tree Detection Methods Using Point Cloud and Aerial Imagery from Unmanned Aerial Vehicles | Unmanned Aerial Vehicles (UAVs) are considered cutting-edge technology with
highly cost-effective and flexible usage scenarios. Although many papers have
reviewed the application of UAVs in agriculture, the review of the application
for tree detection is still insufficient. This paper focuses on tree detection
methods applied to UAV data collected by UAVs. There are two kinds of data, the
point cloud and the images, which are acquired by the Light Detection and
Ranging (LiDAR) sensor and camera, respectively. Among the detection methods
using point-cloud data, this paper mainly classifies these methods according to
LiDAR and Digital Aerial Photography (DAP). For the detection methods using
images directly, this paper reviews these methods by whether or not to use the
Deep Learning (DL) method. Our review concludes and analyses the comparison and
combination between the application of LiDAR-based and DAP-based point cloud
data. The performance, relative merits, and application fields of the methods
are also introduced. Meanwhile, this review counts the number of tree detection
studies using different methods in recent years. From our statics, the
detection task using DL methods on the image has become a mainstream trend as
the number of DL-based detection researches increases to 45% of the total
number of tree detection studies up to 2022. As a result, this review could
help and guide researchers who want to carry out tree detection on specific
forests and for farmers to use UAVs in managing agriculture production. | [
"Weijie Kuang",
"Hann Woei Ho",
"Ye Zhou",
"Shahrel Azmin Suandi",
"Farzad Ismail"
] | 2023-09-28 12:22:39 | http://arxiv.org/abs/2309.16375v2 | http://arxiv.org/pdf/2309.16375v2 | 2309.16375v2 |
MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network | Property prediction plays an important role in material discovery. As an
initial step to eventually develop a foundation model for material science, we
introduce a new autoencoder called the MHG-GNN, which combines graph neural
network (GNN) with Molecular Hypergraph Grammar (MHG). Results on a variety of
property prediction tasks with diverse materials show that MHG-GNN is
promising. | [
"Akihiro Kishimoto",
"Hiroshi Kajino",
"Masataka Hirose",
"Junta Fuchiwaki",
"Indra Priyadarsini",
"Lisa Hamada",
"Hajime Shinohara",
"Daiju Nakano",
"Seiji Takeda"
] | 2023-09-28 12:19:43 | http://arxiv.org/abs/2309.16374v1 | http://arxiv.org/pdf/2309.16374v1 | 2309.16374v1 |
Bringing the Discussion of Minima Sharpness to the Audio Domain: a Filter-Normalised Evaluation for Acoustic Scene Classification | The correlation between the sharpness of loss minima and generalisation in
the context of deep neural networks has been subject to discussion for a long
time. Whilst mostly investigated in the context of selected benchmark data sets
in the area of computer vision, we explore this aspect for the audio scene
classification task of the DCASE2020 challenge data. Our analysis is based on
twodimensional filter-normalised visualisations and a derived sharpness
measure. Our exploratory analysis shows that sharper minima tend to show better
generalisation than flat minima -even more so for out-of-domain data, recorded
from previously unseen devices-, thus adding to the dispute about better
generalisation capabilities of flat minima. We further find that, in
particular, the choice of optimisers is a main driver of the sharpness of
minima and we discuss resulting limitations with respect to comparability. Our
code, trained model states and loss landscape visualisations are publicly
available. | [
"Manuel Milling",
"Andreas Triantafyllopoulos",
"Iosif Tsangko",
"Simon David Noel Rampp",
"Björn Wolfgang Schuller"
] | 2023-09-28 12:13:23 | http://arxiv.org/abs/2309.16369v1 | http://arxiv.org/pdf/2309.16369v1 | 2309.16369v1 |
Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge Graphs | Most knowledge graph completion (KGC) methods learn latent representations of
entities and relations of a given graph by mapping them into a vector space.
Although the majority of these methods focus on static knowledge graphs, a
large number of publicly available KGs contain temporal information stating the
time instant/period over which a certain fact has been true. Such graphs are
often known as temporal knowledge graphs. Furthermore, knowledge graphs may
also contain textual descriptions of entities and relations. Both temporal
information and textual descriptions are not taken into account during
representation learning by static KGC methods, and only structural information
of the graph is leveraged. Recently, some studies have used temporal
information to improve link prediction, yet they do not exploit textual
descriptions and do not support inductive inference (prediction on entities
that have not been seen in training).
We propose a novel framework called TEMT that exploits the power of
pre-trained language models (PLMs) for text-enhanced temporal knowledge graph
completion. The knowledge stored in the parameters of a PLM allows TEMT to
produce rich semantic representations of facts and to generalize on previously
unseen entities. TEMT leverages textual and temporal information available in a
KG, treats them separately, and fuses them to get plausibility scores of facts.
Unlike previous approaches, TEMT effectively captures dependencies across
different time points and enables predictions on unseen entities. To assess the
performance of TEMT, we carried out several experiments including time interval
prediction, both in transductive and inductive settings, and triple
classification. The experimental results show that TEMT is competitive with the
state-of-the-art. | [
"Duygu Sezen Islakoglu",
"Mel Chekol",
"Yannis Velegrakis"
] | 2023-09-28 11:43:49 | http://arxiv.org/abs/2309.16357v1 | http://arxiv.org/pdf/2309.16357v1 | 2309.16357v1 |
Transformer-VQ: Linear-Time Transformers via Vector Quantization | We introduce Transformer-VQ, a decoder-only transformer computing
softmax-based dense self-attention in linear time. Transformer-VQ's efficient
attention is enabled by vector-quantized keys and a novel caching mechanism. In
large-scale experiments, Transformer-VQ is shown highly competitive in quality,
with strong results on Enwik8 (0.99 bpb), PG-19 (26.6 ppl), and ImageNet64
(3.16 bpb). Code: https://github.com/transformer-vq/transformer_vq | [
"Lucas D. Lingle"
] | 2023-09-28 11:26:52 | http://arxiv.org/abs/2309.16354v1 | http://arxiv.org/pdf/2309.16354v1 | 2309.16354v1 |
ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter Averaging | Time series data can be found in almost every domain, ranging from the
medical field to manufacturing and wireless communication. Generating realistic
and useful exemplars and prototypes is a fundamental data analysis task. In
this paper, we investigate a novel approach to generating realistic and useful
exemplars and prototypes for time series data. Our approach uses a new form of
time series average, the ShapeDTW Barycentric Average. We therefore turn our
attention to accurately generating time series prototypes with a novel
approach. The existing time series prototyping approaches rely on the Dynamic
Time Warping (DTW) similarity measure such as DTW Barycentering Average (DBA)
and SoftDBA. These last approaches suffer from a common problem of generating
out-of-distribution artifacts in their prototypes. This is mostly caused by the
DTW variant used and its incapability of detecting neighborhood similarities,
instead it detects absolute similarities. Our proposed method, ShapeDBA, uses
the ShapeDTW variant of DTW, that overcomes this issue. We chose time series
clustering, a popular form of time series analysis to evaluate the outcome of
ShapeDBA compared to the other prototyping approaches. Coupled with the k-means
clustering algorithm, and evaluated on a total of 123 datasets from the UCR
archive, our proposed averaging approach is able to achieve new
state-of-the-art results in terms of Adjusted Rand Index. | [
"Ali Ismail-Fawaz",
"Hassan Ismail Fawaz",
"François Petitjean",
"Maxime Devanne",
"Jonathan Weber",
"Stefano Berretti",
"Geoffrey I. Webb",
"Germain Forestier"
] | 2023-09-28 11:25:02 | http://arxiv.org/abs/2309.16353v1 | http://arxiv.org/pdf/2309.16353v1 | 2309.16353v1 |
Intrinsic Language-Guided Exploration for Complex Long-Horizon Robotic Manipulation Tasks | Current reinforcement learning algorithms struggle in sparse and complex
environments, most notably in long-horizon manipulation tasks entailing a
plethora of different sequences. In this work, we propose the Intrinsically
Guided Exploration from Large Language Models (IGE-LLMs) framework. By
leveraging LLMs as an assistive intrinsic reward, IGE-LLMs guides the
exploratory process in reinforcement learning to address intricate long-horizon
with sparse rewards robotic manipulation tasks. We evaluate our framework and
related intrinsic learning methods in an environment challenged with
exploration, and a complex robotic manipulation task challenged by both
exploration and long-horizons. Results show IGE-LLMs (i) exhibit notably higher
performance over related intrinsic methods and the direct use of LLMs in
decision-making, (ii) can be combined and complement existing learning methods
highlighting its modularity, (iii) are fairly insensitive to different
intrinsic scaling parameters, and (iv) maintain robustness against increased
levels of uncertainty and horizons. | [
"Eleftherios Triantafyllidis",
"Filippos Christianos",
"Zhibin Li"
] | 2023-09-28 11:14:52 | http://arxiv.org/abs/2309.16347v1 | http://arxiv.org/pdf/2309.16347v1 | 2309.16347v1 |
LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite | Machine learning has been successfully applied to grid-based PDE modeling in
various scientific applications. However, learned PDE solvers based on
Lagrangian particle discretizations, which are the preferred approach to
problems with free surfaces or complex physics, remain largely unexplored. We
present LagrangeBench, the first benchmarking suite for Lagrangian particle
problems, focusing on temporal coarse-graining. In particular, our contribution
is: (a) seven new fluid mechanics datasets (four in 2D and three in 3D)
generated with the Smoothed Particle Hydrodynamics (SPH) method including the
Taylor-Green vortex, lid-driven cavity, reverse Poiseuille flow, and dam break,
each of which includes different physics like solid wall interactions or free
surface, (b) efficient JAX-based API with various recent training strategies
and neighbors search routine, and (c) JAX implementation of established Graph
Neural Networks (GNNs) like GNS and SEGNN with baseline results. Finally, to
measure the performance of learned surrogates we go beyond established position
errors and introduce physical metrics like kinetic energy MSE and Sinkhorn
distance for the particle distribution. Our codebase is available under the
URL: https://github.com/tumaer/lagrangebench | [
"Artur P. Toshev",
"Gianluca Galletti",
"Fabian Fritz",
"Stefan Adami",
"Nikolaus A. Adams"
] | 2023-09-28 11:03:23 | http://arxiv.org/abs/2309.16342v1 | http://arxiv.org/pdf/2309.16342v1 | 2309.16342v1 |
EFFL: Egalitarian Fairness in Federated Learning for Mitigating Matthew Effect | Recent advances in federated learning (FL) enable collaborative training of
machine learning (ML) models from large-scale and widely dispersed clients
while protecting their privacy. However, when different clients' datasets are
heterogeneous, traditional FL mechanisms produce a global model that does not
adequately represent the poorer clients with limited data resources, resulting
in lower accuracy and higher bias on their local data. According to the Matthew
effect, which describes how the advantaged gain more advantage and the
disadvantaged lose more over time, deploying such a global model in client
applications may worsen the resource disparity among the clients and harm the
principles of social welfare and fairness. To mitigate the Matthew effect, we
propose Egalitarian Fairness Federated Learning (EFFL), where egalitarian
fairness refers to the global model learned from FL has: (1) equal accuracy
among clients; (2) equal decision bias among clients. Besides achieving
egalitarian fairness among the clients, EFFL also aims for performance
optimality, minimizing the empirical risk loss and the bias for each client;
both are essential for any ML model training, whether centralized or
decentralized. We formulate EFFL as a constrained multi-constrained
multi-objectives optimization (MCMOO) problem, with the decision bias and
egalitarian fairness as constraints and the minimization of the empirical risk
losses on all clients as multiple objectives to be optimized. We propose a
gradient-based three-stage algorithm to obtain the Pareto optimal solutions
within the constraint space. Extensive experiments demonstrate that EFFL
outperforms other state-of-the-art FL algorithms in achieving a
high-performance global model with enhanced egalitarian fairness among all
clients. | [
"Jiashi Gao",
"Changwu Huang",
"Ming Tang",
"Shin Hwei Tan",
"Xin Yao",
"Xuetao Wei"
] | 2023-09-28 10:51:12 | http://arxiv.org/abs/2309.16338v1 | http://arxiv.org/pdf/2309.16338v1 | 2309.16338v1 |
End-to-end Risk Prediction of Atrial Fibrillation from the 12-Lead ECG by Deep Neural Networks | Background: Atrial fibrillation (AF) is one of the most common cardiac
arrhythmias that affects millions of people each year worldwide and it is
closely linked to increased risk of cardiovascular diseases such as stroke and
heart failure. Machine learning methods have shown promising results in
evaluating the risk of developing atrial fibrillation from the
electrocardiogram. We aim to develop and evaluate one such algorithm on a large
CODE dataset collected in Brazil.
Results: The deep neural network model identified patients without indication
of AF in the presented ECG but who will develop AF in the future with an AUC
score of 0.845. From our survival model, we obtain that patients in the
high-risk group (i.e. with the probability of a future AF case being greater
than 0.7) are 50% more likely to develop AF within 40 weeks, while patients
belonging to the minimal-risk group (i.e. with the probability of a future AF
case being less than or equal to 0.1) have more than 85% chance of remaining AF
free up until after seven years.
Conclusion: We developed and validated a model for AF risk prediction. If
applied in clinical practice, the model possesses the potential of providing
valuable and useful information in decision-making and patient management
processes. | [
"Theogene Habineza",
"Antônio H. Ribeiro",
"Daniel Gedon",
"Joachim A. Behar",
"Antonio Luiz P. Ribeiro",
"Thomas B. Schön"
] | 2023-09-28 10:47:40 | http://arxiv.org/abs/2309.16335v1 | http://arxiv.org/pdf/2309.16335v1 | 2309.16335v1 |
DeepPCR: Parallelizing Sequential Operations in Neural Networks | Parallelization techniques have become ubiquitous for accelerating inference
and training of deep neural networks. Despite this, several operations are
still performed in a sequential manner. For instance, the forward and backward
passes are executed layer-by-layer, and the output of diffusion models is
produced by applying a sequence of denoising steps. This sequential approach
results in a computational cost proportional to the number of steps involved,
presenting a potential bottleneck as the number of steps increases. In this
work, we introduce DeepPCR, a novel algorithm which parallelizes typically
sequential operations used in inference and training of neural networks.
DeepPCR is based on interpreting a sequence of $L$ steps as the solution of a
specific system of equations, which we recover using the Parallel Cyclic
Reduction algorithm. This reduces the complexity of computing the sequential
operations from $\mathcal{O}(L)$ to $\mathcal{O}(\log_2L)$, thus yielding a
speedup for large $L$. To verify the theoretical lower complexity of the
algorithm, and to identify regimes for speedup, we test the effectiveness of
DeepPCR in parallelizing the forward and backward pass in multi-layer
perceptrons, and reach speedups of up to $30\times$ for forward and $200\times$
for backward pass. We additionally showcase the flexibility of DeepPCR by
parallelizing training of ResNets with as many as 1024 layers, and generation
in diffusion models, enabling up to $7\times$ faster training and $11\times$
faster generation, respectively, when compared to the sequential approach. | [
"Federico Danieli",
"Miguel Sarabia",
"Xavier Suau",
"Pau Rodríguez",
"Luca Zappella"
] | 2023-09-28 10:15:30 | http://arxiv.org/abs/2309.16318v1 | http://arxiv.org/pdf/2309.16318v1 | 2309.16318v1 |
Astroconformer: The Prospects of Analyzing Stellar Light Curves with Transformer-Based Deep Learning Models | Light curves of stars encapsulate a wealth of information about stellar
oscillations and granulation, thereby offering key insights into the internal
structure and evolutionary state of stars. Conventional asteroseismic
techniques have been largely confined to power spectral analysis, neglecting
the valuable phase information contained within light curves. While recent
machine learning applications in asteroseismology utilizing Convolutional
Neural Networks (CNNs) have successfully inferred stellar attributes from light
curves, they are often limited by the local feature extraction inherent in
convolutional operations. To circumvent these constraints, we present
$\textit{Astroconformer}$, a Transformer-based deep learning framework designed
to capture long-range dependencies in stellar light curves. Our empirical
analysis, which focuses on estimating surface gravity ($\log g$), is grounded
in a carefully curated dataset derived from $\textit{Kepler}$ light curves.
These light curves feature asteroseismic $\log g$ values spanning from 0.2 to
4.4. Our results underscore that, in the regime where the training data is
abundant, $\textit{Astroconformer}$ attains a root-mean-square-error (RMSE) of
0.017 dex around $\log g \approx 3 $. Even in regions where training data are
sparse, the RMSE can reach 0.1 dex. It outperforms not only the K-nearest
neighbor-based model ($\textit{The SWAN}$) but also state-of-the-art CNNs.
Ablation studies confirm that the efficacy of the models in this particular
task is strongly influenced by the size of their receptive fields, with larger
receptive fields correlating with enhanced performance. Moreover, we find that
the attention mechanisms within $\textit{Astroconformer}$ are well-aligned with
the inherent characteristics of stellar oscillations and granulation present in
the light curves. | [
"Jia-Shu Pan",
"Yuan-Sen Ting",
"Jie Yu"
] | 2023-09-28 10:13:23 | http://arxiv.org/abs/2309.16316v1 | http://arxiv.org/pdf/2309.16316v1 | 2309.16316v1 |
A Primer on Bayesian Neural Networks: Review and Debates | Neural networks have achieved remarkable performance across various problem
domains, but their widespread applicability is hindered by inherent limitations
such as overconfidence in predictions, lack of interpretability, and
vulnerability to adversarial attacks. To address these challenges, Bayesian
neural networks (BNNs) have emerged as a compelling extension of conventional
neural networks, integrating uncertainty estimation into their predictive
capabilities.
This comprehensive primer presents a systematic introduction to the
fundamental concepts of neural networks and Bayesian inference, elucidating
their synergistic integration for the development of BNNs. The target audience
comprises statisticians with a potential background in Bayesian methods but
lacking deep learning expertise, as well as machine learners proficient in deep
neural networks but with limited exposure to Bayesian statistics. We provide an
overview of commonly employed priors, examining their impact on model behavior
and performance. Additionally, we delve into the practical considerations
associated with training and inference in BNNs.
Furthermore, we explore advanced topics within the realm of BNN research,
acknowledging the existence of ongoing debates and controversies. By offering
insights into cutting-edge developments, this primer not only equips
researchers and practitioners with a solid foundation in BNNs, but also
illuminates the potential applications of this dynamic field. As a valuable
resource, it fosters an understanding of BNNs and their promising prospects,
facilitating further advancements in the pursuit of knowledge and innovation. | [
"Julyan Arbel",
"Konstantinos Pitas",
"Mariia Vladimirova",
"Vincent Fortuin"
] | 2023-09-28 10:09:15 | http://arxiv.org/abs/2309.16314v1 | http://arxiv.org/pdf/2309.16314v1 | 2309.16314v1 |
3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information | Molecular property prediction offers an effective and efficient approach for
early screening and optimization of drug candidates. Although deep learning
based methods have made notable progress, most existing works still do not
fully utilize 3D spatial information. This can lead to a single molecular
representation representing multiple actual molecules. To address these issues,
we propose a novel 3D structure-based molecular modeling method named 3D-Mol.
In order to accurately represent complete spatial structure, we design a novel
encoder to extract 3D features by deconstructing the molecules into three
geometric graphs. In addition, we use 20M unlabeled data to pretrain our model
by contrastive learning. We consider conformations with the same topological
structure as positive pairs and the opposites as negative pairs, while the
weight is determined by the dissimilarity between the conformations. We compare
3D-Mol with various state-of-the-art (SOTA) baselines on 7 benchmarks and
demonstrate our outstanding performance in 5 benchmarks. | [
"Taojie Kuang",
"Yiming Ren",
"Zhixiang Ren"
] | 2023-09-28 10:05:37 | http://arxiv.org/abs/2309.17366v1 | http://arxiv.org/pdf/2309.17366v1 | 2309.17366v1 |
CasIL: Cognizing and Imitating Skills via a Dual Cognition-Action Architecture | Enabling robots to effectively imitate expert skills in longhorizon tasks
such as locomotion, manipulation, and more, poses a long-standing challenge.
Existing imitation learning (IL) approaches for robots still grapple with
sub-optimal performance in complex tasks. In this paper, we consider how this
challenge can be addressed within the human cognitive priors. Heuristically, we
extend the usual notion of action to a dual Cognition (high-level)-Action
(low-level) architecture by introducing intuitive human cognitive priors, and
propose a novel skill IL framework through human-robot interaction, called
Cognition-Action-based Skill Imitation Learning (CasIL), for the robotic agent
to effectively cognize and imitate the critical skills from raw visual
demonstrations. CasIL enables both cognition and action imitation, while
high-level skill cognition explicitly guides low-level primitive actions,
providing robustness and reliability to the entire skill IL process. We
evaluated our method on MuJoCo and RLBench benchmarks, as well as on the
obstacle avoidance and point-goal navigation tasks for quadrupedal robot
locomotion. Experimental results show that our CasIL consistently achieves
competitive and robust skill imitation capability compared to other
counterparts in a variety of long-horizon robotic tasks. | [
"Zixuan Chen",
"Ze Ji",
"Shuyang Liu",
"Jing Huo",
"Yiyu Chen",
"Yang Gao"
] | 2023-09-28 09:53:05 | http://arxiv.org/abs/2309.16299v1 | http://arxiv.org/pdf/2309.16299v1 | 2309.16299v1 |
Efficiency Separation between RL Methods: Model-Free, Model-Based and Goal-Conditioned | We prove a fundamental limitation on the efficiency of a wide class of
Reinforcement Learning (RL) algorithms. This limitation applies to model-free
RL methods as well as a broad range of model-based methods, such as planning
with tree search.
Under an abstract definition of this class, we provide a family of RL
problems for which these methods suffer a lower bound exponential in the
horizon for their interactions with the environment to find an optimal
behavior. However, there exists a method, not tailored to this specific family
of problems, which can efficiently solve the problems in the family.
In contrast, our limitation does not apply to several types of methods
proposed in the literature, for instance, goal-conditioned methods or other
algorithms that construct an inverse dynamics model. | [
"Brieuc Pinon",
"Raphaël Jungers",
"Jean-Charles Delvenne"
] | 2023-09-28 09:38:27 | http://arxiv.org/abs/2309.16291v1 | http://arxiv.org/pdf/2309.16291v1 | 2309.16291v1 |
LawBench: Benchmarking Legal Knowledge of Large Language Models | Large language models (LLMs) have demonstrated strong capabilities in various
aspects. However, when applying them to the highly specialized, safe-critical
legal domain, it is unclear how much legal knowledge they possess and whether
they can reliably perform legal-related tasks. To address this gap, we propose
a comprehensive evaluation benchmark LawBench. LawBench has been meticulously
crafted to have precise assessment of the LLMs' legal capabilities from three
cognitive levels: (1) Legal knowledge memorization: whether LLMs can memorize
needed legal concepts, articles and facts; (2) Legal knowledge understanding:
whether LLMs can comprehend entities, events and relationships within legal
text; (3) Legal knowledge applying: whether LLMs can properly utilize their
legal knowledge and make necessary reasoning steps to solve realistic legal
tasks. LawBench contains 20 diverse tasks covering 5 task types: single-label
classification (SLC), multi-label classification (MLC), regression, extraction
and generation. We perform extensive evaluations of 51 LLMs on LawBench,
including 20 multilingual LLMs, 22 Chinese-oriented LLMs and 9 legal specific
LLMs. The results show that GPT-4 remains the best-performing LLM in the legal
domain, surpassing the others by a significant margin. While fine-tuning LLMs
on legal specific text brings certain improvements, we are still a long way
from obtaining usable and reliable LLMs in legal tasks. All data, model
predictions and evaluation code are released in
https://github.com/open-compass/LawBench/. We hope this benchmark provides
in-depth understanding of the LLMs' domain-specified capabilities and speed up
the development of LLMs in the legal domain. | [
"Zhiwei Fei",
"Xiaoyu Shen",
"Dawei Zhu",
"Fengzhe Zhou",
"Zhuo Han",
"Songyang Zhang",
"Kai Chen",
"Zongwen Shen",
"Jidong Ge"
] | 2023-09-28 09:35:59 | http://arxiv.org/abs/2309.16289v1 | http://arxiv.org/pdf/2309.16289v1 | 2309.16289v1 |
High Throughput Training of Deep Surrogates from Large Ensemble Runs | Recent years have seen a surge in deep learning approaches to accelerate
numerical solvers, which provide faithful but computationally intensive
simulations of the physical world. These deep surrogates are generally trained
in a supervised manner from limited amounts of data slowly generated by the
same solver they intend to accelerate. We propose an open-source framework that
enables the online training of these models from a large ensemble run of
simulations. It leverages multiple levels of parallelism to generate rich
datasets. The framework avoids I/O bottlenecks and storage issues by directly
streaming the generated data. A training reservoir mitigates the inherent bias
of streaming while maximizing GPU throughput. Experiment on training a fully
connected network as a surrogate for the heat equation shows the proposed
approach enables training on 8TB of data in 2 hours with an accuracy improved
by 47% and a batch throughput multiplied by 13 compared to a traditional
offline procedure. | [
"Lucas Meyer",
"Marc Schouler",
"Robert Alexander Caulk",
"Alejandro Ribés",
"Bruno Raffin"
] | 2023-09-28 09:34:52 | http://arxiv.org/abs/2309.16743v1 | http://arxiv.org/pdf/2309.16743v1 | 2309.16743v1 |
Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning | Federated learning is an important privacy-preserving multi-party learning
paradigm, involving collaborative learning with others and local updating on
private data. Model heterogeneity and catastrophic forgetting are two crucial
challenges, which greatly limit the applicability and generalizability. This
paper presents a novel FCCL+, federated correlation and similarity learning
with non-target distillation, facilitating the both intra-domain
discriminability and inter-domain generalization. For heterogeneity issue, we
leverage irrelevant unlabeled public data for communication between the
heterogeneous participants. We construct cross-correlation matrix and align
instance similarity distribution on both logits and feature levels, which
effectively overcomes the communication barrier and improves the generalizable
ability. For catastrophic forgetting in local updating stage, FCCL+ introduces
Federated Non Target Distillation, which retains inter-domain knowledge while
avoiding the optimization conflict issue, fulling distilling privileged
inter-domain information through depicting posterior classes relation.
Considering that there is no standard benchmark for evaluating existing
heterogeneous federated learning under the same setting, we present a
comprehensive benchmark with extensive representative methods under four domain
shift scenarios, supporting both heterogeneous and homogeneous federated
settings. Empirical results demonstrate the superiority of our method and the
efficiency of modules on various scenarios. | [
"Wenke Huang",
"Mang Ye",
"Zekun Shi",
"Bo Du"
] | 2023-09-28 09:32:27 | http://arxiv.org/abs/2309.16286v1 | http://arxiv.org/pdf/2309.16286v1 | 2309.16286v1 |
A framework for paired-sample hypothesis testing for high-dimensional data | The standard paired-sample testing approach in the multidimensional setting
applies multiple univariate tests on the individual features, followed by
p-value adjustments. Such an approach suffers when the data carry numerous
features. A number of studies have shown that classification accuracy can be
seen as a proxy for two-sample testing. However, neither theoretical
foundations nor practical recipes have been proposed so far on how this
strategy could be extended to multidimensional paired-sample testing. In this
work, we put forward the idea that scoring functions can be produced by the
decision rules defined by the perpendicular bisecting hyperplanes of the line
segments connecting each pair of instances. Then, the optimal scoring function
can be obtained by the pseudomedian of those rules, which we estimate by
extending naturally the Hodges-Lehmann estimator. We accordingly propose a
framework of a two-step testing procedure. First, we estimate the bisecting
hyperplanes for each pair of instances and an aggregated rule derived through
the Hodges-Lehmann estimator. The paired samples are scored by this aggregated
rule to produce a unidimensional representation. Second, we perform a Wilcoxon
signed-rank test on the obtained representation. Our experiments indicate that
our approach has substantial performance gains in testing accuracy compared to
the traditional multivariate and multiple testing, while at the same time
estimates each feature's contribution to the final result. | [
"Ioannis Bargiotas",
"Argyris Kalogeratos",
"Nicolas Vayatis"
] | 2023-09-28 09:17:11 | http://arxiv.org/abs/2309.16274v1 | http://arxiv.org/pdf/2309.16274v1 | 2309.16274v1 |
Hierarchical Network Data Analytics Framework for B5G Network Automation: Design and Implementation | 5G introduced modularized network functions (NFs) to support emerging
services in a more flexible and elastic manner. To mitigate the complexity in
such modularized NF management, automated network operation and management are
indispensable, and thus the 3rd generation partnership project (3GPP) has
introduced a network data analytics function (NWDAF). However, a conventional
NWDAF needs to conduct both inference and training tasks, and thus it is
difficult to provide the analytics results to NFs in a timely manner for an
increased number of analytics requests. In this article, we propose a
hierarchical network data analytics framework (H-NDAF) where inference tasks
are distributed to multiple leaf NWDAFs and training tasks are conducted at the
root NWDAF. Extensive simulation results using open-source software (i.e.,
free5GC) demonstrate that H-NDAF can provide sufficiently accurate analytics
and faster analytics provision time compared to the conventional NWDAF. | [
"Youbin Jeon",
"Sangheon Pack"
] | 2023-09-28 09:04:58 | http://arxiv.org/abs/2309.16269v1 | http://arxiv.org/pdf/2309.16269v1 | 2309.16269v1 |
Supervised Learning Models for Early Detection of Albuminuria Risk in Type-2 Diabetes Mellitus Patients | Diabetes, especially T2DM, continues to be a significant health problem. One
of the major concerns associated with diabetes is the development of its
complications. Diabetic nephropathy, one of the chronic complication of
diabetes, adversely affects the kidneys, leading to kidney damage. Diagnosing
diabetic nephropathy involves considering various criteria, one of which is the
presence of a pathologically significant quantity of albumin in urine, known as
albuminuria. Thus, early prediction of albuminuria in diabetic patients holds
the potential for timely preventive measures. This study aimed to develop a
supervised learning model to predict the risk of developing albuminuria in T2DM
patients. The selected supervised learning algorithms included Na\"ive Bayes,
Support Vector Machine (SVM), decision tree, random forest, AdaBoost, XGBoost,
and Multi-Layer Perceptron (MLP). Our private dataset, comprising 184 entries
of diabetes complications risk factors, was used to train the algorithms. It
consisted of 10 attributes as features and 1 attribute as the target
(albuminuria). Upon conducting the experiments, the MLP demonstrated superior
performance compared to the other algorithms. It achieved accuracy and f1-score
values as high as 0.74 and 0.75, respectively, making it suitable for screening
purposes in predicting albuminuria in T2DM. Nonetheless, further studies are
warranted to enhance the model's performance. | [
"Arief Purnama Muharram",
"Dicky Levenus Tahapary",
"Yeni Dwi Lestari",
"Randy Sarayar",
"Valerie Josephine Dirjayanto"
] | 2023-09-28 08:41:12 | http://arxiv.org/abs/2309.16742v3 | http://arxiv.org/pdf/2309.16742v3 | 2309.16742v3 |
Context-Based Tweet Engagement Prediction | Twitter is currently one of the biggest social media platforms. Its users may
share, read, and engage with short posts called tweets. For the ACM Recommender
Systems Conference 2020, Twitter published a dataset around 70 GB in size for
the annual RecSys Challenge. In 2020, the RecSys Challenge invited
participating teams to create models that would predict engagement likelihoods
for given user-tweet combinations. The submitted models predicting like, reply,
retweet, and quote engagements were evaluated based on two metrics: area under
the precision-recall curve (PRAUC) and relative cross-entropy (RCE).
In this diploma thesis, we used the RecSys 2020 Challenge dataset and
evaluation procedure to investigate how well context alone may be used to
predict tweet engagement likelihood. In doing so, we employed the Spark engine
on TU Wien's Little Big Data Cluster to create scalable data preprocessing,
feature engineering, feature selection, and machine learning pipelines. We
manually created just under 200 additional features to describe tweet context.
The results indicate that features describing users' prior engagement history
and the popularity of hashtags and links in the tweet were the most
informative. We also found that factors such as the prediction algorithm,
training dataset size, training dataset sampling method, and feature selection
significantly affect the results. After comparing the best results of our
context-only prediction models with content-only models and with models
developed by the Challenge winners, we identified that the context-based models
underperformed in terms of the RCE score. This work thus concludes by situating
this discrepancy and proposing potential improvements to our implementation,
which is shared in a public git repository. | [
"Jovan Jeromela"
] | 2023-09-28 08:36:57 | http://arxiv.org/abs/2310.03147v1 | http://arxiv.org/pdf/2310.03147v1 | 2310.03147v1 |
Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints | The increasing capabilities of large language models (LLMs) raise
opportunities for artificial general intelligence but concurrently amplify
safety concerns, such as potential misuse of AI systems, necessitating
effective AI alignment. Reinforcement Learning from Human Feedback (RLHF) has
emerged as a promising pathway towards AI alignment but brings forth challenges
due to its complexity and dependence on a separate reward model. Direct
Preference Optimization (DPO) has been proposed as an alternative, and it
remains equivalent to RLHF under the reverse KL regularization constraint. This
paper presents $f$-DPO, a generalized approach to DPO by incorporating diverse
divergence constraints. We show that under certain $f$-divergences, including
Jensen-Shannon divergence, forward KL divergences and $\alpha$-divergences, the
complex relationship between the reward and optimal policy can also be
simplified by addressing the Karush-Kuhn-Tucker conditions. This eliminates the
need for estimating the normalizing constant in the Bradley-Terry model and
enables a tractable mapping between the reward function and the optimal policy.
Our approach optimizes LLMs to align with human preferences in a more efficient
and supervised manner under a broad set of divergence constraints. Empirically,
adopting these divergences ensures a balance between alignment performance and
generation diversity. Importantly, $f$-DPO outperforms PPO-based methods in
divergence efficiency, and divergence constraints directly influence expected
calibration error (ECE). | [
"Chaoqi Wang",
"Yibo Jiang",
"Chenghao Yang",
"Han Liu",
"Yuxin Chen"
] | 2023-09-28 08:29:44 | http://arxiv.org/abs/2309.16240v1 | http://arxiv.org/pdf/2309.16240v1 | 2309.16240v1 |
Language models in molecular discovery | The success of language models, especially transformer-based architectures,
has trickled into other domains giving rise to "scientific language models"
that operate on small molecules, proteins or polymers. In chemistry, language
models contribute to accelerating the molecule discovery cycle as evidenced by
promising recent findings in early-stage drug discovery. Here, we review the
role of language models in molecular discovery, underlining their strength in
de novo drug design, property prediction and reaction chemistry. We highlight
valuable open-source software assets thus lowering the entry barrier to the
field of scientific language modeling. Last, we sketch a vision for future
molecular design that combines a chatbot interface with access to computational
chemistry tools. Our contribution serves as a valuable resource for
researchers, chemists, and AI enthusiasts interested in understanding how
language models can and will be used to accelerate chemical discovery. | [
"Nikita Janakarajan",
"Tim Erdmann",
"Sarath Swaminathan",
"Teodoro Laino",
"Jannis Born"
] | 2023-09-28 08:19:54 | http://arxiv.org/abs/2309.16235v1 | http://arxiv.org/pdf/2309.16235v1 | 2309.16235v1 |
Multi-Modal Financial Time-Series Retrieval Through Latent Space Projections | Financial firms commonly process and store billions of time-series data,
generated continuously and at a high frequency. To support efficient data
storage and retrieval, specialized time-series databases and systems have
emerged. These databases support indexing and querying of time-series by a
constrained Structured Query Language(SQL)-like format to enable queries like
"Stocks with monthly price returns greater than 5%", and expressed in rigid
formats. However, such queries do not capture the intrinsic complexity of high
dimensional time-series data, which can often be better described by images or
language (e.g., "A stock in low volatility regime"). Moreover, the required
storage, computational time, and retrieval complexity to search in the
time-series space are often non-trivial. In this paper, we propose and
demonstrate a framework to store multi-modal data for financial time-series in
a lower-dimensional latent space using deep encoders, such that the latent
space projections capture not only the time series trends but also other
desirable information or properties of the financial time-series data (such as
price volatility). Moreover, our approach allows user-friendly query
interfaces, enabling natural language text or sketches of time-series, for
which we have developed intuitive interfaces. We demonstrate the advantages of
our method in terms of computational efficiency and accuracy on real historical
data as well as synthetic data, and highlight the utility of latent-space
projections in the storage and retrieval of financial time-series data with
intuitive query modalities. | [
"Tom Bamford",
"Andrea Coletta",
"Elizabeth Fons",
"Sriram Gopalakrishnan",
"Svitlana Vyetrenko",
"Tucker Balch",
"Manuela Veloso"
] | 2023-09-28 08:08:08 | http://arxiv.org/abs/2309.16741v1 | http://arxiv.org/pdf/2309.16741v1 | 2309.16741v1 |
GInX-Eval: Towards In-Distribution Evaluation of Graph Neural Network Explanations | Diverse explainability methods of graph neural networks (GNN) have recently
been developed to highlight the edges and nodes in the graph that contribute
the most to the model predictions. However, it is not clear yet how to evaluate
the correctness of those explanations, whether it is from a human or a model
perspective. One unaddressed bottleneck in the current evaluation procedure is
the problem of out-of-distribution explanations, whose distribution differs
from those of the training data. This important issue affects existing
evaluation metrics such as the popular faithfulness or fidelity score. In this
paper, we show the limitations of faithfulness metrics. We propose GInX-Eval
(Graph In-distribution eXplanation Evaluation), an evaluation procedure of
graph explanations that overcomes the pitfalls of faithfulness and offers new
insights on explainability methods. Using a retraining strategy, the GInX score
measures how informative removed edges are for the model and the EdgeRank score
evaluates if explanatory edges are correctly ordered by their importance.
GInX-Eval verifies if ground-truth explanations are instructive to the GNN
model. In addition, it shows that many popular methods, including
gradient-based methods, produce explanations that are not better than a random
designation of edges as important subgraphs, challenging the findings of
current works in the area. Results with GInX-Eval are consistent across
multiple datasets and align with human evaluation. | [
"Kenza Amara",
"Mennatallah El-Assady",
"Rex Ying"
] | 2023-09-28 07:56:10 | http://arxiv.org/abs/2309.16223v1 | http://arxiv.org/pdf/2309.16223v1 | 2309.16223v1 |
Unmasking the Chameleons: A Benchmark for Out-of-Distribution Detection in Medical Tabular Data | Despite their success, Machine Learning (ML) models do not generalize
effectively to data not originating from the training distribution. To reliably
employ ML models in real-world healthcare systems and avoid inaccurate
predictions on out-of-distribution (OOD) data, it is crucial to detect OOD
samples. Numerous OOD detection approaches have been suggested in other fields
- especially in computer vision - but it remains unclear whether the challenge
is resolved when dealing with medical tabular data. To answer this pressing
need, we propose an extensive reproducible benchmark to compare different
methods across a suite of tests including both near and far OODs. Our benchmark
leverages the latest versions of eICU and MIMIC-IV, two public datasets
encompassing tens of thousands of ICU patients in several hospitals. We
consider a wide array of density-based methods and SOTA post-hoc detectors
across diverse predictive architectures, including MLP, ResNet, and
Transformer. Our findings show that i) the problem appears to be solved for
far-OODs, but remains open for near-OODs; ii) post-hoc methods alone perform
poorly, but improve substantially when coupled with distance-based mechanisms;
iii) the transformer architecture is far less overconfident compared to MLP and
ResNet. | [
"Mohammad Azizmalayeri",
"Ameen Abu-Hanna",
"Giovanni Ciná"
] | 2023-09-28 07:52:01 | http://arxiv.org/abs/2309.16220v1 | http://arxiv.org/pdf/2309.16220v1 | 2309.16220v1 |
Abdominal multi-organ segmentation in CT using Swinunter | Abdominal multi-organ segmentation in computed tomography (CT) is crucial for
many clinical applications including disease detection and treatment planning.
Deep learning methods have shown unprecedented performance in this perspective.
However, it is still quite challenging to accurately segment different organs
utilizing a single network due to the vague boundaries of organs, the complex
background, and the substantially different organ size scales. In this work we
used make transformer-based model for training. It was found through previous
years' competitions that basically all of the top 5 methods used CNN-based
methods, which is likely due to the lack of data volume that prevents
transformer-based methods from taking full advantage. The thousands of samples
in this competition may enable the transformer-based model to have more
excellent results. The results on the public validation set also show that the
transformer-based model can achieve an acceptable result and inference time. | [
"Mingjin Chen",
"Yongkang He",
"Yongyi Lu"
] | 2023-09-28 07:32:22 | http://arxiv.org/abs/2309.16210v1 | http://arxiv.org/pdf/2309.16210v1 | 2309.16210v1 |
Max-Sliced Mutual Information | Quantifying the dependence between high-dimensional random variables is
central to statistical learning and inference. Two classical methods are
canonical correlation analysis (CCA), which identifies maximally correlated
projected versions of the original variables, and Shannon's mutual information,
which is a universal dependence measure that also captures high-order
dependencies. However, CCA only accounts for linear dependence, which may be
insufficient for certain applications, while mutual information is often
infeasible to compute/estimate in high dimensions. This work proposes a middle
ground in the form of a scalable information-theoretic generalization of CCA,
termed max-sliced mutual information (mSMI). mSMI equals the maximal mutual
information between low-dimensional projections of the high-dimensional
variables, which reduces back to CCA in the Gaussian case. It enjoys the best
of both worlds: capturing intricate dependencies in the data while being
amenable to fast computation and scalable estimation from samples. We show that
mSMI retains favorable structural properties of Shannon's mutual information,
like variational forms and identification of independence. We then study
statistical estimation of mSMI, propose an efficiently computable neural
estimator, and couple it with formal non-asymptotic error bounds. We present
experiments that demonstrate the utility of mSMI for several tasks,
encompassing independence testing, multi-view representation learning,
algorithmic fairness, and generative modeling. We observe that mSMI
consistently outperforms competing methods with little-to-no computational
overhead. | [
"Dor Tsur",
"Ziv Goldfeld",
"Kristjan Greenewald"
] | 2023-09-28 06:49:25 | http://arxiv.org/abs/2309.16200v1 | http://arxiv.org/pdf/2309.16200v1 | 2309.16200v1 |
Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities | Large language models (LLMs), which have shown remarkable capabilities, are
revolutionizing AI development and potentially shaping our future. However,
given their multimodality, the status quo cloud-based deployment faces some
critical challenges: 1) long response time; 2) high bandwidth costs; and 3) the
violation of data privacy. 6G mobile edge computing (MEC) systems may resolve
these pressing issues. In this article, we explore the potential of deploying
LLMs at the 6G edge. We start by introducing killer applications powered by
multimodal LLMs, including robotics and healthcare, to highlight the need for
deploying LLMs in the vicinity of end users. Then, we identify the critical
challenges for LLM deployment at the edge and envision the 6G MEC architecture
for LLMs. Furthermore, we delve into two design aspects, i.e., edge training
and edge inference for LLMs. In both aspects, considering the inherent resource
limitations at the edge, we discuss various cutting-edge techniques, including
split learning/inference, parameter-efficient fine-tuning, quantization, and
parameter-sharing inference, to facilitate the efficient deployment of LLMs.
This article serves as a position paper for thoroughly identifying the
motivation, challenges, and pathway for empowering LLMs at the 6G edge. | [
"Zheng Lin",
"Guanqiao Qu",
"Qiyuan Chen",
"Xianhao Chen",
"Zhe Chen",
"Kaibin Huang"
] | 2023-09-28 06:22:59 | http://arxiv.org/abs/2309.16739v1 | http://arxiv.org/pdf/2309.16739v1 | 2309.16739v1 |
Stackelberg Batch Policy Learning | Batch reinforcement learning (RL) defines the task of learning from a fixed
batch of data lacking exhaustive exploration. Worst-case optimality algorithms,
which calibrate a value-function model class from logged experience and perform
some type of pessimistic evaluation under the learned model, have emerged as a
promising paradigm for batch RL. However, contemporary works on this stream
have commonly overlooked the hierarchical decision-making structure hidden in
the optimization landscape. In this paper, we adopt a game-theoretical
viewpoint and model the policy learning diagram as a two-player general-sum
game with a leader-follower structure. We propose a novel stochastic
gradient-based learning algorithm: StackelbergLearner, in which the leader
player updates according to the total derivative of its objective instead of
the usual individual gradient, and the follower player makes individual updates
and ensures transition-consistent pessimistic reasoning. The derived learning
dynamic naturally lends StackelbergLearner to a game-theoretic interpretation
and provides a convergence guarantee to differentiable Stackelberg equilibria.
From a theoretical standpoint, we provide instance-dependent regret bounds with
general function approximation, which shows that our algorithm can learn a
best-effort policy that is able to compete against any comparator policy that
is covered by batch data. Notably, our theoretical regret guarantees only
require realizability without any data coverage and strong function
approximation conditions, e.g., Bellman closedness, which is in contrast to
prior works lacking such guarantees. Through comprehensive experiments, we find
that our algorithm consistently performs as well or better as compared to
state-of-the-art methods in batch RL benchmark and real-world datasets. | [
"Wenzhuo Zhou",
"Annie Qu"
] | 2023-09-28 06:18:34 | http://arxiv.org/abs/2309.16188v2 | http://arxiv.org/pdf/2309.16188v2 | 2309.16188v2 |
Systematic Sampling and Validation of Machine Learning-Parameterizations in Climate Models | Progress in hybrid physics-machine learning (ML) climate simulations has been
limited by the difficulty of obtaining performant coupled (i.e. online)
simulations. While evaluating hundreds of ML parameterizations of subgrid
closures (here of convection and radiation) offline is straightforward, online
evaluation at the same scale is technically challenging. Our software
automation achieves an order-of-magnitude larger sampling of online modeling
errors than has previously been examined. Using this, we evaluate the hybrid
climate model performance and define strategies to improve it. We show that
model online performance improves when incorporating memory, a relative
humidity input feature transformation, and additional input variables. We also
reveal substantial variation in online error and inconsistencies between
offline vs. online error statistics. The implication is that hundreds of
candidate ML models should be evaluated online to detect the effects of
parameterization design choices. This is considerably more sampling than tends
to be reported in the current literature. | [
"Jerry Lin",
"Sungduk Yu",
"Tom Beucler",
"Pierre Gentine",
"David Walling",
"Mike Pritchard"
] | 2023-09-28 05:34:29 | http://arxiv.org/abs/2309.16177v1 | http://arxiv.org/pdf/2309.16177v1 | 2309.16177v1 |
Using Weak Supervision and Data Augmentation in Question Answering | The onset of the COVID-19 pandemic accentuated the need for access to
biomedical literature to answer timely and disease-specific questions. During
the early days of the pandemic, one of the biggest challenges we faced was the
lack of peer-reviewed biomedical articles on COVID-19 that could be used to
train machine learning models for question answering (QA). In this paper, we
explore the roles weak supervision and data augmentation play in training deep
neural network QA models. First, we investigate whether labels generated
automatically from the structured abstracts of scholarly papers using an
information retrieval algorithm, BM25, provide a weak supervision signal to
train an extractive QA model. We also curate new QA pairs using information
retrieval techniques, guided by the clinicaltrials.gov schema and the
structured abstracts of articles, in the absence of annotated data from
biomedical domain experts. Furthermore, we explore augmenting the training data
of a deep neural network model with linguistic features from external sources
such as lexical databases to account for variations in word morphology and
meaning. To better utilize our training data, we apply curriculum learning to
domain adaptation, fine-tuning our QA model in stages based on characteristics
of the QA pairs. We evaluate our methods in the context of QA models at the
core of a system to answer questions about COVID-19. | [
"Chumki Basu",
"Himanshu Garg",
"Allen McIntosh",
"Sezai Sablak",
"John R. Wullert II"
] | 2023-09-28 05:16:51 | http://arxiv.org/abs/2309.16175v1 | http://arxiv.org/pdf/2309.16175v1 | 2309.16175v1 |
Distill to Delete: Unlearning in Graph Networks with Knowledge Distillation | Graph unlearning has emerged as a pivotal method to delete information from a
pre-trained graph neural network (GNN). One may delete nodes, a class of nodes,
edges, or a class of edges. An unlearning method enables the GNN model to
comply with data protection regulations (i.e., the right to be forgotten),
adapt to evolving data distributions, and reduce the GPU-hours carbon footprint
by avoiding repetitive retraining. Existing partitioning and aggregation-based
methods have limitations due to their poor handling of local graph dependencies
and additional overhead costs. More recently, GNNDelete offered a
model-agnostic approach that alleviates some of these issues. Our work takes a
novel approach to address these challenges in graph unlearning through
knowledge distillation, as it distills to delete in GNN (D2DGN). It is a
model-agnostic distillation framework where the complete graph knowledge is
divided and marked for retention and deletion. It performs distillation with
response-based soft targets and feature-based node embedding while minimizing
KL divergence. The unlearned model effectively removes the influence of deleted
graph elements while preserving knowledge about the retained graph elements.
D2DGN surpasses the performance of existing methods when evaluated on various
real-world graph datasets by up to $43.1\%$ (AUC) in edge and node unlearning
tasks. Other notable advantages include better efficiency, better performance
in removing target elements, preservation of performance for the retained
elements, and zero overhead costs. Notably, our D2DGN surpasses the
state-of-the-art GNNDelete in AUC by $2.4\%$, improves membership inference
ratio by $+1.3$, requires $10.2\times10^6$ fewer FLOPs per forward pass and up
to $\mathbf{3.2}\times$ faster. | [
"Yash Sinha",
"Murari Mandal",
"Mohan Kankanhalli"
] | 2023-09-28 05:09:14 | http://arxiv.org/abs/2309.16173v1 | http://arxiv.org/pdf/2309.16173v1 | 2309.16173v1 |
The Trickle-down Impact of Reward (In-)consistency on RLHF | Standard practice within Reinforcement Learning from Human Feedback (RLHF)
involves optimizing against a Reward Model (RM), which itself is trained to
reflect human preferences for desirable generations. A notable subject that is
understudied is the (in-)consistency of RMs -- whether they can recognize the
semantic changes to different prompts and appropriately adapt their reward
assignments -- and their impact on the downstream RLHF model.
In this paper, we visit a series of research questions relevant to RM
inconsistency: (1) How can we measure the consistency of reward models? (2) How
consistent are the existing RMs and how can we improve them? (3) In what ways
does reward inconsistency influence the chatbots resulting from the RLHF model
training?
We propose Contrast Instructions -- a benchmarking strategy for the
consistency of RM. Each example in Contrast Instructions features a pair of
lexically similar instructions with different ground truth responses. A
consistent RM is expected to rank the corresponding instruction and response
higher than other combinations. We observe that current RMs trained with the
standard ranking objective fail miserably on Contrast Instructions compared to
average humans. To show that RM consistency can be improved efficiently without
using extra training budget, we propose two techniques ConvexDA and
RewardFusion, which enhance reward consistency through extrapolation during the
RM training and inference stage, respectively. We show that RLHF models trained
with a more consistent RM yield more useful responses, suggesting that reward
inconsistency exhibits a trickle-down effect on the downstream RLHF process. | [
"Lingfeng Shen",
"Sihao Chen",
"Linfeng Song",
"Lifeng Jin",
"Baolin Peng",
"Haitao Mi",
"Daniel Khashabi",
"Dong Yu"
] | 2023-09-28 04:05:13 | http://arxiv.org/abs/2309.16155v1 | http://arxiv.org/pdf/2309.16155v1 | 2309.16155v1 |
Generative Semi-supervised Learning with Meta-Optimized Synthetic Samples | Semi-supervised learning (SSL) is a promising approach for training deep
classification models using labeled and unlabeled datasets. However, existing
SSL methods rely on a large unlabeled dataset, which may not always be
available in many real-world applications due to legal constraints (e.g.,
GDPR). In this paper, we investigate the research question: Can we train SSL
models without real unlabeled datasets? Instead of using real unlabeled
datasets, we propose an SSL method using synthetic datasets generated from
generative foundation models trained on datasets containing millions of samples
in diverse domains (e.g., ImageNet). Our main concepts are identifying
synthetic samples that emulate unlabeled samples from generative foundation
models and training classifiers using these synthetic samples. To achieve this,
our method is formulated as an alternating optimization problem: (i)
meta-learning of generative foundation models and (ii) SSL of classifiers using
real labeled and synthetic unlabeled samples. For (i), we propose a
meta-learning objective that optimizes latent variables to generate samples
that resemble real labeled samples and minimize the validation loss. For (ii),
we propose a simple unsupervised loss function that regularizes the feature
extractors of classifiers to maximize the performance improvement obtained from
synthetic samples. We confirm that our method outperforms baselines using
generative foundation models on SSL. We also demonstrate that our methods
outperform SSL using real unlabeled datasets in scenarios with extremely small
amounts of labeled datasets. This suggests that synthetic samples have the
potential to provide improvement gains more efficiently than real unlabeled
data. | [
"Shin'ya Yamaguchi"
] | 2023-09-28 03:47:26 | http://arxiv.org/abs/2309.16143v1 | http://arxiv.org/pdf/2309.16143v1 | 2309.16143v1 |
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling | Training high-quality instance segmentation models requires an abundance of
labeled images with instance masks and classifications, which is often
expensive to procure. Active learning addresses this challenge by striving for
optimum performance with minimal labeling cost by selecting the most
informative and representative images for labeling. Despite its potential,
active learning has been less explored in instance segmentation compared to
other tasks like image classification, which require less labeling. In this
study, we propose a post-hoc active learning algorithm that integrates
uncertainty-based sampling with diversity-based sampling. Our proposed
algorithm is not only simple and easy to implement, but it also delivers
superior performance on various datasets. Its practical application is
demonstrated on a real-world overhead imagery dataset, where it increases the
labeling efficiency fivefold. | [
"Ke Yu",
"Stephen Albro",
"Giulia DeSalvo",
"Suraj Kothawade",
"Abdullah Rashwan",
"Sasan Tavakkol",
"Kayhan Batmanghelich",
"Xiaoqi Yin"
] | 2023-09-28 03:40:30 | http://arxiv.org/abs/2309.16139v1 | http://arxiv.org/pdf/2309.16139v1 | 2309.16139v1 |
A Spectral Approach for Learning Spatiotemporal Neural Differential Equations | Rapidly developing machine learning methods has stimulated research interest
in computationally reconstructing differential equations (DEs) from
observational data which may provide additional insight into underlying
causative mechanisms. In this paper, we propose a novel neural-ODE based method
that uses spectral expansions in space to learn spatiotemporal DEs. The major
advantage of our spectral neural DE learning approach is that it does not rely
on spatial discretization, thus allowing the target spatiotemporal equations to
contain long range, nonlocal spatial interactions that act on unbounded spatial
domains. Our spectral approach is shown to be as accurate as some of the latest
machine learning approaches for learning PDEs operating on bounded domains. By
developing a spectral framework for learning both PDEs and integro-differential
equations, we extend machine learning methods to apply to unbounded DEs and a
larger class of problems. | [
"Mingtao Xia",
"Xiangting Li",
"Qijing Shen",
"Tom Chou"
] | 2023-09-28 03:22:49 | http://arxiv.org/abs/2309.16131v1 | http://arxiv.org/pdf/2309.16131v1 | 2309.16131v1 |
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers | We propose a memory-efficient finetuning algorithm for large language models
(LLMs) that supports finetuning LLMs with 65B parameters in 3-bit or 4-bit
precision on as little as one 48GB GPU. Our method, modular low-rank adaptation
(ModuLoRA), integrates any user-specified weight quantizer with finetuning via
low-rank adapters (LoRAs). Our approach relies on a simple
quantization-agnostic backward pass that adaptively materializes low-precision
LLM weights from a custom black-box quantization module. This approach enables
finetuning 3-bit LLMs for the first time--leveraging state-of-the-art 3-bit
OPTQ quantization often outperforms finetuning that relies on less
sophisticated 4-bit and 8-bit methods. In our experiments, ModuLoRA attains
competitive performance on text classification, natural language infernece, and
instruction following tasks using significantly less memory than existing
approaches, and we also surpass the state-of-the-art ROUGE score on a popular
summarization task. We release ModuLoRA together with a series of low-precision
models--including the first family of 3-bit instruction following Alpaca
LLMs--as part of LLMTOOLS, a user-friendly library for quantizing, running, and
finetuning LLMs on consumer GPUs. | [
"Junjie Yin",
"Jiahao Dong",
"Yingheng Wang",
"Christopher De Sa",
"Volodymyr Kuleshov"
] | 2023-09-28 02:55:01 | http://arxiv.org/abs/2309.16119v1 | http://arxiv.org/pdf/2309.16119v1 | 2309.16119v1 |
D$^3$Fields: Dynamic 3D Descriptor Fields for Zero-Shot Generalizable Robotic Manipulation | Scene representation has been a crucial design choice in robotic manipulation
systems. An ideal representation should be 3D, dynamic, and semantic to meet
the demands of diverse manipulation tasks. However, previous works often lack
all three properties simultaneously. In this work, we introduce D$^3$Fields -
dynamic 3D descriptor fields. These fields capture the dynamics of the
underlying 3D environment and encode both semantic features and instance masks.
Specifically, we project arbitrary 3D points in the workspace onto multi-view
2D visual observations and interpolate features derived from foundational
models. The resulting fused descriptor fields allow for flexible goal
specifications using 2D images with varied contexts, styles, and instances. To
evaluate the effectiveness of these descriptor fields, we apply our
representation to a wide range of robotic manipulation tasks in a zero-shot
manner. Through extensive evaluation in both real-world scenarios and
simulations, we demonstrate that D$^3$Fields are both generalizable and
effective for zero-shot robotic manipulation tasks. In quantitative comparisons
with state-of-the-art dense descriptors, such as Dense Object Nets and DINO,
D$^3$Fields exhibit significantly better generalization abilities and
manipulation accuracy. | [
"Yixuan Wang",
"Zhuoran Li",
"Mingtong Zhang",
"Katherine Driggs-Campbell",
"Jiajun Wu",
"Li Fei-Fei",
"Yunzhu Li"
] | 2023-09-28 02:50:16 | http://arxiv.org/abs/2309.16118v2 | http://arxiv.org/pdf/2309.16118v2 | 2309.16118v2 |
E2Net: Resource-Efficient Continual Learning with Elastic Expansion Network | Continual Learning methods are designed to learn new tasks without erasing
previous knowledge. However, Continual Learning often requires massive
computational power and storage capacity for satisfactory performance. In this
paper, we propose a resource-efficient continual learning method called the
Elastic Expansion Network (E2Net). Leveraging core subnet distillation and
precise replay sample selection, E2Net achieves superior average accuracy and
diminished forgetting within the same computational and storage constraints,
all while minimizing processing time. In E2Net, we propose Representative
Network Distillation to identify the representative core subnet by assessing
parameter quantity and output similarity with the working network, distilling
analogous subnets within the working network to mitigate reliance on rehearsal
buffers and facilitating knowledge transfer across previous tasks. To enhance
storage resource utilization, we then propose Subnet Constraint Experience
Replay to optimize rehearsal efficiency through a sample storage strategy based
on the structures of representative networks. Extensive experiments conducted
predominantly on cloud environments with diverse datasets and also spanning the
edge environment demonstrate that E2Net consistently outperforms
state-of-the-art methods. In addition, our method outperforms competitors in
terms of both storage and computational requirements. | [
"RuiQi Liu",
"Boyu Diao",
"Libo Huang",
"Zhulin An",
"Yongjun Xu"
] | 2023-09-28 02:48:13 | http://arxiv.org/abs/2309.16117v1 | http://arxiv.org/pdf/2309.16117v1 | 2309.16117v1 |
Compositional Sculpting of Iterative Generative Processes | High training costs of generative models and the need to fine-tune them for
specific tasks have created a strong interest in model reuse and composition. A
key challenge in composing iterative generative processes, such as GFlowNets
and diffusion models, is that to realize the desired target distribution, all
steps of the generative process need to be coordinated, and satisfy delicate
balance conditions. In this work, we propose Compositional Sculpting: a general
approach for defining compositions of iterative generative processes. We then
introduce a method for sampling from these compositions built on classifier
guidance. We showcase ways to accomplish compositional sculpting in both
GFlowNets and diffusion models. We highlight two binary operations
$\unicode{x2014}$ the harmonic mean ($p_1 \otimes p_2$) and the contrast ($p_1
\unicode{x25D1}\,p_2$) between pairs, and the generalization of these
operations to multiple component distributions. We offer empirical results on
image and molecular generation tasks. | [
"Timur Garipov",
"Sebastiaan De Peuter",
"Ge Yang",
"Vikas Garg",
"Samuel Kaski",
"Tommi Jaakkola"
] | 2023-09-28 02:46:53 | http://arxiv.org/abs/2309.16115v1 | http://arxiv.org/pdf/2309.16115v1 | 2309.16115v1 |
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory Exploration | Robots with increasing autonomy progress our space exploration capabilities,
particularly for in-situ exploration and sampling to stand in for human
explorers. Currently, humans drive robots to meet scientific objectives, but
depending on the robot's location, the exchange of information and driving
commands between the human operator and robot may cause undue delays in mission
fulfillment. An autonomous robot encoded with a scientific objective and an
exploration strategy incurs no communication delays and can fulfill missions
more quickly. Active learning algorithms offer this capability of intelligent
exploration, but the underlying model structure varies the performance of the
active learning algorithm in accurately forming an understanding of the
environment. In this paper, we investigate the performance differences between
active learning algorithms driven by Gaussian processes or Bayesian neural
networks for exploration strategies encoded on agents that are constrained in
their trajectories, like planetary surface rovers. These two active learning
strategies were tested in a simulation environment against science-blind
strategies to predict the spatial distribution of a variable of interest along
multiple datasets. The performance metrics of interest are model accuracy in
root mean squared (RMS) error, training time, model convergence, total distance
traveled until convergence, and total samples until convergence. Active
learning strategies encoded with Gaussian processes require less computation to
train, converge to an accurate model more quickly, and propose trajectories of
shorter distance, except in a few complex environments in which Bayesian neural
networks achieve a more accurate model in the large data regime due to their
more expressive functional bases. The paper concludes with advice on when and
how to implement either exploration strategy for future space missions. | [
"Sapphira Akins",
"Frances Zhu"
] | 2023-09-28 02:45:14 | http://arxiv.org/abs/2309.16114v1 | http://arxiv.org/pdf/2309.16114v1 | 2309.16114v1 |
Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics | Contrastive learning is a self-supervised representation learning framework,
where two positive views generated through data augmentation are made similar
by an attraction force in a data representation space, while a repulsive force
makes them far from negative examples. Non-contrastive learning, represented by
BYOL and SimSiam, further gets rid of negative examples and improves
computational efficiency. While learned representations may collapse into a
single point due to the lack of the repulsive force at first sight, Tian et al.
(2021) revealed through the learning dynamics analysis that the representations
can avoid collapse if data augmentation is sufficiently stronger than
regularization. However, their analysis does not take into account
commonly-used feature normalization, a normalizer before measuring the
similarity of representations, and hence excessively strong regularization may
collapse the dynamics, which is an unnatural behavior under the presence of
feature normalization. Therefore, we extend the previous theory based on the L2
loss by considering the cosine loss, which involves feature normalization. We
show that the cosine loss induces sixth-order dynamics (while the L2 loss
induces a third-order one), in which a stable equilibrium dynamically emerges
even if there are only collapsed solutions with given initial parameters. Thus,
we offer a new understanding that feature normalization plays an important role
in robustly preventing the dynamics collapse. | [
"Han Bao"
] | 2023-09-28 02:23:32 | http://arxiv.org/abs/2309.16109v1 | http://arxiv.org/pdf/2309.16109v1 | 2309.16109v1 |
Channel Vision Transformers: An Image Is Worth C x 16 x 16 Words | Vision Transformer (ViT) has emerged as a powerful architecture in the realm
of modern computer vision. However, its application in certain imaging fields,
such as microscopy and satellite imaging, presents unique challenges. In these
domains, images often contain multiple channels, each carrying semantically
distinct and independent information. Furthermore, the model must demonstrate
robustness to sparsity in input channels, as they may not be densely available
during training or testing. In this paper, we propose a modification to the ViT
architecture that enhances reasoning across the input channels and introduce
Hierarchical Channel Sampling (HCS) as an additional regularization technique
to ensure robustness when only partial channels are presented during test time.
Our proposed model, ChannelViT, constructs patch tokens independently from each
input channel and utilizes a learnable channel embedding that is added to the
patch tokens, similar to positional embeddings. We evaluate the performance of
ChannelViT on ImageNet, JUMP-CP (microscopy cell imaging), and So2Sat
(satellite imaging). Our results show that ChannelViT outperforms ViT on
classification tasks and generalizes well, even when a subset of input channels
is used during testing. Across our experiments, HCS proves to be a powerful
regularizer, independent of the architecture employed, suggesting itself as a
straightforward technique for robust ViT training. Lastly, we find that
ChannelViT generalizes effectively even when there is limited access to all
channels during training, highlighting its potential for multi-channel imaging
under real-world conditions with sparse sensors. Our code is available at
https://github.com/insitro/ChannelViT. | [
"Yujia Bao",
"Srinivasan Sivanandan",
"Theofanis Karaletsos"
] | 2023-09-28 02:20:59 | http://arxiv.org/abs/2309.16108v3 | http://arxiv.org/pdf/2309.16108v3 | 2309.16108v3 |
Differentially Private Secure Multiplication: Hiding Information in the Rubble of Noise | We consider the problem of private distributed multi-party multiplication. It
is well-established that Shamir secret-sharing coding strategies can enable
perfect information-theoretic privacy in distributed computation via the
celebrated algorithm of Ben Or, Goldwasser and Wigderson (the "BGW algorithm").
However, perfect privacy and accuracy require an honest majority, that is, $N
\geq 2t+1$ compute nodes are required to ensure privacy against any $t$
colluding adversarial nodes. By allowing for some controlled amount of
information leakage and approximate multiplication instead of exact
multiplication, we study coding schemes for the setting where the number of
honest nodes can be a minority, that is $N< 2t+1.$ We develop a tight
characterization privacy-accuracy trade-off for cases where $N < 2t+1$ by
measuring information leakage using {differential} privacy instead of perfect
privacy, and using the mean squared error metric for accuracy. A novel
technical aspect is an intricately layered noise distribution that merges ideas
from differential privacy and Shamir secret-sharing at different layers. | [
"Viveck R. Cadambe",
"Ateet Devulapalli",
"Haewon Jeong",
"Flavio P. Calmon"
] | 2023-09-28 02:13:13 | http://arxiv.org/abs/2309.16105v1 | http://arxiv.org/pdf/2309.16105v1 | 2309.16105v1 |
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness | The susceptibility of modern machine learning classifiers to adversarial
examples has motivated theoretical results suggesting that these might be
unavoidable. However, these results can be too general to be applicable to
natural data distributions. Indeed, humans are quite robust for tasks involving
vision. This apparent conflict motivates a deeper dive into the question: Are
adversarial examples truly unavoidable? In this work, we theoretically
demonstrate that a key property of the data distribution -- concentration on
small-volume subsets of the input space -- determines whether a robust
classifier exists. We further demonstrate that, for a data distribution
concentrated on a union of low-dimensional linear subspaces, exploiting data
structure naturally leads to classifiers that enjoy good robustness guarantees,
improving upon methods for provable certification in certain regimes. | [
"Ambar Pal",
"Jeremias Sulam",
"René Vidal"
] | 2023-09-28 01:39:47 | http://arxiv.org/abs/2309.16096v1 | http://arxiv.org/pdf/2309.16096v1 | 2309.16096v1 |
AI Potentiality and Awareness: A Position Paper from the Perspective of Human-AI Teaming in Cybersecurity | This position paper explores the broad landscape of AI potentiality in the
context of cybersecurity, with a particular emphasis on its possible risk
factors with awareness, which can be managed by incorporating human experts in
the loop, i.e., "Human-AI" teaming. As artificial intelligence (AI)
technologies advance, they will provide unparalleled opportunities for attack
identification, incident response, and recovery. However, the successful
deployment of AI into cybersecurity measures necessitates an in-depth
understanding of its capabilities, challenges, and ethical and legal
implications to handle associated risk factors in real-world application areas.
Towards this, we emphasize the importance of a balanced approach that
incorporates AI's computational power with human expertise. AI systems may
proactively discover vulnerabilities and detect anomalies through pattern
recognition, and predictive modeling, significantly enhancing speed and
accuracy. Human experts can explain AI-generated decisions to stakeholders,
regulators, and end-users in critical situations, ensuring responsibility and
accountability, which helps establish trust in AI-driven security solutions.
Therefore, in this position paper, we argue that human-AI teaming is worthwhile
in cybersecurity, in which human expertise such as intuition, critical
thinking, or contextual understanding is combined with AI's computational power
to improve overall cyber defenses. | [
"Iqbal H. Sarker",
"Helge Janicke",
"Nazeeruddin Mohammad",
"Paul Watters",
"Surya Nepal"
] | 2023-09-28 01:20:44 | http://arxiv.org/abs/2310.12162v1 | http://arxiv.org/pdf/2310.12162v1 | 2310.12162v1 |
Task-Oriented Koopman-Based Control with Contrastive Encoder | We present task-oriented Koopman-based control that utilizes end-to-end
reinforcement learning and contrastive encoder to simultaneously learn the
Koopman latent embedding, operator and associated linear controller within an
iterative loop. By prioritizing the task cost as main objective for controller
learning, we reduce the reliance of controller design on a well-identified
model, which extends Koopman control beyond low-dimensional systems to
high-dimensional, complex nonlinear systems, including pixel-based scenarios. | [
"Xubo Lyu",
"Hanyang Hu",
"Seth Siriya",
"Ye Pu",
"Mo Chen"
] | 2023-09-28 00:27:07 | http://arxiv.org/abs/2309.16077v1 | http://arxiv.org/pdf/2309.16077v1 | 2309.16077v1 |
Infer and Adapt: Bipedal Locomotion Reward Learning from Demonstrations via Inverse Reinforcement Learning | Enabling bipedal walking robots to learn how to maneuver over highly uneven,
dynamically changing terrains is challenging due to the complexity of robot
dynamics and interacted environments. Recent advancements in learning from
demonstrations have shown promising results for robot learning in complex
environments. While imitation learning of expert policies has been
well-explored, the study of learning expert reward functions is largely
under-explored in legged locomotion. This paper brings state-of-the-art Inverse
Reinforcement Learning (IRL) techniques to solving bipedal locomotion problems
over complex terrains. We propose algorithms for learning expert reward
functions, and we subsequently analyze the learned functions. Through nonlinear
function approximation, we uncover meaningful insights into the expert's
locomotion strategies. Furthermore, we empirically demonstrate that training a
bipedal locomotion policy with the inferred reward functions enhances its
walking performance on unseen terrains, highlighting the adaptability offered
by reward learning. | [
"Feiyang Wu",
"Zhaoyuan Gu",
"Hanran Wu",
"Anqi Wu",
"Ye Zhao"
] | 2023-09-28 00:11:06 | http://arxiv.org/abs/2309.16074v1 | http://arxiv.org/pdf/2309.16074v1 | 2309.16074v1 |
Label Augmentation Method for Medical Landmark Detection in Hip Radiograph Images | This work reports the empirical performance of an automated medical landmark
detection method for predict clinical markers in hip radiograph images.
Notably, the detection method was trained using a label-only augmentation
scheme; our results indicate that this form of augmentation outperforms
traditional data augmentation and produces highly sample efficient estimators.
We train a generic U-Net-based architecture under a curriculum consisting of
two phases: initially relaxing the landmarking task by enlarging the label
points to regions, then gradually eroding these label regions back to the base
task. We measure the benefits of this approach on six datasets of radiographs
with gold-standard expert annotations. | [
"Yehyun Suh",
"Peter Chan",
"J. Ryan Martin",
"Daniel Moyer"
] | 2023-09-27 23:17:58 | http://arxiv.org/abs/2309.16066v1 | http://arxiv.org/pdf/2309.16066v1 | 2309.16066v1 |
Masked autoencoders are scalable learners of cellular morphology | Inferring biological relationships from cellular phenotypes in high-content
microscopy screens provides significant opportunity and challenge in biological
research. Prior results have shown that deep vision models can capture
biological signal better than hand-crafted features. This work explores how
weakly supervised and self-supervised deep learning approaches scale when
training larger models on larger datasets. Our results show that both CNN- and
ViT-based masked autoencoders significantly outperform weakly supervised
models. At the high-end of our scale, a ViT-L/8 trained on over 3.5-billion
unique crops sampled from 95-million microscopy images achieves relative
improvements as high as 28% over our best weakly supervised models at inferring
known biological relationships curated from public databases. | [
"Oren Kraus",
"Kian Kenyon-Dean",
"Saber Saberian",
"Maryam Fallah",
"Peter McLean",
"Jess Leung",
"Vasudev Sharma",
"Ayla Khan",
"Jia Balakrishnan",
"Safiye Celik",
"Maciej Sypetkowski",
"Chi Vicky Cheng",
"Kristen Morse",
"Maureen Makes",
"Ben Mabey",
"Berton Earnshaw"
] | 2023-09-27 23:11:35 | http://arxiv.org/abs/2309.16064v1 | http://arxiv.org/pdf/2309.16064v1 | 2309.16064v1 |
Predicting Cardiovascular Complications in Post-COVID-19 Patients Using Data-Driven Machine Learning Models | The COVID-19 pandemic has globally posed numerous health challenges, notably
the emergence of post-COVID-19 cardiovascular complications. This study
addresses this by utilizing data-driven machine learning models to predict such
complications in 352 post-COVID-19 patients from Iraq. Clinical data, including
demographics, comorbidities, lab results, and imaging, were collected and used
to construct predictive models. These models, leveraging various machine
learning algorithms, demonstrated commendable performance in identifying
patients at risk. Early detection through these models promises timely
interventions and improved outcomes. In conclusion, this research underscores
the potential of data-driven machine learning for predicting post-COVID-19
cardiovascular complications, emphasizing the need for continued validation and
research in diverse clinical settings. | [
"Maitham G. Yousif",
"Hector J. Castro"
] | 2023-09-27 22:52:08 | http://arxiv.org/abs/2309.16059v1 | http://arxiv.org/pdf/2309.16059v1 | 2309.16059v1 |
AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model | We present Any-Modality Augmented Language Model (AnyMAL), a unified model
that reasons over diverse input modality signals (i.e. text, image, video,
audio, IMU motion sensor), and generates textual responses. AnyMAL inherits the
powerful text-based reasoning abilities of the state-of-the-art LLMs including
LLaMA-2 (70B), and converts modality-specific signals to the joint textual
space through a pre-trained aligner module. To further strengthen the
multimodal LLM's capabilities, we fine-tune the model with a multimodal
instruction set manually collected to cover diverse topics and tasks beyond
simple QAs. We conduct comprehensive empirical analysis comprising both human
and automatic evaluations, and demonstrate state-of-the-art performance on
various multimodal tasks. | [
"Seungwhan Moon",
"Andrea Madotto",
"Zhaojiang Lin",
"Tushar Nagarajan",
"Matt Smith",
"Shashank Jain",
"Chun-Fu Yeh",
"Prakash Murugesan",
"Peyman Heidari",
"Yue Liu",
"Kavya Srinet",
"Babak Damavandi",
"Anuj Kumar"
] | 2023-09-27 22:50:51 | http://arxiv.org/abs/2309.16058v1 | http://arxiv.org/pdf/2309.16058v1 | 2309.16058v1 |
Machine Learning-driven Analysis of Gastrointestinal Symptoms in Post-COVID-19 Patients | The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has posed
significant health challenges worldwide. While respiratory symptoms have been
the primary focus, emerging evidence has highlighted the impact of COVID-19 on
various organ systems, including the gastrointestinal (GI) tract. This study,
based on data from 913 post-COVID-19 patients in Iraq collected during 2022 and
2023, investigates the prevalence and patterns of GI symptoms in individuals
recovering from COVID-19 and leverages machine learning algorithms to identify
predictive factors for these symptoms. The research findings reveal that a
notable percentage of post-COVID-19 patients experience GI symptoms during
their recovery phase. Diarrhea emerged as the most frequently reported symptom,
followed by abdominal pain and nausea. Machine learning analysis uncovered
significant predictive factors for GI symptoms, including age, gender, disease
severity, comorbidities, and the duration of COVID-19 illness. These findings
underscore the importance of monitoring and addressing GI symptoms in
post-COVID-19 care, with machine learning offering valuable tools for early
identification and personalized intervention. This study contributes to the
understanding of the long-term consequences of COVID-19 on GI health and
emphasizes the potential benefits of utilizing machine learning-driven analysis
in predicting and managing these symptoms. Further research is warranted to
delve into the mechanisms underlying GI symptoms in COVID-19 survivors and to
develop targeted interventions for symptom management. Keywords: COVID-19,
gastrointestinal symptoms, machine learning, predictive factors, post-COVID-19
care, long COVID. | [
"Maitham G. Yousif",
"Fadhil G. Al-Amran",
"Salman Rawaf",
"Mohammad Abdulla Grmt"
] | 2023-09-27 22:34:19 | http://arxiv.org/abs/2310.00540v1 | http://arxiv.org/pdf/2310.00540v1 | 2310.00540v1 |
Identifying Risk Factors for Post-COVID-19 Mental Health Disorders: A Machine Learning Perspective | In this study, we leveraged machine learning techniques to identify risk
factors associated with post-COVID-19 mental health disorders. Our analysis,
based on data collected from 669 patients across various provinces in Iraq,
yielded valuable insights. We found that age, gender, and geographical region
of residence were significant demographic factors influencing the likelihood of
developing mental health disorders in post-COVID-19 patients. Additionally,
comorbidities and the severity of COVID-19 illness were important clinical
predictors. Psychosocial factors, such as social support, coping strategies,
and perceived stress levels, also played a substantial role. Our findings
emphasize the complex interplay of multiple factors in the development of
mental health disorders following COVID-19 recovery. Healthcare providers and
policymakers should consider these risk factors when designing targeted
interventions and support systems for individuals at risk. Machine
learning-based approaches can provide a valuable tool for predicting and
preventing adverse mental health outcomes in post-COVID-19 patients. Further
research and prospective studies are needed to validate these findings and
enhance our understanding of the long-term psychological impact of the COVID-19
pandemic. This study contributes to the growing body of knowledge regarding the
mental health consequences of the COVID-19 pandemic and underscores the
importance of a multidisciplinary approach to address the diverse needs of
individuals on the path to recovery. Keywords: COVID-19, mental health, risk
factors, machine learning, Iraq | [
"Maitham G. Yousif",
"Fadhil G. Al-Amran",
"Hector J. Castro"
] | 2023-09-27 22:30:11 | http://arxiv.org/abs/2309.16055v1 | http://arxiv.org/pdf/2309.16055v1 | 2309.16055v1 |
Cognizance of Post-COVID-19 Multi-Organ Dysfunction through Machine Learning Analysis | In the year 2022, a total of 466 patients from various cities across Iraq
were included in this study. This research paper focuses on the application of
machine learning techniques to analyse and predict multi-organ dysfunction in
individuals experiencing Post-COVID-19 Syndrome, commonly known as Long COVID.
Post-COVID-19 Syndrome presents a wide array of persistent symptoms affecting
various organ systems, posing a significant challenge to healthcare. Leveraging
the power of artificial intelligence, this study aims to enhance early
detection and management of this complex condition. The paper outlines the
importance of data collection and preprocessing, feature selection and
engineering, model development and validation, and ethical considerations in
conducting research in this field. By improving our understanding of
Post-COVID-19 Syndrome through machine learning, healthcare providers can
identify at-risk individuals and offer timely interventions, potentially
improving patient outcomes and quality of life. Further research is essential
to refine models, validate their clinical utility, and explore treatment
options for Long COVID. Keywords: Post-COVID-19 Syndrome, Machine Learning,
Multi-Organ Dysfunction, Healthcare, Artificial Intelligence. | [
"Hector J. Castro",
"Maitham G. Yousif"
] | 2023-09-27 22:25:49 | http://arxiv.org/abs/2309.16736v1 | http://arxiv.org/pdf/2309.16736v1 | 2309.16736v1 |
Improving Adaptive Online Learning Using Refined Discretization | We study unconstrained Online Linear Optimization with Lipschitz losses. The
goal is to simultaneously achieve ($i$) second order gradient adaptivity; and
($ii$) comparator norm adaptivity also known as "parameter freeness" in the
literature. Existing regret bounds (Cutkosky and Orabona, 2018; Mhammedi and
Koolen, 2020; Jacobsen and Cutkosky, 2022) have the suboptimal $O(\sqrt{V_T\log
V_T})$ dependence on the gradient variance $V_T$, while the present work
improves it to the optimal rate $O(\sqrt{V_T})$ using a novel
continuous-time-inspired algorithm, without any impractical doubling trick.
This result can be extended to the setting with unknown Lipschitz constant,
eliminating the range ratio problem from prior works (Mhammedi and Koolen,
2020).
Concretely, we first show that the aimed simultaneous adaptivity can be
achieved fairly easily in a continuous time analogue of the problem, where the
environment is modeled by an arbitrary continuous semimartingale. Then, our key
innovation is a new discretization argument that preserves such adaptivity in
the discrete time adversarial setting. This refines a non-gradient-adaptive
discretization argument from (Harvey et al., 2023), both algorithmically and
analytically, which could be of independent interest. | [
"Zhiyu Zhang",
"Heng Yang",
"Ashok Cutkosky",
"Ioannis Ch. Paschalidis"
] | 2023-09-27 21:54:52 | http://arxiv.org/abs/2309.16044v1 | http://arxiv.org/pdf/2309.16044v1 | 2309.16044v1 |
Towards Best Practices of Activation Patching in Language Models: Metrics and Methods | Mechanistic interpretability seeks to understand the internal mechanisms of
machine learning models, where localization -- identifying the important model
components -- is a key step. Activation patching, also known as causal tracing
or interchange intervention, is a standard technique for this task (Vig et al.,
2020), but the literature contains many variants with little consensus on the
choice of hyperparameters or methodology. In this work, we systematically
examine the impact of methodological details in activation patching, including
evaluation metrics and corruption methods. In several settings of localization
and circuit discovery in language models, we find that varying these
hyperparameters could lead to disparate interpretability results. Backed by
empirical observations, we give conceptual arguments for why certain metrics or
methods may be preferred. Finally, we provide recommendations for the best
practices of activation patching going forwards. | [
"Fred Zhang",
"Neel Nanda"
] | 2023-09-27 21:53:56 | http://arxiv.org/abs/2309.16042v1 | http://arxiv.org/pdf/2309.16042v1 | 2309.16042v1 |
Analytical Modelling of Raw Data for Flow-Guided In-body Nanoscale Localization | Advancements in nanotechnology and material science are paving the way toward
nanoscale devices that combine sensing, computing, data and energy storage, and
wireless communication. In precision medicine, these nanodevices show promise
for disease diagnostics, treatment, and monitoring from within the patients'
bloodstreams. Assigning the location of a sensed biological event with the
event itself, which is the main proposition of flow-guided in-body nanoscale
localization, would be immensely beneficial from the perspective of precision
medicine. The nanoscale nature of the nanodevices and the challenging
environment that the bloodstream represents, result in current flow-guided
localization approaches being constrained in their communication and
energy-related capabilities. The communication and energy constraints of the
nanodevices result in different features of raw data for flow-guided
localization, in turn affecting its performance. An analytical modeling of the
effects of imperfect communication and constrained energy causing intermittent
operation of the nanodevices on the raw data produced by the nanodevices would
be beneficial. Hence, we propose an analytical model of raw data for
flow-guided localization, where the raw data is modeled as a function of
communication and energy-related capabilities of the nanodevice. We evaluate
the model by comparing its output with the one obtained through the utilization
of a simulator for objective evaluation of flow-guided localization, featuring
comparably higher level of realism. Our results across a number of scenarios
and heterogeneous performance metrics indicate high similarity between the
model and simulator-generated raw datasets. | [
"Guillem Pascual",
"Filip Lemic",
"Carmen Delgado",
"Xavier Costa-Perez"
] | 2023-09-27 21:26:01 | http://arxiv.org/abs/2309.16034v1 | http://arxiv.org/pdf/2309.16034v1 | 2309.16034v1 |
Learning Dissipative Neural Dynamical Systems | Consider an unknown nonlinear dynamical system that is known to be
dissipative. The objective of this paper is to learn a neural dynamical model
that approximates this system, while preserving the dissipativity property in
the model. In general, imposing dissipativity constraints during neural network
training is a hard problem for which no known techniques exist. In this work,
we address the problem of learning a dissipative neural dynamical system model
in two stages. First, we learn an unconstrained neural dynamical model that
closely approximates the system dynamics. Next, we derive sufficient conditions
to perturb the weights of the neural dynamical model to ensure dissipativity,
followed by perturbation of the biases to retain the fit of the model to the
trajectories of the nonlinear system. We show that these two perturbation
problems can be solved independently to obtain a neural dynamical model that is
guaranteed to be dissipative while closely approximating the nonlinear system. | [
"Yuezhu Xu",
"S. Sivaranjani"
] | 2023-09-27 21:25:26 | http://arxiv.org/abs/2309.16032v1 | http://arxiv.org/pdf/2309.16032v1 | 2309.16032v1 |
Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies | Current methods of imitation learning (IL), primarily based on deep neural
networks, offer efficient means for obtaining driving policies from real-world
data but suffer from significant limitations in interpretability and
generalizability. These shortcomings are particularly concerning in
safety-critical applications like autonomous driving. In this paper, we address
these limitations by introducing Symbolic Imitation Learning (SIL), a
groundbreaking method that employs Inductive Logic Programming (ILP) to learn
driving policies which are transparent, explainable and generalisable from
available datasets. Utilizing the real-world highD dataset, we subject our
method to a rigorous comparative analysis against prevailing
neural-network-based IL methods. Our results demonstrate that SIL not only
enhances the interpretability of driving policies but also significantly
improves their applicability across varied driving situations. Hence, this work
offers a novel pathway to more reliable and safer autonomous driving systems,
underscoring the potential of integrating ILP into the domain of IL. | [
"Iman Sharifi",
"Saber Fallah"
] | 2023-09-27 21:03:45 | http://arxiv.org/abs/2309.16025v1 | http://arxiv.org/pdf/2309.16025v1 | 2309.16025v1 |
GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis | With the ever-growing popularity of Graph Neural Networks (GNNs), efficient
GNN inference is gaining tremendous attention. Field-Programming Gate Arrays
(FPGAs) are a promising execution platform due to their fine-grained
parallelism, low-power consumption, reconfigurability, and concurrent
execution. Even better, High-Level Synthesis (HLS) tools bridge the gap between
the non-trivial FPGA development efforts and rapid emergence of new GNN models.
In this paper, we propose GNNHLS, an open-source framework to comprehensively
evaluate GNN inference acceleration on FPGAs via HLS, containing a software
stack for data generation and baseline deployment, and FPGA implementations of
6 well-tuned GNN HLS kernels. We evaluate GNNHLS on 4 graph datasets with
distinct topologies and scales. The results show that GNNHLS achieves up to
50.8x speedup and 423x energy reduction relative to the CPU baselines. Compared
with the GPU baselines, GNNHLS achieves up to 5.16x speedup and 74.5x energy
reduction. | [
"Chenfeng Zhao",
"Zehao Dong",
"Yixin Chen",
"Xuan Zhang",
"Roger D. Chamberlain"
] | 2023-09-27 20:58:33 | http://arxiv.org/abs/2309.16022v1 | http://arxiv.org/pdf/2309.16022v1 | 2309.16022v1 |
GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization | Worldwide Geo-localization aims to pinpoint the precise location of images
taken anywhere on Earth. This task has considerable challenges due to immense
variation in geographic landscapes. The image-to-image retrieval-based
approaches fail to solve this problem on a global scale as it is not feasible
to construct a large gallery of images covering the entire world. Instead,
existing approaches divide the globe into discrete geographic cells,
transforming the problem into a classification task. However, their performance
is limited by the predefined classes and often results in inaccurate
localizations when an image's location significantly deviates from its class
center. To overcome these limitations, we propose GeoCLIP, a novel
CLIP-inspired Image-to-GPS retrieval approach that enforces alignment between
the image and its corresponding GPS locations. GeoCLIP's location encoder
models the Earth as a continuous function by employing positional encoding
through random Fourier features and constructing a hierarchical representation
that captures information at varying resolutions to yield a semantically rich
high-dimensional feature suitable to use even beyond geo-localization. To the
best of our knowledge, this is the first work employing GPS encoding for
geo-localization. We demonstrate the efficacy of our method via extensive
experiments and ablations on benchmark datasets. We achieve competitive
performance with just 20% of training data, highlighting its effectiveness even
in limited-data settings. Furthermore, we qualitatively demonstrate
geo-localization using a text query by leveraging CLIP backbone of our image
encoder. | [
"Vicente Vivanco Cepeda",
"Gaurav Kumar Nayak",
"Mubarak Shah"
] | 2023-09-27 20:54:56 | http://arxiv.org/abs/2309.16020v1 | http://arxiv.org/pdf/2309.16020v1 | 2309.16020v1 |
Graph-level Representation Learning with Joint-Embedding Predictive Architectures | Joint-Embedding Predictive Architectures (JEPAs) have recently emerged as a
novel and powerful technique for self-supervised representation learning. They
aim to learn an energy-based model by predicting the latent representation of a
target signal $y$ from a context signal $x$. JEPAs bypass the need for data
augmentation and negative samples, which are typically required by contrastive
learning, while avoiding the overfitting issues associated with
generative-based pretraining. In this paper, we show that graph-level
representations can be effectively modeled using this paradigm and propose
Graph-JEPA, the first JEPA for the graph domain. In particular, we employ
masked modeling to learn embeddings for different subgraphs of the input graph.
To endow the representations with the implicit hierarchy that is often present
in graph-level concepts, we devise an alternative training objective that
consists of predicting the coordinates of the encoded subgraphs on the unit
hyperbola in the 2D plane. Extensive validation shows that Graph-JEPA can learn
representations that are expressive and competitive in both graph
classification and regression problems. | [
"Geri Skenderi",
"Hang Li",
"Jiliang Tang",
"Marco Cristani"
] | 2023-09-27 20:42:02 | http://arxiv.org/abs/2309.16014v1 | http://arxiv.org/pdf/2309.16014v1 | 2309.16014v1 |
Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systems | Anomaly detection is critical to ensure the security of cyber-physical
systems (CPS). However, due to the increasing complexity of attacks and CPS
themselves, anomaly detection in CPS is becoming more and more challenging. In
our previous work, we proposed a digital twin-based anomaly detection method,
called ATTAIN, which takes advantage of both historical and real-time data of
CPS. However, such data vary significantly in terms of difficulty. Therefore,
similar to human learning processes, deep learning models (e.g., ATTAIN) can
benefit from an easy-to-difficult curriculum. To this end, in this paper, we
present a novel approach, named digitaL twin-based Anomaly deTecTion wIth
Curriculum lEarning (LATTICE), which extends ATTAIN by introducing curriculum
learning to optimize its learning paradigm. LATTICE attributes each sample with
a difficulty score, before being fed into a training scheduler. The training
scheduler samples batches of training data based on these difficulty scores
such that learning from easy to difficult data can be performed. To evaluate
LATTICE, we use five publicly available datasets collected from five real-world
CPS testbeds. We compare LATTICE with ATTAIN and two other state-of-the-art
anomaly detectors. Evaluation results show that LATTICE outperforms the three
baselines and ATTAIN by 0.906%-2.367% in terms of the F1 score. LATTICE also,
on average, reduces the training time of ATTAIN by 4.2% on the five datasets
and is on par with the baselines in terms of detection delay time. | [
"Qinghua Xu",
"Shaukat Ali",
"Tao Yue"
] | 2023-09-27 20:18:02 | http://arxiv.org/abs/2309.15995v1 | http://arxiv.org/pdf/2309.15995v1 | 2309.15995v1 |
Machine Learning Based Analytics for the Significance of Gait Analysis in Monitoring and Managing Lower Extremity Injuries | This study explored the potential of gait analysis as a tool for assessing
post-injury complications, e.g., infection, malunion, or hardware irritation,
in patients with lower extremity fractures. The research focused on the
proficiency of supervised machine learning models predicting complications
using consecutive gait datasets. We identified patients with lower extremity
fractures at an academic center. Patients underwent gait analysis with a
chest-mounted IMU device. Using software, raw gait data was preprocessed,
emphasizing 12 essential gait variables. Machine learning models including
XGBoost, Logistic Regression, SVM, LightGBM, and Random Forest were trained,
tested, and evaluated. Attention was given to class imbalance, addressed using
SMOTE. We introduced a methodology to compute the Rate of Change (ROC) for gait
variables, independent of the time difference between gait analyses. XGBoost
was the optimal model both before and after applying SMOTE. Prior to SMOTE, the
model achieved an average test AUC of 0.90 (95% CI: [0.79, 1.00]) and test
accuracy of 86% (95% CI: [75%, 97%]). Feature importance analysis attributed
importance to the duration between injury and gait analysis. Data patterns
showed early physiological compensations, followed by stabilization phases,
emphasizing prompt gait analysis. This study underscores the potential of
machine learning, particularly XGBoost, in gait analysis for orthopedic care.
Predicting post-injury complications, early gait assessment becomes vital,
revealing intervention points. The findings support a shift in orthopedics
towards a data-informed approach, enhancing patient outcomes. | [
"Mostafa Rezapour",
"Rachel B. Seymour",
"Stephen H. Sims",
"Madhav A. Karunakar",
"Nahir Habet",
"Metin Nafi Gurcan"
] | 2023-09-27 20:12:19 | http://arxiv.org/abs/2309.15990v1 | http://arxiv.org/pdf/2309.15990v1 | 2309.15990v1 |
Open Source Infrastructure for Differentiable Density Functional Theory | Learning exchange correlation functionals, used in quantum chemistry
calculations, from data has become increasingly important in recent years, but
training such a functional requires sophisticated software infrastructure. For
this reason, we build open source infrastructure to train neural exchange
correlation functionals. We aim to standardize the processing pipeline by
adapting state-of-the-art techniques from work done by multiple groups. We have
open sourced the model in the DeepChem library to provide a platform for
additional research on differentiable quantum chemistry methods. | [
"Advika Vidhyadhiraja",
"Arun Pa Thiagarajan",
"Shang Zhu",
"Venkat Viswanathan",
"Bharath Ramsundar"
] | 2023-09-27 20:07:26 | http://arxiv.org/abs/2309.15985v1 | http://arxiv.org/pdf/2309.15985v1 | 2309.15985v1 |
TraCE: Trajectory Counterfactual Explanation Scores | Counterfactual explanations, and their associated algorithmic recourse, are
typically leveraged to understand, explain, and potentially alter a prediction
coming from a black-box classifier. In this paper, we propose to extend the use
of counterfactuals to evaluate progress in sequential decision making tasks. To
this end, we introduce a model-agnostic modular framework, TraCE (Trajectory
Counterfactual Explanation) scores, which is able to distill and condense
progress in highly complex scenarios into a single value. We demonstrate
TraCE's utility across domains by showcasing its main properties in two case
studies spanning healthcare and climate change. | [
"Jeffrey N. Clark",
"Edward A. Small",
"Nawid Keshtmand",
"Michelle W. L. Wan",
"Elena Fillola Mayoral",
"Enrico Werner",
"Christopher P. Bourdeaux",
"Raul Santos-Rodriguez"
] | 2023-09-27 19:24:57 | http://arxiv.org/abs/2309.15965v1 | http://arxiv.org/pdf/2309.15965v1 | 2309.15965v1 |
Resilience of Deep Learning applications: a systematic survey of analysis and hardening techniques | Machine Learning (ML) is currently being exploited in numerous applications
being one of the most effective Artificial Intelligence (AI) technologies, used
in diverse fields, such as vision, autonomous systems, and alike. The trend
motivated a significant amount of contributions to the analysis and design of
ML applications against faults affecting the underlying hardware. The authors
investigate the existing body of knowledge on Deep Learning (among ML
techniques) resilience against hardware faults systematically through a
thoughtful review in which the strengths and weaknesses of this literature
stream are presented clearly and then future avenues of research are set out.
The review is based on 163 scientific articles published between January 2019
and March 2023. The authors adopt a classifying framework to interpret and
highlight research similarities and peculiarities, based on several parameters,
starting from the main scope of the work, the adopted fault and error models,
to their reproducibility. This framework allows for a comparison of the
different solutions and the identification of possible synergies. Furthermore,
suggestions concerning the future direction of research are proposed in the
form of open challenges to be addressed. | [
"Cristiana Bolchini",
"Luca Cassano",
"Antonio Miele"
] | 2023-09-27 19:22:19 | http://arxiv.org/abs/2309.16733v1 | http://arxiv.org/pdf/2309.16733v1 | 2309.16733v1 |
The Devil is in the Details: A Deep Dive into the Rabbit Hole of Data Filtering | The quality of pre-training data plays a critical role in the performance of
foundation models. Popular foundation models often design their own recipe for
data filtering, which makes it hard to analyze and compare different data
filtering approaches. DataComp is a new benchmark dedicated to evaluating
different methods for data filtering. This paper describes our learning and
solution when participating in the DataComp challenge. Our filtering strategy
includes three stages: single-modality filtering, cross-modality filtering, and
data distribution alignment. We integrate existing methods and propose new
solutions, such as computing CLIP score on horizontally flipped images to
mitigate the interference of scene text, using vision and language models to
retrieve training samples for target downstream tasks, rebalancing the data
distribution to improve the efficiency of allocating the computational budget,
etc. We slice and dice our design choices, provide in-depth analysis, and
discuss open questions. Our approach outperforms the best method from the
DataComp paper by over 4% on the average performance of 38 tasks and by over 2%
on ImageNet. | [
"Haichao Yu",
"Yu Tian",
"Sateesh Kumar",
"Linjie Yang",
"Heng Wang"
] | 2023-09-27 19:10:43 | http://arxiv.org/abs/2309.15954v1 | http://arxiv.org/pdf/2309.15954v1 | 2309.15954v1 |
Unified Long-Term Time-Series Forecasting Benchmark | In order to support the advancement of machine learning methods for
predicting time-series data, we present a comprehensive dataset designed
explicitly for long-term time-series forecasting. We incorporate a collection
of datasets obtained from diverse, dynamic systems and real-life records. Each
dataset is standardized by dividing it into training and test trajectories with
predetermined lookback lengths. We include trajectories of length up to $2000$
to ensure a reliable evaluation of long-term forecasting capabilities. To
determine the most effective model in diverse scenarios, we conduct an
extensive benchmarking analysis using classical and state-of-the-art models,
namely LSTM, DeepAR, NLinear, N-Hits, PatchTST, and LatentODE. Our findings
reveal intriguing performance comparisons among these models, highlighting the
dataset-dependent nature of model effectiveness. Notably, we introduce a custom
latent NLinear model and enhance DeepAR with a curriculum learning phase. Both
consistently outperform their vanilla counterparts. | [
"Jacek Cyranka",
"Szymon Haponiuk"
] | 2023-09-27 18:59:00 | http://arxiv.org/abs/2309.15946v1 | http://arxiv.org/pdf/2309.15946v1 | 2309.15946v1 |
Exploring Self-Supervised Contrastive Learning of Spatial Sound Event Representation | In this study, we present a simple multi-channel framework for contrastive
learning (MC-SimCLR) to encode 'what' and 'where' of spatial audios. MC-SimCLR
learns joint spectral and spatial representations from unlabeled spatial
audios, thereby enhancing both event classification and sound localization in
downstream tasks. At its core, we propose a multi-level data augmentation
pipeline that augments different levels of audio features, including waveforms,
Mel spectrograms, and generalized cross-correlation (GCC) features. In
addition, we introduce simple yet effective channel-wise augmentation methods
to randomly swap the order of the microphones and mask Mel and GCC channels. By
using these augmentations, we find that linear layers on top of the learned
representation significantly outperform supervised models in terms of both
event classification accuracy and localization error. We also perform a
comprehensive analysis of the effect of each augmentation method and a
comparison of the fine-tuning performance using different amounts of labeled
data. | [
"Xilin Jiang",
"Cong Han",
"Yinghao Aaron Li",
"Nima Mesgarani"
] | 2023-09-27 18:23:03 | http://arxiv.org/abs/2309.15938v1 | http://arxiv.org/pdf/2309.15938v1 | 2309.15938v1 |
SHACIRA: Scalable HAsh-grid Compression for Implicit Neural Representations | Implicit Neural Representations (INR) or neural fields have emerged as a
popular framework to encode multimedia signals such as images and radiance
fields while retaining high-quality. Recently, learnable feature grids proposed
by Instant-NGP have allowed significant speed-up in the training as well as the
sampling of INRs by replacing a large neural network with a multi-resolution
look-up table of feature vectors and a much smaller neural network. However,
these feature grids come at the expense of large memory consumption which can
be a bottleneck for storage and streaming applications. In this work, we
propose SHACIRA, a simple yet effective task-agnostic framework for compressing
such feature grids with no additional post-hoc pruning/quantization stages. We
reparameterize feature grids with quantized latent weights and apply entropy
regularization in the latent space to achieve high levels of compression across
various domains. Quantitative and qualitative results on diverse datasets
consisting of images, videos, and radiance fields, show that our approach
outperforms existing INR approaches without the need for any large datasets or
domain-specific heuristics. Our project page is available at
http://shacira.github.io . | [
"Sharath Girish",
"Abhinav Shrivastava",
"Kamal Gupta"
] | 2023-09-27 17:59:48 | http://arxiv.org/abs/2309.15848v1 | http://arxiv.org/pdf/2309.15848v1 | 2309.15848v1 |
Exploiting the Signal-Leak Bias in Diffusion Models | There is a bias in the inference pipeline of most diffusion models. This bias
arises from a signal leak whose distribution deviates from the noise
distribution, creating a discrepancy between training and inference processes.
We demonstrate that this signal-leak bias is particularly significant when
models are tuned to a specific style, causing sub-optimal style matching.
Recent research tries to avoid the signal leakage during training. We instead
show how we can exploit this signal-leak bias in existing diffusion models to
allow more control over the generated images. This enables us to generate
images with more varied brightness, and images that better match a desired
style or color. By modeling the distribution of the signal leak in the spatial
frequency and pixel domains, and including a signal leak in the initial latent,
we generate images that better match expected results without any additional
training. | [
"Martin Nicolas Everaert",
"Athanasios Fitsios",
"Marco Bocchio",
"Sami Arpa",
"Sabine Süsstrunk",
"Radhakrishna Achanta"
] | 2023-09-27 17:59:11 | http://arxiv.org/abs/2309.15842v1 | http://arxiv.org/pdf/2309.15842v1 | 2309.15842v1 |
Deep Learning-Based Real-Time Rate Control for Live Streaming on Wireless Networks | Providing wireless users with high-quality video content has become
increasingly important. However, ensuring consistent video quality poses
challenges due to variable encoded bitrate caused by dynamic video content and
fluctuating channel bitrate caused by wireless fading effects. Suboptimal
selection of encoder parameters can lead to video quality loss due to
underutilized bandwidth or the introduction of video artifacts due to packet
loss. To address this, a real-time deep learning based H.264 controller is
proposed. This controller leverages instantaneous channel quality data driven
from the physical layer, along with the video chunk, to dynamically estimate
the optimal encoder parameters with a negligible delay in real-time. The
objective is to maintain an encoded video bitrate slightly below the available
channel bitrate. Experimental results, conducted on both QCIF dataset and a
diverse selection of random videos from public datasets, validate the
effectiveness of the approach. Remarkably, improvements of 10-20 dB in PSNR
with repect to the state-of-the-art adaptive bitrate video streaming is
achieved, with an average packet drop rate as low as 0.002. | [
"Matin Mortaheb",
"Mohammad A. Amir Khojastepour",
"Srimat T. Chakradhar",
"Sennur Ulukus"
] | 2023-09-27 17:53:35 | http://arxiv.org/abs/2310.06857v1 | http://arxiv.org/pdf/2310.06857v1 | 2310.06857v1 |
Multi-unit soft sensing permits few-shot learning | Recent literature has explored various ways to improve soft sensors using
learning algorithms with transferability. Broadly put, the performance of a
soft sensor may be strengthened when it is learned by solving multiple tasks.
The usefulness of transferability depends on how strongly related the devised
learning tasks are. A particularly relevant case for transferability, is when a
soft sensor is to be developed for a process of which there are many
realizations, e.g. system or device with many implementations from which data
is available. Then, each realization presents a soft sensor learning task, and
it is reasonable to expect that the different tasks are strongly related.
Applying transferability in this setting leads to what we call multi-unit soft
sensing, where a soft sensor models a process by learning from data from all of
its realizations.
This paper explores the learning abilities of a multi-unit soft sensor, which
is formulated as a hierarchical model and implemented using a deep neural
network. In particular, we investigate how well the soft sensor generalizes as
the number of units increase. Using a large industrial dataset, we demonstrate
that, when the soft sensor is learned from a sufficient number of tasks, it
permits few-shot learning on data from new units. Surprisingly, regarding the
difficulty of the task, few-shot learning on 1-3 data points often leads to a
high performance on new units. | [
"Bjarne Grimstad",
"Kristian Løvland",
"Lars S. Imsland"
] | 2023-09-27 17:50:05 | http://arxiv.org/abs/2309.15828v1 | http://arxiv.org/pdf/2309.15828v1 | 2309.15828v1 |
Fair Canonical Correlation Analysis | This paper investigates fairness and bias in Canonical Correlation Analysis
(CCA), a widely used statistical technique for examining the relationship
between two sets of variables. We present a framework that alleviates
unfairness by minimizing the correlation disparity error associated with
protected attributes. Our approach enables CCA to learn global projection
matrices from all data points while ensuring that these matrices yield
comparable correlation levels to group-specific projection matrices.
Experimental evaluation on both synthetic and real-world datasets demonstrates
the efficacy of our method in reducing correlation disparity error without
compromising CCA accuracy. | [
"Zhuoping Zhou",
"Davoud Ataee Tarzanagh",
"Bojian Hou",
"Boning Tong",
"Jia Xu",
"Yanbo Feng",
"Qi Long",
"Li Shen"
] | 2023-09-27 17:34:13 | http://arxiv.org/abs/2309.15809v1 | http://arxiv.org/pdf/2309.15809v1 | 2309.15809v1 |
Node-Aligned Graph-to-Graph Generation for Retrosynthesis Prediction | Single-step retrosynthesis is a crucial task in organic chemistry and drug
design, requiring the identification of required reactants to synthesize a
specific compound. with the advent of computer-aided synthesis planning, there
is growing interest in using machine-learning techniques to facilitate the
process. Existing template-free machine learning-based models typically utilize
transformer structures and represent molecules as ID sequences. However, these
methods often face challenges in fully leveraging the extensive topological
information of the molecule and aligning atoms between the production and
reactants, leading to results that are not as competitive as those of
semi-template models. Our proposed method, Node-Aligned Graph-to-Graph (NAG2G),
also serves as a transformer-based template-free model but utilizes 2D
molecular graphs and 3D conformation information. Furthermore, our approach
simplifies the incorporation of production-reactant atom mapping alignment by
leveraging node alignment to determine a specific order for node generation and
generating molecular graphs in an auto-regressive manner node-by-node. This
method ensures that the node generation order coincides with the node order in
the input graph, overcoming the difficulty of determining a specific node
generation order in an auto-regressive manner. Our extensive benchmarking
results demonstrate that the proposed NAG2G can outperform the previous
state-of-the-art baselines in various metrics. | [
"Lin Yao",
"Zhen Wang",
"Wentao Guo",
"Shang Xiang",
"Wentan Liu",
"Guolin Ke"
] | 2023-09-27 17:16:32 | http://arxiv.org/abs/2309.15798v1 | http://arxiv.org/pdf/2309.15798v1 | 2309.15798v1 |
Large Language Model Routing with Benchmark Datasets | There is a rapidly growing number of open-source Large Language Models (LLMs)
and benchmark datasets to compare them. While some models dominate these
benchmarks, no single model typically achieves the best accuracy in all tasks
and use cases. In this work, we address the challenge of selecting the best LLM
out of a collection of models for new tasks. We propose a new formulation for
the problem, in which benchmark datasets are repurposed to learn a "router"
model for this LLM selection, and we show that this problem can be reduced to a
collection of binary classification tasks. We demonstrate the utility and
limitations of learning model routers from various benchmark datasets, where we
consistently improve performance upon using any single model for all tasks. | [
"Tal Shnitzer",
"Anthony Ou",
"Mírian Silva",
"Kate Soule",
"Yuekai Sun",
"Justin Solomon",
"Neil Thompson",
"Mikhail Yurochkin"
] | 2023-09-27 17:08:40 | http://arxiv.org/abs/2309.15789v1 | http://arxiv.org/pdf/2309.15789v1 | 2309.15789v1 |
Partial Transport for Point-Cloud Registration | Point cloud registration plays a crucial role in various fields, including
robotics, computer graphics, and medical imaging. This process involves
determining spatial relationships between different sets of points, typically
within a 3D space. In real-world scenarios, complexities arise from non-rigid
movements and partial visibility, such as occlusions or sensor noise, making
non-rigid registration a challenging problem. Classic non-rigid registration
methods are often computationally demanding, suffer from unstable performance,
and, importantly, have limited theoretical guarantees. The optimal transport
problem and its unbalanced variations (e.g., the optimal partial transport
problem) have emerged as powerful tools for point-cloud registration,
establishing a strong benchmark in this field. These methods view point clouds
as empirical measures and provide a mathematically rigorous way to quantify the
`correspondence' between (the transformed) source and target points. In this
paper, we approach the point-cloud registration problem through the lens of
optimal transport theory and first propose a comprehensive set of non-rigid
registration methods based on the optimal partial transportation problem.
Subsequently, leveraging the emerging work on efficient solutions to the
one-dimensional optimal partial transport problem, we extend our proposed
algorithms via slicing to gain significant computational efficiency, resulting
in fast and robust non-rigid registration algorithms. We demonstrate the
effectiveness of our proposed methods and compare them against baselines on
various 3D and 2D non-rigid registration problems where the source and target
point clouds are corrupted by random noise. | [
"Yikun Bai",
"Huy Tran",
"Steven B. Damelin",
"Soheil Kolouri"
] | 2023-09-27 17:04:22 | http://arxiv.org/abs/2309.15787v1 | http://arxiv.org/pdf/2309.15787v1 | 2309.15787v1 |
Learning the Efficient Frontier | The efficient frontier (EF) is a fundamental resource allocation problem
where one has to find an optimal portfolio maximizing a reward at a given level
of risk. This optimal solution is traditionally found by solving a convex
optimization problem. In this paper, we introduce NeuralEF: a fast neural
approximation framework that robustly forecasts the result of the EF convex
optimization problem with respect to heterogeneous linear constraints and
variable number of optimization inputs. By reformulating an optimization
problem as a sequence to sequence problem, we show that NeuralEF is a viable
solution to accelerate large-scale simulation while handling discontinuous
behavior. | [
"Philippe Chatigny",
"Ivan Sergienko",
"Ryan Ferguson",
"Jordan Weir",
"Maxime Bergeron"
] | 2023-09-27 16:49:37 | http://arxiv.org/abs/2309.15775v2 | http://arxiv.org/pdf/2309.15775v2 | 2309.15775v2 |
Importance-Weighted Offline Learning Done Right | We study the problem of offline policy optimization in stochastic contextual
bandit problems, where the goal is to learn a near-optimal policy based on a
dataset of decision data collected by a suboptimal behavior policy. Rather than
making any structural assumptions on the reward function, we assume access to a
given policy class and aim to compete with the best comparator policy within
this class. In this setting, a standard approach is to compute
importance-weighted estimators of the value of each policy, and select a policy
that minimizes the estimated value up to a "pessimistic" adjustment subtracted
from the estimates to reduce their random fluctuations. In this paper, we show
that a simple alternative approach based on the "implicit exploration"
estimator of \citet{Neu2015} yields performance guarantees that are superior in
nearly all possible terms to all previous results. Most notably, we remove an
extremely restrictive "uniform coverage" assumption made in all previous works.
These improvements are made possible by the observation that the upper and
lower tails importance-weighted estimators behave very differently from each
other, and their careful control can massively improve on previous results that
were all based on symmetric two-sided concentration inequalities. We also
extend our results to infinite policy classes in a PAC-Bayesian fashion, and
showcase the robustness of our algorithm to the choice of hyper-parameters by
means of numerical simulations. | [
"Germano Gabbianelli",
"Gergely Neu",
"Matteo Papini"
] | 2023-09-27 16:42:10 | http://arxiv.org/abs/2309.15771v1 | http://arxiv.org/pdf/2309.15771v1 | 2309.15771v1 |
Algebraic and Statistical Properties of the Ordinary Least Squares Interpolator | Deep learning research has uncovered the phenomenon of benign overfitting for
over-parameterized statistical models, which has drawn significant theoretical
interest in recent years. Given its simplicity and practicality, the ordinary
least squares (OLS) interpolator has become essential to gain foundational
insights into this phenomenon. While properties of OLS are well established in
classical settings, its behavior in high-dimensional settings is less explored
(unlike for ridge or lasso regression) though significant progress has been
made of late. We contribute to this growing literature by providing fundamental
algebraic and statistical results for the minimum $\ell_2$-norm OLS
interpolator. In particular, we provide high-dimensional algebraic equivalents
of (i) the leave-$k$-out residual formula, (ii) Cochran's formula, and (iii)
the Frisch-Waugh-Lovell theorem. These results aid in understanding the OLS
interpolator's ability to generalize and have substantive implications for
causal inference. Additionally, under the Gauss-Markov model, we present
statistical results such as a high-dimensional extension of the Gauss-Markov
theorem and an analysis of variance estimation under homoskedastic errors. To
substantiate our theoretical contributions, we conduct simulation studies that
further explore the stochastic properties of the OLS interpolator. | [
"Dennis Shen",
"Dogyoon Song",
"Peng Ding",
"Jasjeet S. Sekhon"
] | 2023-09-27 16:41:10 | http://arxiv.org/abs/2309.15769v1 | http://arxiv.org/pdf/2309.15769v1 | 2309.15769v1 |
High Perceptual Quality Wireless Image Delivery with Denoising Diffusion Models | We consider the image transmission problem over a noisy wireless channel via
deep learning-based joint source-channel coding (DeepJSCC) along with a
denoising diffusion probabilistic model (DDPM) at the receiver. Specifically,
we are interested in the perception-distortion trade-off in the practical
finite block length regime, in which separate source and channel coding can be
highly suboptimal. We introduce a novel scheme that utilizes the range-null
space decomposition of the target image. We transmit the range-space of the
image after encoding and employ DDPM to progressively refine its null space
contents. Through extensive experiments, we demonstrate significant
improvements in distortion and perceptual quality of reconstructed images
compared to standard DeepJSCC and the state-of-the-art generative
learning-based method. We will publicly share our source code to facilitate
further research and reproducibility. | [
"Selim F. Yilmaz",
"Xueyan Niu",
"Bo Bai",
"Wei Han",
"Lei Deng",
"Deniz Gunduz"
] | 2023-09-27 16:30:59 | http://arxiv.org/abs/2309.15889v1 | http://arxiv.org/pdf/2309.15889v1 | 2309.15889v1 |
Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback | We propose a method for adapting neural networks to distribution shifts at
test-time. In contrast to training-time robustness mechanisms that attempt to
anticipate and counter the shift, we create a closed-loop system and make use
of a test-time feedback signal to adapt a network on the fly. We show that this
loop can be effectively implemented using a learning-based function, which
realizes an amortized optimizer for the network. This leads to an adaptation
method, named Rapid Network Adaptation (RNA), that is notably more flexible and
orders of magnitude faster than the baselines. Through a broad set of
experiments using various adaptation signals and target tasks, we study the
efficiency and flexibility of this method. We perform the evaluations using
various datasets (Taskonomy, Replica, ScanNet, Hypersim, COCO, ImageNet), tasks
(depth, optical flow, semantic segmentation, classification), and distribution
shifts (Cross-datasets, 2D and 3D Common Corruptions) with promising results.
We end with a discussion on general formulations for handling distribution
shifts and our observations from comparing with similar approaches from other
domains. | [
"Teresa Yeo",
"Oğuzhan Fatih Kar",
"Zahra Sodagar",
"Amir Zamir"
] | 2023-09-27 16:20:39 | http://arxiv.org/abs/2309.15762v1 | http://arxiv.org/pdf/2309.15762v1 | 2309.15762v1 |
Borges and AI | Many believe that Large Language Models (LLMs) open the era of Artificial
Intelligence (AI). Some see opportunities while others see dangers. Yet both
proponents and opponents grasp AI through the imagery popularised by science
fiction. Will the machine become sentient and rebel against its creators? Will
we experience a paperclip apocalypse? Before answering such questions, we
should first ask whether this mental imagery provides a good description of the
phenomenon at hand. Understanding weather patterns through the moods of the
gods only goes so far. The present paper instead advocates understanding LLMs
and their connection to AI through the imagery of Jorge Luis Borges, a master
of 20th century literature, forerunner of magical realism, and precursor to
postmodern literature. This exercise leads to a new perspective that
illuminates the relation between language modelling and artificial
intelligence. | [
"Léon Bottou",
"Bernhard Schölkopf"
] | 2023-09-27 16:15:34 | http://arxiv.org/abs/2310.01425v2 | http://arxiv.org/pdf/2310.01425v2 | 2310.01425v2 |
Latent Graphs for Semi-Supervised Learning on Biomedical Tabular Data | In the domain of semi-supervised learning, the current approaches
insufficiently exploit the potential of considering inter-instance
relationships among (un)labeled data. In this work, we address this limitation
by providing an approach for inferring latent graphs that capture the intrinsic
data relationships. By leveraging graph-based representations, our approach
facilitates the seamless propagation of information throughout the graph,
effectively incorporating global and local knowledge. Through evaluations on
biomedical tabular datasets, we compare the capabilities of our approach to
other contemporary methods. Our work demonstrates the significance of
inter-instance relationship discovery as practical means for constructing
robust latent graphs to enhance semi-supervised learning techniques. The
experiments show that the proposed methodology outperforms contemporary
state-of-the-art methods for (semi-)supervised learning on three biomedical
datasets. | [
"Boshko Koloski",
"Nada Lavrač",
"Senja Pollak",
"Blaž Škrlj"
] | 2023-09-27 16:13:36 | http://arxiv.org/abs/2309.15757v3 | http://arxiv.org/pdf/2309.15757v3 | 2309.15757v3 |
Provably Efficient Exploration in Constrained Reinforcement Learning:Posterior Sampling Is All You Need | We present a new algorithm based on posterior sampling for learning in
constrained Markov decision processes (CMDP) in the infinite-horizon
undiscounted setting. The algorithm achieves near-optimal regret bounds while
being advantageous empirically compared to the existing algorithms. Our main
theoretical result is a Bayesian regret bound for each cost component of
\tilde{O} (HS \sqrt{AT}) for any communicating CMDP with S states, A actions,
and bound on the hitting time H. This regret bound matches the lower bound in
order of time horizon T and is the best-known regret bound for communicating
CMDPs in the infinite-horizon undiscounted setting. Empirical results show
that, despite its simplicity, our posterior sampling algorithm outperforms the
existing algorithms for constrained reinforcement learning. | [
"Danil Provodin",
"Pratik Gajane",
"Mykola Pechenizkiy",
"Maurits Kaptein"
] | 2023-09-27 15:48:36 | http://arxiv.org/abs/2309.15737v1 | http://arxiv.org/pdf/2309.15737v1 | 2309.15737v1 |
Deep Learning-based Analysis of Basins of Attraction | This study showcases the effectiveness of convolutional neural networks
(CNNs) in characterizing the complexity and unpredictability of basins of
attraction for diverse dynamical systems. This novel method is optimal for
exploring different parameters of dynamical systems since the conventional
methods are computationally expensive for characterizing multiple basins of
attraction. Additionally, our research includes a comparison of different CNN
architectures for this task showing the superiority of our proposed
characterization method over the conventional methods, even with obsolete
architectures. | [
"David Valle",
"Alexandre Wagemakers",
"Miguel A. F. Sanjuán"
] | 2023-09-27 15:41:12 | http://arxiv.org/abs/2309.15732v1 | http://arxiv.org/pdf/2309.15732v1 | 2309.15732v1 |
Temporal graph models fail to capture global temporal dynamics | A recently released Temporal Graph Benchmark is analyzed in the context of
Dynamic Link Property Prediction. We outline our observations and propose a
trivial optimization-free baseline of "recently popular nodes" outperforming
other methods on medium and large-size datasets in the Temporal Graph
Benchmark. We propose two measures based on Wasserstein distance which can
quantify the strength of short-term and long-term global dynamics of datasets.
By analyzing our unexpectedly strong baseline, we show how standard negative
sampling evaluation can be unsuitable for datasets with strong temporal
dynamics. We also show how simple negative-sampling can lead to model
degeneration during training, resulting in impossible to rank, fully saturated
predictions of temporal graph networks. We propose improved negative sampling
schemes for both training and evaluation and prove their usefulness. We conduct
a comparison with a model trained non-contrastively without negative sampling.
Our results provide a challenging baseline and indicate that temporal graph
network architectures need deep rethinking for usage in problems with
significant global dynamics, such as social media, cryptocurrency markets or
e-commerce. We open-source the code for baselines, measures and proposed
negative sampling schemes. | [
"Michał Daniluk",
"Jacek Dąbrowski"
] | 2023-09-27 15:36:45 | http://arxiv.org/abs/2309.15730v2 | http://arxiv.org/pdf/2309.15730v2 | 2309.15730v2 |
Factorized Diffusion Architectures for Unsupervised Image Generation and Segmentation | We develop a neural network architecture which, trained in an unsupervised
manner as a denoising diffusion model, simultaneously learns to both generate
and segment images. Learning is driven entirely by the denoising diffusion
objective, without any annotation or prior knowledge about regions during
training. A computational bottleneck, built into the neural architecture,
encourages the denoising network to partition an input into regions, denoise
them in parallel, and combine the results. Our trained model generates both
synthetic images and, by simple examination of its internal predicted
partitions, a semantic segmentation of those images. Without any finetuning, we
directly apply our unsupervised model to the downstream task of segmenting real
images via noising and subsequently denoising them. Experiments demonstrate
that our model achieves accurate unsupervised image segmentation and
high-quality synthetic image generation across multiple datasets. | [
"Xin Yuan",
"Michael Maire"
] | 2023-09-27 15:32:46 | http://arxiv.org/abs/2309.15726v1 | http://arxiv.org/pdf/2309.15726v1 | 2309.15726v1 |
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