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Learning topological operations on meshes with application to block decomposition of polygons | We present a learning based framework for mesh quality improvement on
unstructured triangular and quadrilateral meshes. Our model learns to improve
mesh quality according to a prescribed objective function purely via self-play
reinforcement learning with no prior heuristics. The actions performed on the
mesh are standard local and global element operations. The goal is to minimize
the deviation of the node degrees from their ideal values, which in the case of
interior vertices leads to a minimization of irregular nodes. | [
"Arjun Narayanan",
"Yulong Pan",
"Per-Olof Persson"
] | 2023-09-12 18:00:27 | http://arxiv.org/abs/2309.06484v1 | http://arxiv.org/pdf/2309.06484v1 | 2309.06484v1 |
Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation | Many components of data analysis in high energy physics and beyond require
morphing one dataset into another. This is commonly solved via reweighting, but
there are many advantages of preserving weights and shifting the data points
instead. Normalizing flows are machine learning models with impressive
precision on a variety of particle physics tasks. Naively, normalizing flows
cannot be used for morphing because they require knowledge of the probability
density of the starting dataset. In most cases in particle physics, we can
generate more examples, but we do not know densities explicitly. We propose a
protocol called flows for flows for training normalizing flows to morph one
dataset into another even if the underlying probability density of neither
dataset is known explicitly. This enables a morphing strategy trained with
maximum likelihood estimation, a setup that has been shown to be highly
effective in related tasks. We study variations on this protocol to explore how
far the data points are moved to statistically match the two datasets.
Furthermore, we show how to condition the learned flows on particular features
in order to create a morphing function for every value of the conditioning
feature. For illustration, we demonstrate flows for flows for toy examples as
well as a collider physics example involving dijet events | [
"Tobias Golling",
"Samuel Klein",
"Radha Mastandrea",
"Benjamin Nachman",
"John Andrew Raine"
] | 2023-09-12 18:00:01 | http://arxiv.org/abs/2309.06472v1 | http://arxiv.org/pdf/2309.06472v1 | 2309.06472v1 |
LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning | Dexterous manipulation has been a long-standing challenge in robotics. While
machine learning techniques have shown some promise, results have largely been
currently limited to simulation. This can be mostly attributed to the lack of
suitable hardware. In this paper, we present LEAP Hand, a low-cost dexterous
and anthropomorphic hand for machine learning research. In contrast to previous
hands, LEAP Hand has a novel kinematic structure that allows maximal dexterity
regardless of finger pose. LEAP Hand is low-cost and can be assembled in 4
hours at a cost of 2000 USD from readily available parts. It is capable of
consistently exerting large torques over long durations of time. We show that
LEAP Hand can be used to perform several manipulation tasks in the real world
-- from visual teleoperation to learning from passive video data and sim2real.
LEAP Hand significantly outperforms its closest competitor Allegro Hand in all
our experiments while being 1/8th of the cost. We release detailed assembly
instructions, the Sim2Real pipeline and a development platform with useful APIs
on our website at https://leap-hand.github.io/ | [
"Kenneth Shaw",
"Ananye Agarwal",
"Deepak Pathak"
] | 2023-09-12 17:59:20 | http://arxiv.org/abs/2309.06440v1 | http://arxiv.org/pdf/2309.06440v1 | 2309.06440v1 |
Unveiling the potential of large language models in generating semantic and cross-language clones | Semantic and Cross-language code clone generation may be useful for code
reuse, code comprehension, refactoring and benchmarking. OpenAI's GPT model has
potential in such clone generation as GPT is used for text generation. When
developers copy/paste codes from Stack Overflow (SO) or within a system, there
might be inconsistent changes leading to unexpected behaviours. Similarly, if
someone possesses a code snippet in a particular programming language but seeks
equivalent functionality in a different language, a semantic cross-language
code clone generation approach could provide valuable assistance. In this
study, using SemanticCloneBench as a vehicle, we evaluated how well the GPT-3
model could help generate semantic and cross-language clone variants for a
given fragment.We have comprised a diverse set of code fragments and assessed
GPT-3s performance in generating code variants.Through extensive
experimentation and analysis, where 9 judges spent 158 hours to validate, we
investigate the model's ability to produce accurate and semantically correct
variants. Our findings shed light on GPT-3's strengths in code generation,
offering insights into the potential applications and challenges of using
advanced language models in software development. Our quantitative analysis
yields compelling results. In the realm of semantic clones, GPT-3 attains an
impressive accuracy of 62.14% and 0.55 BLEU score, achieved through few-shot
prompt engineering. Furthermore, the model shines in transcending linguistic
confines, boasting an exceptional 91.25% accuracy in generating cross-language
clones | [
"Palash R. Roy",
"Ajmain I. Alam",
"Farouq Al-omari",
"Banani Roy",
"Chanchal K. Roy",
"Kevin A. Schneider"
] | 2023-09-12 17:40:49 | http://arxiv.org/abs/2309.06424v1 | http://arxiv.org/pdf/2309.06424v1 | 2309.06424v1 |
On Computationally Efficient Learning of Exponential Family Distributions | We consider the classical problem of learning, with arbitrary accuracy, the
natural parameters of a $k$-parameter truncated \textit{minimal} exponential
family from i.i.d. samples in a computationally and statistically efficient
manner. We focus on the setting where the support as well as the natural
parameters are appropriately bounded. While the traditional maximum likelihood
estimator for this class of exponential family is consistent, asymptotically
normal, and asymptotically efficient, evaluating it is computationally hard. In
this work, we propose a novel loss function and a computationally efficient
estimator that is consistent as well as asymptotically normal under mild
conditions. We show that, at the population level, our method can be viewed as
the maximum likelihood estimation of a re-parameterized distribution belonging
to the same class of exponential family. Further, we show that our estimator
can be interpreted as a solution to minimizing a particular Bregman score as
well as an instance of minimizing the \textit{surrogate} likelihood. We also
provide finite sample guarantees to achieve an error (in $\ell_2$-norm) of
$\alpha$ in the parameter estimation with sample complexity $O({\sf
poly}(k)/\alpha^2)$. Our method achives the order-optimal sample complexity of
$O({\sf log}(k)/\alpha^2)$ when tailored for node-wise-sparse Markov random
fields. Finally, we demonstrate the performance of our estimator via numerical
experiments. | [
"Abhin Shah",
"Devavrat Shah",
"Gregory W. Wornell"
] | 2023-09-12 17:25:32 | http://arxiv.org/abs/2309.06413v1 | http://arxiv.org/pdf/2309.06413v1 | 2309.06413v1 |
Exploring Large Language Models for Ontology Alignment | This work investigates the applicability of recent generative Large Language
Models (LLMs), such as the GPT series and Flan-T5, to ontology alignment for
identifying concept equivalence mappings across ontologies. To test the
zero-shot performance of Flan-T5-XXL and GPT-3.5-turbo, we leverage challenging
subsets from two equivalence matching datasets of the OAEI Bio-ML track, taking
into account concept labels and structural contexts. Preliminary findings
suggest that LLMs have the potential to outperform existing ontology alignment
systems like BERTMap, given careful framework and prompt design. | [
"Yuan He",
"Jiaoyan Chen",
"Hang Dong",
"Ian Horrocks"
] | 2023-09-12 17:01:02 | http://arxiv.org/abs/2309.07172v1 | http://arxiv.org/pdf/2309.07172v1 | 2309.07172v1 |
Ensemble Mask Networks | Can an $\mathbb{R}^n\rightarrow \mathbb{R}^n$ feedforward network learn
matrix-vector multiplication? This study introduces two mechanisms - flexible
masking to take matrix inputs, and a unique network pruning to respect the
mask's dependency structure. Networks can approximate fixed operations such as
matrix-vector multiplication $\phi(A,x) \rightarrow Ax$, motivating the
mechanisms introduced with applications towards litmus-testing dependencies or
interaction order in graph-based models. | [
"Jonny Luntzel"
] | 2023-09-12 16:48:00 | http://arxiv.org/abs/2309.06382v2 | http://arxiv.org/pdf/2309.06382v2 | 2309.06382v2 |
InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation | Diffusion models have revolutionized text-to-image generation with its
exceptional quality and creativity. However, its multi-step sampling process is
known to be slow, often requiring tens of inference steps to obtain
satisfactory results. Previous attempts to improve its sampling speed and
reduce computational costs through distillation have been unsuccessful in
achieving a functional one-step model. In this paper, we explore a recent
method called Rectified Flow, which, thus far, has only been applied to small
datasets. The core of Rectified Flow lies in its \emph{reflow} procedure, which
straightens the trajectories of probability flows, refines the coupling between
noises and images, and facilitates the distillation process with student
models. We propose a novel text-conditioned pipeline to turn Stable Diffusion
(SD) into an ultra-fast one-step model, in which we find reflow plays a
critical role in improving the assignment between noise and images. Leveraging
our new pipeline, we create, to the best of our knowledge, the first one-step
diffusion-based text-to-image generator with SD-level image quality, achieving
an FID (Frechet Inception Distance) of $23.3$ on MS COCO 2017-5k, surpassing
the previous state-of-the-art technique, progressive distillation, by a
significant margin ($37.2$ $\rightarrow$ $23.3$ in FID). By utilizing an
expanded network with 1.7B parameters, we further improve the FID to $22.4$. We
call our one-step models \emph{InstaFlow}. On MS COCO 2014-30k, InstaFlow
yields an FID of $13.1$ in just $0.09$ second, the best in $\leq 0.1$ second
regime, outperforming the recent StyleGAN-T ($13.9$ in $0.1$ second). Notably,
the training of InstaFlow only costs 199 A100 GPU days. Project
page:~\url{https://github.com/gnobitab/InstaFlow}. | [
"Xingchao Liu",
"Xiwen Zhang",
"Jianzhu Ma",
"Jian Peng",
"Qiang Liu"
] | 2023-09-12 16:42:09 | http://arxiv.org/abs/2309.06380v1 | http://arxiv.org/pdf/2309.06380v1 | 2309.06380v1 |
Recovering from Privacy-Preserving Masking with Large Language Models | Model adaptation is crucial to handle the discrepancy between proxy training
data and actual users data received. To effectively perform adaptation, textual
data of users is typically stored on servers or their local devices, where
downstream natural language processing (NLP) models can be directly trained
using such in-domain data. However, this might raise privacy and security
concerns due to the extra risks of exposing user information to adversaries.
Replacing identifying information in textual data with a generic marker has
been recently explored. In this work, we leverage large language models (LLMs)
to suggest substitutes of masked tokens and have their effectiveness evaluated
on downstream language modeling tasks. Specifically, we propose multiple
pre-trained and fine-tuned LLM-based approaches and perform empirical studies
on various datasets for the comparison of these methods. Experimental results
show that models trained on the obfuscation corpora are able to achieve
comparable performance with the ones trained on the original data without
privacy-preserving token masking. | [
"Arpita Vats",
"Zhe Liu",
"Peng Su",
"Debjyoti Paul",
"Yingyi Ma",
"Yutong Pang",
"Zeeshan Ahmed",
"Ozlem Kalinli"
] | 2023-09-12 16:39:41 | http://arxiv.org/abs/2309.08628v2 | http://arxiv.org/pdf/2309.08628v2 | 2309.08628v2 |
Using Reed-Muller Codes for Classification with Rejection and Recovery | When deploying classifiers in the real world, users expect them to respond to
inputs appropriately. However, traditional classifiers are not equipped to
handle inputs which lie far from the distribution they were trained on.
Malicious actors can exploit this defect by making adversarial perturbations
designed to cause the classifier to give an incorrect output.
Classification-with-rejection methods attempt to solve this problem by allowing
networks to refuse to classify an input in which they have low confidence. This
works well for strongly adversarial examples, but also leads to the rejection
of weakly perturbed images, which intuitively could be correctly classified. To
address these issues, we propose Reed-Muller Aggregation Networks (RMAggNet), a
classifier inspired by Reed-Muller error-correction codes which can correct and
reject inputs. This paper shows that RMAggNet can minimise incorrectness while
maintaining good correctness over multiple adversarial attacks at different
perturbation budgets by leveraging the ability to correct errors in the
classification process. This provides an alternative
classification-with-rejection method which can reduce the amount of additional
processing in situations where a small number of incorrect classifications are
permissible. | [
"Daniel Fentham",
"David Parker",
"Mark Ryan"
] | 2023-09-12 16:20:20 | http://arxiv.org/abs/2309.06359v1 | http://arxiv.org/pdf/2309.06359v1 | 2309.06359v1 |
Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors | Thompson sampling (TS) is one of the most popular and earliest algorithms to
solve stochastic multi-armed bandit problems. We consider a variant of TS,
named $\alpha$-TS, where we use a fractional or $\alpha$-posterior
($\alpha\in(0,1)$) instead of the standard posterior distribution. To compute
an $\alpha$-posterior, the likelihood in the definition of the standard
posterior is tempered with a factor $\alpha$. For $\alpha$-TS we obtain both
instance-dependent $\mathcal{O}\left(\sum_{k \neq i^*}
\Delta_k\left(\frac{\log(T)}{C(\alpha)\Delta_k^2} + \frac{1}{2} \right)\right)$
and instance-independent $\mathcal{O}(\sqrt{KT\log K})$ frequentist regret
bounds under very mild conditions on the prior and reward distributions, where
$\Delta_k$ is the gap between the true mean rewards of the $k^{th}$ and the
best arms, and $C(\alpha)$ is a known constant. Both the sub-Gaussian and
exponential family models satisfy our general conditions on the reward
distribution. Our conditions on the prior distribution just require its density
to be positive, continuous, and bounded. We also establish another
instance-dependent regret upper bound that matches (up to constants) to that of
improved UCB [Auer and Ortner, 2010]. Our regret analysis carefully combines
recent theoretical developments in the non-asymptotic concentration analysis
and Bernstein-von Mises type results for the $\alpha$-posterior distribution.
Moreover, our analysis does not require additional structural properties such
as closed-form posteriors or conjugate priors. | [
"Prateek Jaiswal",
"Debdeep Pati",
"Anirban Bhattacharya",
"Bani K. Mallick"
] | 2023-09-12 16:15:33 | http://arxiv.org/abs/2309.06349v1 | http://arxiv.org/pdf/2309.06349v1 | 2309.06349v1 |
Band-gap regression with architecture-optimized message-passing neural networks | Graph-based neural networks and, specifically, message-passing neural
networks (MPNNs) have shown great potential in predicting physical properties
of solids. In this work, we train an MPNN to first classify materials through
density functional theory data from the AFLOW database as being metallic or
semiconducting/insulating. We then perform a neural-architecture search to
explore the model architecture and hyperparameter space of MPNNs to predict the
band gaps of the materials identified as non-metals. The parameters in the
search include the number of message-passing steps, latent size, and
activation-function, among others. The top-performing models from the search
are pooled into an ensemble that significantly outperforms existing models from
the literature. Uncertainty quantification is evaluated with Monte-Carlo
Dropout and ensembling, with the ensemble method proving superior. The domain
of applicability of the ensemble model is analyzed with respect to the crystal
systems, the inclusion of a Hubbard parameter in the density functional
calculations, and the atomic species building up the materials. | [
"Tim Bechtel",
"Daniel T. Speckhard",
"Jonathan Godwin",
"Claudia Draxl"
] | 2023-09-12 16:13:10 | http://arxiv.org/abs/2309.06348v1 | http://arxiv.org/pdf/2309.06348v1 | 2309.06348v1 |
Efficient Graphics Representation with Differentiable Indirection | We introduce differentiable indirection -- a novel learned primitive that
employs differentiable multi-scale lookup tables as an effective substitute for
traditional compute and data operations across the graphics pipeline. We
demonstrate its flexibility on a number of graphics tasks, i.e., geometric and
image representation, texture mapping, shading, and radiance field
representation. In all cases, differentiable indirection seamlessly integrates
into existing architectures, trains rapidly, and yields both versatile and
efficient results. | [
"Sayantan Datta",
"Carl Marshall",
"Zhao Dong",
"Zhengqin Li",
"Derek Nowrouzezahrai"
] | 2023-09-12 16:05:45 | http://arxiv.org/abs/2309.08387v1 | http://arxiv.org/pdf/2309.08387v1 | 2309.08387v1 |
Learning Minimalistic Tsetlin Machine Clauses with Markov Boundary-Guided Pruning | A set of variables is the Markov blanket of a random variable if it contains
all the information needed for predicting the variable. If the blanket cannot
be reduced without losing useful information, it is called a Markov boundary.
Identifying the Markov boundary of a random variable is advantageous because
all variables outside the boundary are superfluous. Hence, the Markov boundary
provides an optimal feature set. However, learning the Markov boundary from
data is challenging for two reasons. If one or more variables are removed from
the Markov boundary, variables outside the boundary may start providing
information. Conversely, variables within the boundary may stop providing
information. The true role of each candidate variable is only manifesting when
the Markov boundary has been identified. In this paper, we propose a new
Tsetlin Machine (TM) feedback scheme that supplements Type I and Type II
feedback. The scheme introduces a novel Finite State Automaton - a
Context-Specific Independence Automaton. The automaton learns which features
are outside the Markov boundary of the target, allowing them to be pruned from
the TM during learning. We investigate the new scheme empirically, showing how
it is capable of exploiting context-specific independence to find Markov
boundaries. Further, we provide a theoretical analysis of convergence. Our
approach thus connects the field of Bayesian networks (BN) with TMs,
potentially opening up for synergies when it comes to inference and learning,
including TM-produced Bayesian knowledge bases and TM-based Bayesian inference. | [
"Ole-Christoffer Granmo",
"Per-Arne Andersen",
"Lei Jiao",
"Xuan Zhang",
"Christian Blakely",
"Tor Tveit"
] | 2023-09-12 15:27:00 | http://arxiv.org/abs/2309.06315v1 | http://arxiv.org/pdf/2309.06315v1 | 2309.06315v1 |
Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle | It is difficult to perform 3D reconstruction from on-vehicle gathered video
due to the large forward motion of the vehicle. Even object detection and human
sensing models perform significantly worse on onboard videos when compared to
standard benchmarks because objects often appear far away from the camera
compared to the standard object detection benchmarks, image quality is often
decreased by motion blur and occlusions occur often. This has led to the
popularisation of traffic data-specific benchmarks. Recently Light Detection
And Ranging (LiDAR) sensors have become popular to directly estimate depths
without the need to perform 3D reconstructions. However, LiDAR-based methods
still lack in articulated human detection at a distance when compared to
image-based methods. We hypothesize that benchmarks targeted at articulated
human sensing from LiDAR data could bring about increased research in human
sensing and prediction in traffic and could lead to improved traffic safety for
pedestrians. | [
"Maria Priisalu"
] | 2023-09-12 15:24:26 | http://arxiv.org/abs/2309.06313v1 | http://arxiv.org/pdf/2309.06313v1 | 2309.06313v1 |
Modeling Supply and Demand in Public Transportation Systems | We propose two neural network based and data-driven supply and demand models
to analyze the efficiency, identify service gaps, and determine the significant
predictors of demand, in the bus system for the Department of Public
Transportation (HDPT) in Harrisonburg City, Virginia, which is the home to
James Madison University (JMU). The supply and demand models, one temporal and
one spatial, take many variables into account, including the demographic data
surrounding the bus stops, the metrics that the HDPT reports to the federal
government, and the drastic change in population between when JMU is on or off
session. These direct and data-driven models to quantify supply and demand and
identify service gaps can generalize to other cities' bus systems. | [
"Miranda Bihler",
"Hala Nelson",
"Erin Okey",
"Noe Reyes Rivas",
"John Webb",
"Anna White"
] | 2023-09-12 15:05:11 | http://arxiv.org/abs/2309.06299v2 | http://arxiv.org/pdf/2309.06299v2 | 2309.06299v2 |
Transferability analysis of data-driven additive manufacturing knowledge: a case study between powder bed fusion and directed energy deposition | Data-driven research in Additive Manufacturing (AM) has gained significant
success in recent years. This has led to a plethora of scientific literature to
emerge. The knowledge in these works consists of AM and Artificial Intelligence
(AI) contexts that have not been mined and formalized in an integrated way.
Moreover, no tools or guidelines exist to support data-driven knowledge
transfer from one context to another. As a result, data-driven solutions using
specific AI techniques are being developed and validated only for specific AM
process technologies. There is a potential to exploit the inherent similarities
across various AM technologies and adapt the existing solutions from one
process or problem to another using AI, such as Transfer Learning. We propose a
three-step knowledge transferability analysis framework in AM to support
data-driven AM knowledge transfer. As a prerequisite to transferability
analysis, AM knowledge is featurized into identified knowledge components. The
framework consists of pre-transfer, transfer, and post-transfer steps to
accomplish knowledge transfer. A case study is conducted between flagship metal
AM processes. Laser Powder Bed Fusion (LPBF) is the source of knowledge
motivated by its relative matureness in applying AI over Directed Energy
Deposition (DED), which drives the need for knowledge transfer as the less
explored target process. We show successful transfer at different levels of the
data-driven solution, including data representation, model architecture, and
model parameters. The pipeline of AM knowledge transfer can be automated in the
future to allow efficient cross-context or cross-process knowledge exchange. | [
"Mutahar Safdar",
"Jiarui Xie",
"Hyunwoong Ko",
"Yan Lu",
"Guy Lamouche",
"Yaoyao Fiona Zhao"
] | 2023-09-12 14:46:56 | http://arxiv.org/abs/2309.06286v1 | http://arxiv.org/pdf/2309.06286v1 | 2309.06286v1 |
ELRA: Exponential learning rate adaption gradient descent optimization method | We present a novel, fast (exponential rate adaption), ab initio
(hyper-parameter-free) gradient based optimizer algorithm. The main idea of the
method is to adapt the learning rate $\alpha$ by situational awareness, mainly
striving for orthogonal neighboring gradients. The method has a high success
and fast convergence rate and does not rely on hand-tuned parameters giving it
greater universality. It can be applied to problems of any dimensions n and
scales only linearly (of order O(n)) with the dimension of the problem. It
optimizes convex and non-convex continuous landscapes providing some kind of
gradient. In contrast to the Ada-family (AdaGrad, AdaMax, AdaDelta, Adam, etc.)
the method is rotation invariant: optimization path and performance are
independent of coordinate choices. The impressive performance is demonstrated
by extensive experiments on the MNIST benchmark data-set against
state-of-the-art optimizers. We name this new class of optimizers after its
core idea Exponential Learning Rate Adaption - ELRA. We present it in two
variants c2min and p2min with slightly different control. The authors strongly
believe that ELRA will open a completely new research direction for gradient
descent optimize. | [
"Alexander Kleinsorge",
"Stefan Kupper",
"Alexander Fauck",
"Felix Rothe"
] | 2023-09-12 14:36:13 | http://arxiv.org/abs/2309.06274v1 | http://arxiv.org/pdf/2309.06274v1 | 2309.06274v1 |
ssVERDICT: Self-Supervised VERDICT-MRI for Enhanced Prostate Tumour Characterisation | Purpose: Demonstrating and assessing self-supervised machine learning fitting
of the VERDICT (Vascular, Extracellular and Restricted DIffusion for Cytometry
in Tumours) model for prostate. Methods: We derive a self-supervised neural
network for fitting VERDICT (ssVERDICT) that estimates parameter maps without
training data. We compare the performance of ssVERDICT to two established
baseline methods for fitting diffusion MRI models: conventional nonlinear least
squares (NLLS) and supervised deep learning. We do this quantitatively on
simulated data, by comparing the Pearson's correlation coefficient,
mean-squared error (MSE), bias, and variance with respect to the simulated
ground truth. We also calculate in vivo parameter maps on a cohort of 20
prostate cancer patients and compare the methods' performance in discriminating
benign from cancerous tissue via Wilcoxon's signed-rank test. Results: In
simulations, ssVERDICT outperforms the baseline methods (NLLS and supervised
DL) in estimating all the parameters from the VERDICT prostate model in terms
of Pearson's correlation coefficient, bias, and MSE. In vivo, ssVERDICT shows
stronger lesion conspicuity across all parameter maps, and improves
discrimination between benign and cancerous tissue over the baseline methods.
Conclusion: ssVERDICT significantly outperforms state-of-the-art methods for
VERDICT model fitting, and shows for the first time, fitting of a complex
three-compartment biophysical model with machine learning without the
requirement of explicit training labels. | [
"Snigdha Sen",
"Saurabh Singh",
"Hayley Pye",
"Caroline M. Moore",
"Hayley Whitaker",
"Shonit Punwani",
"David Atkinson",
"Eleftheria Panagiotaki",
"Paddy J. Slator"
] | 2023-09-12 14:31:33 | http://arxiv.org/abs/2309.06268v2 | http://arxiv.org/pdf/2309.06268v2 | 2309.06268v2 |
Toward Discretization-Consistent Closure Schemes for Large Eddy Simulation Using Reinforcement Learning | We propose a novel method for developing discretization-consistent closure
schemes for implicitly filtered Large Eddy Simulation (LES). In implicitly
filtered LES, the induced filter kernel, and thus the closure terms, are
determined by the properties of the grid and the discretization operator,
leading to additional computational subgrid terms that are generally unknown in
a priori analysis. Therefore, the task of adapting the coefficients of LES
closure models is formulated as a Markov decision process and solved in an a
posteriori manner with Reinforcement Learning (RL). This allows to adjust the
model to the actual discretization as it also incorporates the interaction
between the discretization and the model itself. This optimization framework is
applied to both explicit and implicit closure models. An element-local eddy
viscosity model is optimized as the explicit model. For the implicit modeling,
RL is applied to identify an optimal blending strategy for a hybrid
discontinuous Galerkin (DG) and finite volume scheme. All newly derived models
achieve accurate and consistent results, either matching or outperforming
classical state-of-the-art models for different discretizations and
resolutions. Moreover, the explicit model is demonstrated to adapt its
distribution of viscosity within the DG elements to the inhomogeneous
discretization properties of the operator. In the implicit case, the optimized
hybrid scheme renders itself as a viable modeling ansatz that could initiate a
new class of high order schemes for compressible turbulence. Overall, the
results demonstrate that the proposed RL optimization can provide
discretization-consistent closures that could reduce the uncertainty in
implicitly filtered LES. | [
"Andrea Beck",
"Marius Kurz"
] | 2023-09-12 14:20:12 | http://arxiv.org/abs/2309.06260v1 | http://arxiv.org/pdf/2309.06260v1 | 2309.06260v1 |
Speciality vs Generality: An Empirical Study on Catastrophic Forgetting in Fine-tuning Foundation Models | Foundation models, including Vision Language Models (VLMs) and Large Language
Models (LLMs), possess the $generality$ to handle diverse distributions and
tasks, which stems from their extensive pre-training datasets. The fine-tuning
of foundation models is a common practice to enhance task performance or align
the model's behavior with human expectations, allowing them to gain
$speciality$. However, the small datasets used for fine-tuning may not
adequately cover the diverse distributions and tasks encountered during
pre-training. Consequently, the pursuit of speciality during fine-tuning can
lead to a loss of {generality} in the model, which is related to catastrophic
forgetting (CF) in deep learning. In this study, we demonstrate this phenomenon
in both VLMs and LLMs. For instance, fine-tuning VLMs like CLIP on ImageNet
results in a loss of generality in handling diverse distributions, and
fine-tuning LLMs like Galactica in the medical domain leads to a loss in
following instructions and common sense.
To address the trade-off between the speciality and generality, we
investigate multiple regularization methods from continual learning, the weight
averaging method (Wise-FT) from out-of-distributional (OOD) generalization,
which interpolates parameters between pre-trained and fine-tuned models, and
parameter-efficient fine-tuning methods like Low-Rank Adaptation (LoRA). Our
findings show that both continual learning and Wise-ft methods effectively
mitigate the loss of generality, with Wise-FT exhibiting the strongest
performance in balancing speciality and generality. | [
"Yong Lin",
"Lu Tan",
"Hangyu Lin",
"Zeming Zheng",
"Renjie Pi",
"Jipeng Zhang",
"Shizhe Diao",
"Haoxiang Wang",
"Han Zhao",
"Yuan Yao",
"Tong Zhang"
] | 2023-09-12 14:16:54 | http://arxiv.org/abs/2309.06256v2 | http://arxiv.org/pdf/2309.06256v2 | 2309.06256v2 |
Enhancing Multi-modal Cooperation via Fine-grained Modality Valuation | One primary topic of multi-modal learning is to jointly incorporate
heterogeneous information from different modalities. However, most models often
suffer from unsatisfactory multi-modal cooperation, which could not jointly
utilize all modalities well. Some methods are proposed to identify and enhance
the worse learnt modality, but are often hard to provide the fine-grained
observation of multi-modal cooperation at sample-level with theoretical
support. Hence, it is essential to reasonably observe and improve the
fine-grained cooperation between modalities, especially when facing realistic
scenarios where the modality discrepancy could vary across different samples.
To this end, we introduce a fine-grained modality valuation metric to evaluate
the contribution of each modality at sample-level. Via modality valuation, we
regretfully observe that the multi-modal model tends to rely on one specific
modality, resulting in other modalities being low-contributing. We further
analyze this issue and improve cooperation between modalities by enhancing the
discriminative ability of low-contributing modalities in a targeted manner.
Overall, our methods reasonably observe the fine-grained uni-modal contribution
at sample-level and achieve considerable improvement on different multi-modal
models. | [
"Yake Wei",
"Ruoxuan Feng",
"Zihe Wang",
"Di Hu"
] | 2023-09-12 14:16:34 | http://arxiv.org/abs/2309.06255v1 | http://arxiv.org/pdf/2309.06255v1 | 2309.06255v1 |
Rethinking Evaluation Metric for Probability Estimation Models Using Esports Data | Probability estimation models play an important role in various fields, such
as weather forecasting, recommendation systems, and sports analysis. Among
several models estimating probabilities, it is difficult to evaluate which
model gives reliable probabilities since the ground-truth probabilities are not
available. The win probability estimation model for esports, which calculates
the win probability under a certain game state, is also one of the fields being
actively studied in probability estimation. However, most of the previous works
evaluated their models using accuracy, a metric that only can measure the
performance of discrimination. In this work, we firstly investigate the Brier
score and the Expected Calibration Error (ECE) as a replacement of accuracy
used as a performance evaluation metric for win probability estimation models
in esports field. Based on the analysis, we propose a novel metric called
Balance score which is a simple yet effective metric in terms of six good
properties that probability estimation metric should have. Under the general
condition, we also found that the Balance score can be an effective
approximation of the true expected calibration error which has been imperfectly
approximated by ECE using the binning technique. Extensive evaluations using
simulation studies and real game snapshot data demonstrate the promising
potential to adopt the proposed metric not only for the win probability
estimation model for esports but also for evaluating general probability
estimation models. | [
"Euihyeon Choi",
"Jooyoung Kim",
"Wonkyung Lee"
] | 2023-09-12 14:04:12 | http://arxiv.org/abs/2309.06248v1 | http://arxiv.org/pdf/2309.06248v1 | 2309.06248v1 |
Consistency and adaptivity are complementary targets for the validation of variance-based uncertainty quantification metrics in machine learning regression tasks | Reliable uncertainty quantification (UQ) in machine learning (ML) regression
tasks is becoming the focus of many studies in materials and chemical science.
It is now well understood that average calibration is insufficient, and most
studies implement additional methods testing the conditional calibration with
respect to uncertainty, i.e. consistency. Consistency is assessed mostly by
so-called reliability diagrams. There exists however another way beyond average
calibration, which is conditional calibration with respect to input features,
i.e. adaptivity. In practice, adaptivity is the main concern of the final users
of a ML-UQ method, seeking for the reliability of predictions and uncertainties
for any point in features space. This article aims to show that consistency and
adaptivity are complementary validation targets, and that a good consistency
does not imply a good adaptivity. Adapted validation methods are proposed and
illustrated on a representative example. | [
"Pascal Pernot"
] | 2023-09-12 13:58:04 | http://arxiv.org/abs/2309.06240v1 | http://arxiv.org/pdf/2309.06240v1 | 2309.06240v1 |
Risk-Aware Reinforcement Learning through Optimal Transport Theory | In the dynamic and uncertain environments where reinforcement learning (RL)
operates, risk management becomes a crucial factor in ensuring reliable
decision-making. Traditional RL approaches, while effective in reward
optimization, often overlook the landscape of potential risks. In response,
this paper pioneers the integration of Optimal Transport (OT) theory with RL to
create a risk-aware framework. Our approach modifies the objective function,
ensuring that the resulting policy not only maximizes expected rewards but also
respects risk constraints dictated by OT distances between state visitation
distributions and the desired risk profiles. By leveraging the mathematical
precision of OT, we offer a formulation that elevates risk considerations
alongside conventional RL objectives. Our contributions are substantiated with
a series of theorems, mapping the relationships between risk distributions,
optimal value functions, and policy behaviors. Through the lens of OT, this
work illuminates a promising direction for RL, ensuring a balanced fusion of
reward pursuit and risk awareness. | [
"Ali Baheri"
] | 2023-09-12 13:55:01 | http://arxiv.org/abs/2309.06239v1 | http://arxiv.org/pdf/2309.06239v1 | 2309.06239v1 |
The first step is the hardest: Pitfalls of Representing and Tokenizing Temporal Data for Large Language Models | Large Language Models (LLMs) have demonstrated remarkable generalization
across diverse tasks, leading individuals to increasingly use them as personal
assistants and universal computing engines. Nevertheless, a notable obstacle
emerges when feeding numerical/temporal data into these models, such as data
sourced from wearables or electronic health records. LLMs employ tokenizers in
their input that break down text into smaller units. However, tokenizers are
not designed to represent numerical values and might struggle to understand
repetitive patterns and context, treating consecutive values as separate tokens
and disregarding their temporal relationships. Here, we discuss recent works
that employ LLMs for human-centric tasks such as in mobile health sensing and
present a case study showing that popular LLMs tokenize temporal data
incorrectly. To address that, we highlight potential solutions such as prompt
tuning with lightweight embedding layers as well as multimodal adapters, that
can help bridge this "modality gap". While the capability of language models to
generalize to other modalities with minimal or no finetuning is exciting, this
paper underscores the fact that their outputs cannot be meaningful if they
stumble over input nuances. | [
"Dimitris Spathis",
"Fahim Kawsar"
] | 2023-09-12 13:51:29 | http://arxiv.org/abs/2309.06236v1 | http://arxiv.org/pdf/2309.06236v1 | 2309.06236v1 |
A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Models | Analysis of high-dimensional data has led to increased interest in both
single index models (SIMs) and best subset selection. SIMs provide an
interpretable and flexible modeling framework for high-dimensional data, while
best subset selection aims to find a sparse model from a large set of
predictors. However, best subset selection in high-dimensional models is known
to be computationally intractable. Existing methods tend to relax the
selection, but do not yield the best subset solution. In this paper, we
directly tackle the intractability by proposing the first provably scalable
algorithm for best subset selection in high-dimensional SIMs. Our algorithmic
solution enjoys the subset selection consistency and has the oracle property
with a high probability. The algorithm comprises a generalized information
criterion to determine the support size of the regression coefficients,
eliminating the model selection tuning. Moreover, our method does not assume an
error distribution or a specific link function and hence is flexible to apply.
Extensive simulation results demonstrate that our method is not only
computationally efficient but also able to exactly recover the best subset in
various settings (e.g., linear regression, Poisson regression, heteroscedastic
models). | [
"Borui Tang",
"Jin Zhu",
"Junxian Zhu",
"Xueqin Wang",
"Heping Zhang"
] | 2023-09-12 13:48:06 | http://arxiv.org/abs/2309.06230v1 | http://arxiv.org/pdf/2309.06230v1 | 2309.06230v1 |
Evaluating Dynamic Topic Models | There is a lack of quantitative measures to evaluate the progression of
topics through time in dynamic topic models (DTMs). Filling this gap, we
propose a novel evaluation measure for DTMs that analyzes the changes in the
quality of each topic over time. Additionally, we propose an extension
combining topic quality with the model's temporal consistency. We demonstrate
the utility of the proposed measure by applying it to synthetic data and data
from existing DTMs. We also conducted a human evaluation, which indicates that
the proposed measure correlates well with human judgment. Our findings may help
in identifying changing topics, evaluating different DTMs, and guiding future
research in this area. | [
"Charu James",
"Mayank Nagda",
"Nooshin Haji Ghassemi",
"Marius Kloft",
"Sophie Fellenz"
] | 2023-09-12 13:30:25 | http://arxiv.org/abs/2309.08627v1 | http://arxiv.org/pdf/2309.08627v1 | 2309.08627v1 |
Long-term drought prediction using deep neural networks based on geospatial weather data | The accurate prediction of drought probability in specific regions is crucial
for informed decision-making in agricultural practices. It is important to make
predictions one year in advance, particularly for long-term decisions. However,
forecasting this probability presents challenges due to the complex interplay
of various factors within the region of interest and neighboring areas. In this
study, we propose an end-to-end solution to address this issue based on various
spatiotemporal neural networks. The models considered focus on predicting the
drought intensity based on the Palmer Drought Severity Index (PDSI) for
subregions of interest, leveraging intrinsic factors and insights from climate
models to enhance drought predictions.
Comparative evaluations demonstrate the superior accuracy of Convolutional
LSTM (ConvLSTM) and transformer models compared to baseline gradient boosting
and logistic regression solutions. The two former models achieved impressive
ROC AUC scores from 0.90 to 0.70 for forecast horizons from one to six months,
outperforming baseline models. The transformer showed superiority for shorter
horizons, while ConvLSTM did so for longer horizons. Thus, we recommend
selecting the models accordingly for long-term drought forecasting.
To ensure the broad applicability of the considered models, we conduct
extensive validation across regions worldwide, considering different
environmental conditions. We also run several ablation and sensitivity studies
to challenge our findings and provide additional information on how to solve
the problem. | [
"Vsevolod Grabar",
"Alexander Marusov",
"Yury Maximov",
"Nazar Sotiriadi",
"Alexander Bulkin",
"Alexey Zaytsev"
] | 2023-09-12 13:28:06 | http://arxiv.org/abs/2309.06212v2 | http://arxiv.org/pdf/2309.06212v2 | 2309.06212v2 |
Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding | Solving linear inverse problems plays a crucial role in numerous
applications. Algorithm unfolding based, model-aware data-driven approaches
have gained significant attention for effectively addressing these problems.
Learned iterative soft-thresholding algorithm (LISTA) and alternating direction
method of multipliers compressive sensing network (ADMM-CSNet) are two widely
used such approaches, based on ISTA and ADMM algorithms, respectively. In this
work, we study optimization guarantees, i.e., achieving near-zero training loss
with the increase in the number of learning epochs, for finite-layer unfolded
networks such as LISTA and ADMM-CSNet with smooth soft-thresholding in an
over-parameterized (OP) regime. We achieve this by leveraging a modified
version of the Polyak-Lojasiewicz, denoted PL$^*$, condition. Satisfying the
PL$^*$ condition within a specific region of the loss landscape ensures the
existence of a global minimum and exponential convergence from initialization
using gradient descent based methods. Hence, we provide conditions, in terms of
the network width and the number of training samples, on these unfolded
networks for the PL$^*$ condition to hold. We achieve this by deriving the
Hessian spectral norm of these networks. Additionally, we show that the
threshold on the number of training samples increases with the increase in the
network width. Furthermore, we compare the threshold on training samples of
unfolded networks with that of a standard fully-connected feed-forward network
(FFNN) with smooth soft-thresholding non-linearity. We prove that unfolded
networks have a higher threshold value than FFNN. Consequently, one can expect
a better expected error for unfolded networks than FFNN. | [
"Shaik Basheeruddin Shah",
"Pradyumna Pradhan",
"Wei Pu",
"Ramunaidu Randhi",
"Miguel R. D. Rodrigues",
"Yonina C. Eldar"
] | 2023-09-12 13:03:47 | http://arxiv.org/abs/2309.06195v1 | http://arxiv.org/pdf/2309.06195v1 | 2309.06195v1 |
Assessing the Generalization Gap of Learning-Based Speech Enhancement Systems in Noisy and Reverberant Environments | The acoustic variability of noisy and reverberant speech mixtures is
influenced by multiple factors, such as the spectro-temporal characteristics of
the target speaker and the interfering noise, the signal-to-noise ratio (SNR)
and the room characteristics. This large variability poses a major challenge
for learning-based speech enhancement systems, since a mismatch between the
training and testing conditions can substantially reduce the performance of the
system. Generalization to unseen conditions is typically assessed by testing
the system with a new speech, noise or binaural room impulse response (BRIR)
database different from the one used during training. However, the difficulty
of the speech enhancement task can change across databases, which can
substantially influence the results. The present study introduces a
generalization assessment framework that uses a reference model trained on the
test condition, such that it can be used as a proxy for the difficulty of the
test condition. This allows to disentangle the effect of the change in task
difficulty from the effect of dealing with new data, and thus to define a new
measure of generalization performance termed the generalization gap. The
procedure is repeated in a cross-validation fashion by cycling through multiple
speech, noise, and BRIR databases to accurately estimate the generalization
gap. The proposed framework is applied to evaluate the generalization potential
of a feedforward neural network (FFNN), Conv-TasNet, DCCRN and MANNER. We find
that for all models, the performance degrades the most in speech mismatches,
while good noise and room generalization can be achieved by training on
multiple databases. Moreover, while recent models show higher performance in
matched conditions, their performance substantially decreases in mismatched
conditions and can become inferior to that of the FFNN-based system. | [
"Philippe Gonzalez",
"Tommy Sonne Alstrøm",
"Tobias May"
] | 2023-09-12 12:51:12 | http://arxiv.org/abs/2309.06183v1 | http://arxiv.org/pdf/2309.06183v1 | 2309.06183v1 |
Efficient Memory Management for Large Language Model Serving with PagedAttention | High throughput serving of large language models (LLMs) requires batching
sufficiently many requests at a time. However, existing systems struggle
because the key-value cache (KV cache) memory for each request is huge and
grows and shrinks dynamically. When managed inefficiently, this memory can be
significantly wasted by fragmentation and redundant duplication, limiting the
batch size. To address this problem, we propose PagedAttention, an attention
algorithm inspired by the classical virtual memory and paging techniques in
operating systems. On top of it, we build vLLM, an LLM serving system that
achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV
cache within and across requests to further reduce memory usage. Our
evaluations show that vLLM improves the throughput of popular LLMs by
2-4$\times$ with the same level of latency compared to the state-of-the-art
systems, such as FasterTransformer and Orca. The improvement is more pronounced
with longer sequences, larger models, and more complex decoding algorithms.
vLLM's source code is publicly available at
https://github.com/vllm-project/vllm | [
"Woosuk Kwon",
"Zhuohan Li",
"Siyuan Zhuang",
"Ying Sheng",
"Lianmin Zheng",
"Cody Hao Yu",
"Joseph E. Gonzalez",
"Hao Zhang",
"Ion Stoica"
] | 2023-09-12 12:50:04 | http://arxiv.org/abs/2309.06180v1 | http://arxiv.org/pdf/2309.06180v1 | 2309.06180v1 |
Elucidating the solution space of extended reverse-time SDE for diffusion models | Diffusion models (DMs) demonstrate potent image generation capabilities in
various generative modeling tasks. Nevertheless, their primary limitation lies
in slow sampling speed, requiring hundreds or thousands of sequential function
evaluations through large neural networks to generate high-quality images.
Sampling from DMs can be seen alternatively as solving corresponding stochastic
differential equations (SDEs) or ordinary differential equations (ODEs). In
this work, we formulate the sampling process as an extended reverse-time SDE
(ER SDE), unifying prior explorations into ODEs and SDEs. Leveraging the
semi-linear structure of ER SDE solutions, we offer exact solutions and
arbitrarily high-order approximate solutions for VP SDE and VE SDE,
respectively. Based on the solution space of the ER SDE, we yield mathematical
insights elucidating the superior performance of ODE solvers over SDE solvers
in terms of fast sampling. Additionally, we unveil that VP SDE solvers stand on
par with their VE SDE counterparts. Finally, we devise fast and training-free
samplers, ER-SDE-Solvers, achieving state-of-the-art performance across all
stochastic samplers. Experimental results demonstrate achieving 3.45 FID in 20
function evaluations and 2.24 FID in 50 function evaluations on the ImageNet
$64\times64$ dataset. | [
"Qinpeng Cui",
"Xinyi Zhang",
"Zongqing Lu",
"Qingmin Liao"
] | 2023-09-12 12:27:17 | http://arxiv.org/abs/2309.06169v2 | http://arxiv.org/pdf/2309.06169v2 | 2309.06169v2 |
Certified Robust Models with Slack Control and Large Lipschitz Constants | Despite recent success, state-of-the-art learning-based models remain highly
vulnerable to input changes such as adversarial examples. In order to obtain
certifiable robustness against such perturbations, recent work considers
Lipschitz-based regularizers or constraints while at the same time increasing
prediction margin. Unfortunately, this comes at the cost of significantly
decreased accuracy. In this paper, we propose a Calibrated Lipschitz-Margin
Loss (CLL) that addresses this issue and improves certified robustness by
tackling two problems: Firstly, commonly used margin losses do not adjust the
penalties to the shrinking output distribution; caused by minimizing the
Lipschitz constant $K$. Secondly, and most importantly, we observe that
minimization of $K$ can lead to overly smooth decision functions. This limits
the model's complexity and thus reduces accuracy. Our CLL addresses these
issues by explicitly calibrating the loss w.r.t. margin and Lipschitz constant,
thereby establishing full control over slack and improving robustness
certificates even with larger Lipschitz constants. On CIFAR-10, CIFAR-100 and
Tiny-ImageNet, our models consistently outperform losses that leave the
constant unattended. On CIFAR-100 and Tiny-ImageNet, CLL improves upon
state-of-the-art deterministic $L_2$ robust accuracies. In contrast to current
trends, we unlock potential of much smaller models without $K=1$ constraints. | [
"Max Losch",
"David Stutz",
"Bernt Schiele",
"Mario Fritz"
] | 2023-09-12 12:23:49 | http://arxiv.org/abs/2309.06166v1 | http://arxiv.org/pdf/2309.06166v1 | 2309.06166v1 |
Robust-MBDL: A Robust Multi-branch Deep Learning Based Model for Remaining Useful Life Prediction and Operational Condition Identification of Rotating Machines | In this paper, a Robust Multi-branch Deep learning-based system for remaining
useful life (RUL) prediction and condition operations (CO) identification of
rotating machines is proposed. In particular, the proposed system comprises
main components: (1) an LSTM-Autoencoder to denoise the vibration data; (2) a
feature extraction to generate time-domain, frequency-domain, and
time-frequency based features from the denoised data; (3) a novel and robust
multi-branch deep learning network architecture to exploit the multiple
features. The performance of our proposed system was evaluated and compared to
the state-of-the-art systems on two benchmark datasets of XJTU-SY and
PRONOSTIA. The experimental results prove that our proposed system outperforms
the state-of-the-art systems and presents potential for real-life applications
on bearing machines. | [
"Khoa Tran",
"Hai-Canh Vu",
"Lam Pham",
"Nassim Boudaoud"
] | 2023-09-12 11:58:53 | http://arxiv.org/abs/2309.06157v1 | http://arxiv.org/pdf/2309.06157v1 | 2309.06157v1 |
Towards Reliable Domain Generalization: A New Dataset and Evaluations | There are ubiquitous distribution shifts in the real world. However, deep
neural networks (DNNs) are easily biased towards the training set, which causes
severe performance degradation when they receive out-of-distribution data. Many
methods are studied to train models that generalize under various distribution
shifts in the literature of domain generalization (DG). However, the recent
DomainBed and WILDS benchmarks challenged the effectiveness of these methods.
Aiming at the problems in the existing research, we propose a new domain
generalization task for handwritten Chinese character recognition (HCCR) to
enrich the application scenarios of DG method research. We evaluate eighteen DG
methods on the proposed PaHCC (Printed and Handwritten Chinese Characters)
dataset and show that the performance of existing methods on this dataset is
still unsatisfactory. Besides, under a designed dynamic DG setting, we reveal
more properties of DG methods and argue that only the leave-one-domain-out
protocol is unreliable. We advocate that researchers in the DG community refer
to dynamic performance of methods for more comprehensive and reliable
evaluation. Our dataset and evaluations bring new perspectives to the community
for more substantial progress. We will make our dataset public with the article
published to facilitate the study of domain generalization. | [
"Jiao Zhang",
"Xu-Yao Zhang",
"Cheng-Lin Liu"
] | 2023-09-12 11:29:12 | http://arxiv.org/abs/2309.06142v1 | http://arxiv.org/pdf/2309.06142v1 | 2309.06142v1 |
Fingerprint Attack: Client De-Anonymization in Federated Learning | Federated Learning allows collaborative training without data sharing in
settings where participants do not trust the central server and one another.
Privacy can be further improved by ensuring that communication between the
participants and the server is anonymized through a shuffle; decoupling the
participant identity from their data. This paper seeks to examine whether such
a defense is adequate to guarantee anonymity, by proposing a novel
fingerprinting attack over gradients sent by the participants to the server. We
show that clustering of gradients can easily break the anonymization in an
empirical study of learning federated language models on two language corpora.
We then show that training with differential privacy can provide a practical
defense against our fingerprint attack. | [
"Qiongkai Xu",
"Trevor Cohn",
"Olga Ohrimenko"
] | 2023-09-12 11:10:30 | http://arxiv.org/abs/2310.05960v1 | http://arxiv.org/pdf/2310.05960v1 | 2310.05960v1 |
Accelerating Edge AI with Morpher: An Integrated Design, Compilation and Simulation Framework for CGRAs | Coarse-Grained Reconfigurable Arrays (CGRAs) hold great promise as
power-efficient edge accelerator, offering versatility beyond AI applications.
Morpher, an open-source, architecture-adaptive CGRA design framework, is
specifically designed to explore the vast design space of CGRAs. The
comprehensive ecosystem of Morpher includes a tailored compiler, simulator,
accelerator synthesis, and validation framework. This study provides an
overview of Morpher, highlighting its capabilities in automatically compiling
AI application kernels onto user-defined CGRA architectures and verifying their
functionality. Through the Morpher framework, the versatility of CGRAs is
harnessed to facilitate efficient compilation and verification of edge AI
applications, covering important kernels representative of a wide range of
embedded AI workloads. Morpher is available online at
https://github.com/ecolab-nus/morpher-v2. | [
"Dhananjaya Wijerathne",
"Zhaoying Li",
"Tulika Mitra"
] | 2023-09-12 11:06:01 | http://arxiv.org/abs/2309.06127v1 | http://arxiv.org/pdf/2309.06127v1 | 2309.06127v1 |
AstroLLaMA: Towards Specialized Foundation Models in Astronomy | Large language models excel in many human-language tasks but often falter in
highly specialized domains like scholarly astronomy. To bridge this gap, we
introduce AstroLLaMA, a 7-billion-parameter model fine-tuned from LLaMA-2 using
over 300,000 astronomy abstracts from arXiv. Optimized for traditional causal
language modeling, AstroLLaMA achieves a 30% lower perplexity than Llama-2,
showing marked domain adaptation. Our model generates more insightful and
scientifically relevant text completions and embedding extraction than
state-of-the-arts foundation models despite having significantly fewer
parameters. AstroLLaMA serves as a robust, domain-specific model with broad
fine-tuning potential. Its public release aims to spur astronomy-focused
research, including automatic paper summarization and conversational agent
development. | [
"Tuan Dung Nguyen",
"Yuan-Sen Ting",
"Ioana Ciucă",
"Charlie O'Neill",
"Ze-Chang Sun",
"Maja Jabłońska",
"Sandor Kruk",
"Ernest Perkowski",
"Jack Miller",
"Jason Li",
"Josh Peek",
"Kartheik Iyer",
"Tomasz Różański",
"Pranav Khetarpal",
"Sharaf Zaman",
"David Brodrick",
"Sergio J. Rodríguez Méndez",
"Thang Bui",
"Alyssa Goodman",
"Alberto Accomazzi",
"Jill Naiman",
"Jesse Cranney",
"Kevin Schawinski",
"UniverseTBD"
] | 2023-09-12 11:02:27 | http://arxiv.org/abs/2309.06126v1 | http://arxiv.org/pdf/2309.06126v1 | 2309.06126v1 |
Automating global landslide detection with heterogeneous ensemble deep-learning classification | With changing climatic conditions, we are already seeing an increase in
extreme weather events and their secondary consequences, including landslides.
Landslides threaten infrastructure, including roads, railways, buildings, and
human life. Hazard-based spatial planning and early warning systems are
cost-effective strategies to reduce the risk to society from landslides.
However, these both rely on data from previous landslide events, which is often
scarce. Many deep learning (DL) models have recently been applied for landside
mapping using medium- to high-resolution satellite images as input. However,
they often suffer from sensitivity problems, overfitting, and low mapping
accuracy. This study addresses some of these limitations by using a diverse
global landslide dataset, using different segmentation models, such as Unet,
Linknet, PSP-Net, PAN, and DeepLab and based on their performances, building an
ensemble model. The ensemble model achieved the highest F1-score (0.69) when
combining both Sentinel-1 and Sentinel-2 bands, with the highest average
improvement of 6.87 % when the ensemble size was 20. On the other hand,
Sentinel-2 bands only performed very well, with an F1 score of 0.61 when the
ensemble size is 20 with an improvement of 14.59 % when the ensemble size is
20. This result shows considerable potential in building a robust and reliable
monitoring system based on changes in vegetation index dNDVI only. | [
"Alexandra Jarna Ganerød",
"Gabriele Franch",
"Erin Lindsay",
"Martina Calovi"
] | 2023-09-12 10:56:16 | http://arxiv.org/abs/2310.05959v1 | http://arxiv.org/pdf/2310.05959v1 | 2310.05959v1 |
A robust synthetic data generation framework for machine learning in High-Resolution Transmission Electron Microscopy (HRTEM) | Machine learning techniques are attractive options for developing
highly-accurate automated analysis tools for nanomaterials characterization,
including high-resolution transmission electron microscopy (HRTEM). However,
successfully implementing such machine learning tools can be difficult due to
the challenges in procuring sufficiently large, high-quality training datasets
from experiments. In this work, we introduce Construction Zone, a Python
package for rapidly generating complex nanoscale atomic structures, and develop
an end-to-end workflow for creating large simulated databases for training
neural networks. Construction Zone enables fast, systematic sampling of
realistic nanomaterial structures, and can be used as a random structure
generator for simulated databases, which is important for generating large,
diverse synthetic datasets. Using HRTEM imaging as an example, we train a
series of neural networks on various subsets of our simulated databases to
segment nanoparticles and holistically study the data curation process to
understand how various aspects of the curated simulated data -- including
simulation fidelity, the distribution of atomic structures, and the
distribution of imaging conditions -- affect model performance across several
experimental benchmarks. Using our results, we are able to achieve
state-of-the-art segmentation performance on experimental HRTEM images of
nanoparticles from several experimental benchmarks and, further, we discuss
robust strategies for consistently achieving high performance with machine
learning in experimental settings using purely synthetic data. | [
"Luis Rangel DaCosta",
"Katherine Sytwu",
"Catherine Groschner",
"Mary Scott"
] | 2023-09-12 10:44:15 | http://arxiv.org/abs/2309.06122v1 | http://arxiv.org/pdf/2309.06122v1 | 2309.06122v1 |
Overview of Human Activity Recognition Using Sensor Data | Human activity recognition (HAR) is an essential research field that has been
used in different applications including home and workplace automation,
security and surveillance as well as healthcare. Starting from conventional
machine learning methods to the recently developing deep learning techniques
and the Internet of things, significant contributions have been shown in the
HAR area in the last decade. Even though several review and survey studies have
been published, there is a lack of sensor-based HAR overview studies focusing
on summarising the usage of wearable sensors and smart home sensors data as
well as applications of HAR and deep learning techniques. Hence, we overview
sensor-based HAR, discuss several important applications that rely on HAR, and
highlight the most common machine learning methods that have been used for HAR.
Finally, several challenges of HAR are explored that should be addressed to
further improve the robustness of HAR. | [
"Rebeen Ali Hamad",
"Wai Lok Woo",
"Bo Wei",
"Longzhi Yang"
] | 2023-09-12 10:37:42 | http://arxiv.org/abs/2309.07170v1 | http://arxiv.org/pdf/2309.07170v1 | 2309.07170v1 |
Fidelity-Induced Interpretable Policy Extraction for Reinforcement Learning | Deep Reinforcement Learning (DRL) has achieved remarkable success in
sequential decision-making problems. However, existing DRL agents make
decisions in an opaque fashion, hindering the user from establishing trust and
scrutinizing weaknesses of the agents. While recent research has developed
Interpretable Policy Extraction (IPE) methods for explaining how an agent takes
actions, their explanations are often inconsistent with the agent's behavior
and thus, frequently fail to explain. To tackle this issue, we propose a novel
method, Fidelity-Induced Policy Extraction (FIPE). Specifically, we start by
analyzing the optimization mechanism of existing IPE methods, elaborating on
the issue of ignoring consistency while increasing cumulative rewards. We then
design a fidelity-induced mechanism by integrate a fidelity measurement into
the reinforcement learning feedback. We conduct experiments in the complex
control environment of StarCraft II, an arena typically avoided by current IPE
methods. The experiment results demonstrate that FIPE outperforms the baselines
in terms of interaction performance and consistency, meanwhile easy to
understand. | [
"Xiao Liu",
"Wubing Chen",
"Mao Tan"
] | 2023-09-12 10:03:32 | http://arxiv.org/abs/2309.06097v1 | http://arxiv.org/pdf/2309.06097v1 | 2309.06097v1 |
A General Verification Framework for Dynamical and Control Models via Certificate Synthesis | An emerging branch of control theory specialises in certificate learning,
concerning the specification of a desired (possibly complex) system behaviour
for an autonomous or control model, which is then analytically verified by
means of a function-based proof. However, the synthesis of controllers abiding
by these complex requirements is in general a non-trivial task and may elude
the most expert control engineers. This results in a need for automatic
techniques that are able to design controllers and to analyse a wide range of
elaborate specifications. In this paper, we provide a general framework to
encode system specifications and define corresponding certificates, and we
present an automated approach to formally synthesise controllers and
certificates. Our approach contributes to the broad field of safe learning for
control, exploiting the flexibility of neural networks to provide candidate
control and certificate functions, whilst using SMT-solvers to offer a formal
guarantee of correctness. We test our framework by developing a prototype
software tool, and assess its efficacy at verification via control and
certificate synthesis over a large and varied suite of benchmarks. | [
"Alec Edwards",
"Andrea Peruffo",
"Alessandro Abate"
] | 2023-09-12 09:37:26 | http://arxiv.org/abs/2309.06090v1 | http://arxiv.org/pdf/2309.06090v1 | 2309.06090v1 |
Measuring Catastrophic Forgetting in Cross-Lingual Transfer Paradigms: Exploring Tuning Strategies | The cross-lingual transfer is a promising technique to solve tasks in
less-resourced languages. In this empirical study, we compare two fine-tuning
approaches combined with zero-shot and full-shot learning approaches for large
language models in a cross-lingual setting. As fine-tuning strategies, we
compare parameter-efficient adapter methods with fine-tuning of all parameters.
As cross-lingual transfer strategies, we compare the intermediate-training
(\textit{IT}) that uses each language sequentially and cross-lingual validation
(\textit{CLV}) that uses a target language already in the validation phase of
fine-tuning. We assess the success of transfer and the extent of catastrophic
forgetting in a source language due to cross-lingual transfer, i.e., how much
previously acquired knowledge is lost when we learn new information in a
different language. The results on two different classification problems, hate
speech detection and product reviews, each containing datasets in several
languages, show that the \textit{IT} cross-lingual strategy outperforms
\textit{CLV} for the target language. Our findings indicate that, in the
majority of cases, the \textit{CLV} strategy demonstrates superior retention of
knowledge in the base language (English) compared to the \textit{IT} strategy,
when evaluating catastrophic forgetting in multiple cross-lingual transfers. | [
"Boshko Koloski",
"Blaž Škrlj",
"Marko Robnik-Šikonja",
"Senja Pollak"
] | 2023-09-12 09:37:08 | http://arxiv.org/abs/2309.06089v1 | http://arxiv.org/pdf/2309.06089v1 | 2309.06089v1 |
Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning | Continuous unsupervised representation learning (CURL) research has greatly
benefited from improvements in self-supervised learning (SSL) techniques. As a
result, existing CURL methods using SSL can learn high-quality representations
without any labels, but with a notable performance drop when learning on a
many-tasks data stream. We hypothesize that this is caused by the
regularization losses that are imposed to prevent forgetting, leading to a
suboptimal plasticity-stability trade-off: they either do not adapt fully to
the incoming data (low plasticity), or incur significant forgetting when
allowed to fully adapt to a new SSL pretext-task (low stability). In this work,
we propose to train an expert network that is relieved of the duty of keeping
the previous knowledge and can focus on performing optimally on the new tasks
(optimizing plasticity). In the second phase, we combine this new knowledge
with the previous network in an adaptation-retrospection phase to avoid
forgetting and initialize a new expert with the knowledge of the old network.
We perform several experiments showing that our proposed approach outperforms
other CURL exemplar-free methods in few- and many-task split settings.
Furthermore, we show how to adapt our approach to semi-supervised continual
learning (Semi-SCL) and show that we surpass the accuracy of other
exemplar-free Semi-SCL methods and reach the results of some others that use
exemplars. | [
"Alex Gomez-Villa",
"Bartlomiej Twardowski",
"Kai Wang",
"Joost van de Weijer"
] | 2023-09-12 09:31:34 | http://arxiv.org/abs/2309.06086v1 | http://arxiv.org/pdf/2309.06086v1 | 2309.06086v1 |
A Machine Learning Framework to Deconstruct the Primary Drivers for Electricity Market Price Events | Power grids are moving towards 100% renewable energy source bulk power grids,
and the overall dynamics of power system operations and electricity markets are
changing. The electricity markets are not only dispatching resources
economically but also taking into account various controllable actions like
renewable curtailment, transmission congestion mitigation, and energy storage
optimization to ensure grid reliability. As a result, price formations in
electricity markets have become quite complex. Traditional root cause analysis
and statistical approaches are rendered inapplicable to analyze and infer the
main drivers behind price formation in the modern grid and markets with
variable renewable energy (VRE). In this paper, we propose a machine
learning-based analysis framework to deconstruct the primary drivers for price
spike events in modern electricity markets with high renewable energy. The
outcomes can be utilized for various critical aspects of market design,
renewable dispatch and curtailment, operations, and cyber-security
applications. The framework can be applied to any ISO or market data; however,
in this paper, it is applied to open-source publicly available datasets from
California Independent System Operator (CAISO) and ISO New England (ISO-NE). | [
"Milan Jain",
"Xueqing Sun",
"Sohom Datta",
"Abhishek Somani"
] | 2023-09-12 09:24:21 | http://arxiv.org/abs/2309.06082v1 | http://arxiv.org/pdf/2309.06082v1 | 2309.06082v1 |
Information Flow in Graph Neural Networks: A Clinical Triage Use Case | Graph Neural Networks (GNNs) have gained popularity in healthcare and other
domains due to their ability to process multi-modal and multi-relational
graphs. However, efficient training of GNNs remains challenging, with several
open research questions. In this paper, we investigate how the flow of
embedding information within GNNs affects the prediction of links in Knowledge
Graphs (KGs). Specifically, we propose a mathematical model that decouples the
GNN connectivity from the connectivity of the graph data and evaluate the
performance of GNNs in a clinical triage use case. Our results demonstrate that
incorporating domain knowledge into the GNN connectivity leads to better
performance than using the same connectivity as the KG or allowing
unconstrained embedding propagation. Moreover, we show that negative edges play
a crucial role in achieving good predictions, and that using too many GNN
layers can degrade performance. | [
"Víctor Valls",
"Mykhaylo Zayats",
"Alessandra Pascale"
] | 2023-09-12 09:18:12 | http://arxiv.org/abs/2309.06081v1 | http://arxiv.org/pdf/2309.06081v1 | 2309.06081v1 |
A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation | We present a semi-supervised domain adaptation framework for brain vessel
segmentation from different image modalities. Existing state-of-the-art methods
focus on a single modality, despite the wide range of available cerebrovascular
imaging techniques. This can lead to significant distribution shifts that
negatively impact the generalization across modalities. By relying on annotated
angiographies and a limited number of annotated venographies, our framework
accomplishes image-to-image translation and semantic segmentation, leveraging a
disentangled and semantically rich latent space to represent heterogeneous data
and perform image-level adaptation from source to target domains. Moreover, we
reduce the typical complexity of cycle-based architectures and minimize the use
of adversarial training, which allows us to build an efficient and intuitive
model with stable training. We evaluate our method on magnetic resonance
angiographies and venographies. While achieving state-of-the-art performance in
the source domain, our method attains a Dice score coefficient in the target
domain that is only 8.9% lower, highlighting its promising potential for robust
cerebrovascular image segmentation across different modalities. | [
"Francesco Galati",
"Daniele Falcetta",
"Rosa Cortese",
"Barbara Casolla",
"Ferran Prados",
"Ninon Burgos",
"Maria A. Zuluaga"
] | 2023-09-12 09:12:37 | http://arxiv.org/abs/2309.06075v1 | http://arxiv.org/pdf/2309.06075v1 | 2309.06075v1 |
Selection of contributing factors for predicting landslide susceptibility using machine learning and deep learning models | Landslides are a common natural disaster that can cause casualties, property
safety threats and economic losses. Therefore, it is important to understand or
predict the probability of landslide occurrence at potentially risky sites. A
commonly used means is to carry out a landslide susceptibility assessment based
on a landslide inventory and a set of landslide contributing factors. This can
be readily achieved using machine learning (ML) models such as logistic
regression (LR), support vector machine (SVM), random forest (RF), extreme
gradient boosting (Xgboost), or deep learning (DL) models such as convolutional
neural network (CNN) and long short time memory (LSTM). As the input data for
these models, landslide contributing factors have varying influences on
landslide occurrence. Therefore, it is logically feasible to select more
important contributing factors and eliminate less relevant ones, with the aim
of increasing the prediction accuracy of these models. However, selecting more
important factors is still a challenging task and there is no generally
accepted method. Furthermore, the effects of factor selection using various
methods on the prediction accuracy of ML and DL models are unclear. In this
study, the impact of the selection of contributing factors on the accuracy of
landslide susceptibility predictions using ML and DL models was investigated.
Four methods for selecting contributing factors were considered for all the
aforementioned ML and DL models, which included Information Gain Ratio (IGR),
Recursive Feature Elimination (RFE), Particle Swarm Optimization (PSO), Least
Absolute Shrinkage and Selection Operators (LASSO) and Harris Hawk Optimization
(HHO). In addition, autoencoder-based factor selection methods for DL models
were also investigated. To assess their performances, an exhaustive approach
was adopted,... | [
"Cheng Chen",
"Lei Fan"
] | 2023-09-12 09:00:17 | http://arxiv.org/abs/2309.06062v2 | http://arxiv.org/pdf/2309.06062v2 | 2309.06062v2 |
Verifiable Fairness: Privacy-preserving Computation of Fairness for Machine Learning Systems | Fair machine learning is a thriving and vibrant research topic. In this
paper, we propose Fairness as a Service (FaaS), a secure, verifiable and
privacy-preserving protocol to computes and verify the fairness of any machine
learning (ML) model. In the deisgn of FaaS, the data and outcomes are
represented through cryptograms to ensure privacy. Also, zero knowledge proofs
guarantee the well-formedness of the cryptograms and underlying data. FaaS is
model--agnostic and can support various fairness metrics; hence, it can be used
as a service to audit the fairness of any ML model. Our solution requires no
trusted third party or private channels for the computation of the fairness
metric. The security guarantees and commitments are implemented in a way that
every step is securely transparent and verifiable from the start to the end of
the process. The cryptograms of all input data are publicly available for
everyone, e.g., auditors, social activists and experts, to verify the
correctness of the process. We implemented FaaS to investigate performance and
demonstrate the successful use of FaaS for a publicly available data set with
thousands of entries. | [
"Ehsan Toreini",
"Maryam Mehrnezhad",
"Aad van Moorsel"
] | 2023-09-12 09:00:03 | http://arxiv.org/abs/2309.06061v1 | http://arxiv.org/pdf/2309.06061v1 | 2309.06061v1 |
How does representation impact in-context learning: A exploration on a synthetic task | In-context learning, i.e., learning from in-context samples, is an impressive
ability of Transformer. However, the mechanism driving the in-context learning
is not yet fully understood. In this study, we aim to investigate from an
underexplored perspective of representation learning. The representation is
more complex for in-context learning senario, where the representation can be
impacted by both model weights and in-context samples. We refer the above two
conceptually aspects of representation as in-weight component and in-context
component, respectively. To study how the two components affect in-context
learning capabilities, we construct a novel synthetic task, making it possible
to device two probes, in-weights probe and in-context probe, to evaluate the
two components, respectively. We demonstrate that the goodness of in-context
component is highly related to the in-context learning performance, which
indicates the entanglement between in-context learning and representation
learning. Furthermore, we find that a good in-weights component can actually
benefit the learning of the in-context component, indicating that in-weights
learning should be the foundation of in-context learning. To further understand
the the in-context learning mechanism and importance of the in-weights
component, we proof by construction that a simple Transformer, which uses
pattern matching and copy-past mechanism to perform in-context learning, can
match the in-context learning performance with more complex, best tuned
Transformer under the perfect in-weights component assumption. In short, those
discoveries from representation learning perspective shed light on new
approaches to improve the in-context capacity. | [
"Jingwen Fu",
"Tao Yang",
"Yuwang Wang",
"Yan Lu",
"Nanning Zheng"
] | 2023-09-12 08:45:25 | http://arxiv.org/abs/2309.06054v1 | http://arxiv.org/pdf/2309.06054v1 | 2309.06054v1 |
Frequency Convergence of Complexon Shift Operators (Extended Version) | Topological Signal Processing (TSP) utilizes simplicial complexes to model
structures with higher order than vertices and edges. In this paper, we study
the transferability of TSP via a generalized higher-order version of graphon,
known as complexon. We recall the notion of a complexon as the limit of a
simplicial complex sequence. Inspired by the integral operator form of graphon
shift operators, we construct a marginal complexon and complexon shift operator
(CSO) according to components of all possible dimensions from the complexon. We
investigate the CSO's eigenvalues and eigenvectors, and relate them to a new
family of weighted adjacency matrices. We prove that when a simplicial complex
sequence converges to a complexon, the eigenvalues of the corresponding CSOs
converge to that of the limit complexon. This conclusion is further verified by
a numerical experiment. These results hint at learning transferability on large
simplicial complexes or simplicial complex sequences, which generalize the
graphon signal processing framework. | [
"Purui Zhang",
"Xingchao Jian",
"Feng Ji",
"Wee Peng Tay",
"Bihan Wen"
] | 2023-09-12 08:40:20 | http://arxiv.org/abs/2309.07169v2 | http://arxiv.org/pdf/2309.07169v2 | 2309.07169v2 |
A Perceptron-based Fine Approximation Technique for Linear Separation | This paper presents a novel online learning method that aims at finding a
separator hyperplane between data points labelled as either positive or
negative. Since weights and biases of artificial neurons can directly be
related to hyperplanes in high-dimensional spaces, the technique is applicable
to train perceptron-based binary classifiers in machine learning. In case of
large or imbalanced data sets, use of analytical or gradient-based solutions
can become prohibitive and impractical, where heuristics and approximation
techniques are still applicable. The proposed method is based on the Perceptron
algorithm, however, it tunes neuron weights in just the necessary extent during
searching the separator hyperplane. Due to an appropriate transformation of the
initial data set we need not to consider data labels, neither the bias term.
respectively, reducing separability to a one-class classification problem. The
presented method has proven converge; empirical results show that it can be
more efficient than the Perceptron algorithm, especially, when the size of the
data set exceeds data dimensionality. | [
"Ákos Hajnal"
] | 2023-09-12 08:35:24 | http://arxiv.org/abs/2309.06049v1 | http://arxiv.org/pdf/2309.06049v1 | 2309.06049v1 |
BatMan-CLR: Making Few-shots Meta-Learners Resilient Against Label Noise | The negative impact of label noise is well studied in classical supervised
learning yet remains an open research question in meta-learning. Meta-learners
aim to adapt to unseen learning tasks by learning a good initial model in
meta-training and consecutively fine-tuning it according to new tasks during
meta-testing. In this paper, we present the first extensive analysis of the
impact of varying levels of label noise on the performance of state-of-the-art
meta-learners, specifically gradient-based $N$-way $K$-shot learners. We show
that the accuracy of Reptile, iMAML, and foMAML drops by up to 42% on the
Omniglot and CifarFS datasets when meta-training is affected by label noise. To
strengthen the resilience against label noise, we propose two sampling
techniques, namely manifold (Man) and batch manifold (BatMan), which transform
the noisy supervised learners into semi-supervised ones to increase the utility
of noisy labels. We first construct manifold samples of $N$-way
$2$-contrastive-shot tasks through augmentation, learning the embedding via a
contrastive loss in meta-training, and then perform classification through
zeroing on the embedding in meta-testing. We show that our approach can
effectively mitigate the impact of meta-training label noise. Even with 60%
wrong labels \batman and \man can limit the meta-testing accuracy drop to
${2.5}$, ${9.4}$, ${1.1}$ percent points, respectively, with existing
meta-learners across the Omniglot, CifarFS, and MiniImagenet datasets. | [
"Jeroen M. Galjaard",
"Robert Birke",
"Juan Perez",
"Lydia Y. Chen"
] | 2023-09-12 08:30:35 | http://arxiv.org/abs/2309.06046v1 | http://arxiv.org/pdf/2309.06046v1 | 2309.06046v1 |
Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language Model | Sentence Representation Learning (SRL) is a fundamental task in Natural
Language Processing (NLP), with Contrastive learning of Sentence Embeddings
(CSE) as the mainstream technique due to its superior performance. An
intriguing phenomenon in CSE is the significant performance gap between
supervised and unsupervised methods, even when their sentence encoder and loss
function are the same. Previous works attribute this performance gap to
differences in two representation properties (alignment and uniformity).
However, alignment and uniformity only measure the results, which means they
cannot answer "What happens during the training process that leads to the
performance gap?" and "How can the performance gap be narrowed?". In this
paper, we conduct empirical experiments to answer these "What" and "How"
questions. We first answer the "What" question by thoroughly comparing the
behavior of supervised and unsupervised CSE during their respective training
processes. From the comparison, We observe a significant difference in fitting
difficulty. Thus, we introduce a metric, called Fitting Difficulty Increment
(FDI), to measure the fitting difficulty gap between the evaluation dataset and
the held-out training dataset, and use the metric to answer the "What"
question. Then, based on the insights gained from the "What" question, we
tackle the "How" question by increasing the fitting difficulty of the training
dataset. We achieve this by leveraging the In-Context Learning (ICL) capability
of the Large Language Model (LLM) to generate data that simulates complex
patterns. By utilizing the hierarchical patterns in the LLM-generated data, we
effectively narrow the gap between supervised and unsupervised CSE. | [
"Mingxin Li",
"Richong Zhang",
"Zhijie Nie",
"Yongyi Mao"
] | 2023-09-12 08:16:58 | http://arxiv.org/abs/2309.06453v1 | http://arxiv.org/pdf/2309.06453v1 | 2309.06453v1 |
Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning | Graph anomaly detection (GAD) has attracted increasing attention in machine
learning and data mining. Recent works have mainly focused on how to capture
richer information to improve the quality of node embeddings for GAD. Despite
their significant advances in detection performance, there is still a relative
dearth of research on the properties of the task. GAD aims to discern the
anomalies that deviate from most nodes. However, the model is prone to learn
the pattern of normal samples which make up the majority of samples. Meanwhile,
anomalies can be easily detected when their behaviors differ from normality.
Therefore, the performance can be further improved by enhancing the ability to
learn the normal pattern. To this end, we propose a normality learning-based
GAD framework via multi-scale contrastive learning networks (NLGAD for
abbreviation). Specifically, we first initialize the model with the contrastive
networks on different scales. To provide sufficient and reliable normal nodes
for normality learning, we design an effective hybrid strategy for normality
selection. Finally, the model is refined with the only input of reliable normal
nodes and learns a more accurate estimate of normality so that anomalous nodes
can be more easily distinguished. Eventually, extensive experiments on six
benchmark graph datasets demonstrate the effectiveness of our normality
learning-based scheme on GAD. Notably, the proposed algorithm improves the
detection performance (up to 5.89% AUC gain) compared with the state-of-the-art
methods. The source code is released at https://github.com/FelixDJC/NLGAD. | [
"Jingcan Duan",
"Pei Zhang",
"Siwei Wang",
"Jingtao Hu",
"Hu Jin",
"Jiaxin Zhang",
"Haifang Zhou",
"Xinwang Liu"
] | 2023-09-12 08:06:04 | http://arxiv.org/abs/2309.06034v2 | http://arxiv.org/pdf/2309.06034v2 | 2309.06034v2 |
Energy-Aware Federated Learning with Distributed User Sampling and Multichannel ALOHA | Distributed learning on edge devices has attracted increased attention with
the advent of federated learning (FL). Notably, edge devices often have limited
battery and heterogeneous energy availability, while multiple rounds are
required in FL for convergence, intensifying the need for energy efficiency.
Energy depletion may hinder the training process and the efficient utilization
of the trained model. To solve these problems, this letter considers the
integration of energy harvesting (EH) devices into a FL network with
multi-channel ALOHA, while proposing a method to ensure both low energy outage
probability and successful execution of future tasks. Numerical results
demonstrate the effectiveness of this method, particularly in critical setups
where the average energy income fails to cover the iteration cost. The method
outperforms a norm based solution in terms of convergence time and battery
level. | [
"Rafael Valente da Silva",
"Onel L. Alcaraz López",
"Richard Demo Souza"
] | 2023-09-12 08:05:39 | http://arxiv.org/abs/2309.06033v1 | http://arxiv.org/pdf/2309.06033v1 | 2309.06033v1 |
Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks | In different wireless network scenarios, multiple network entities need to
cooperate in order to achieve a common task with minimum delay and energy
consumption. Future wireless networks mandate exchanging high dimensional data
in dynamic and uncertain environments, therefore implementing communication
control tasks becomes challenging and highly complex. Multi-agent reinforcement
learning with emergent communication (EC-MARL) is a promising solution to
address high dimensional continuous control problems with partially observable
states in a cooperative fashion where agents build an emergent communication
protocol to solve complex tasks. This paper articulates the importance of
EC-MARL within the context of future 6G wireless networks, which imbues
autonomous decision-making capabilities into network entities to solve complex
tasks such as autonomous driving, robot navigation, flying base stations
network planning, and smart city applications. An overview of EC-MARL
algorithms and their design criteria are provided while presenting use cases
and research opportunities on this emerging topic. | [
"Marwa Chafii",
"Salmane Naoumi",
"Reda Alami",
"Ebtesam Almazrouei",
"Mehdi Bennis",
"Merouane Debbah"
] | 2023-09-12 07:40:53 | http://arxiv.org/abs/2309.06021v1 | http://arxiv.org/pdf/2309.06021v1 | 2309.06021v1 |
Interpolation, Approximation and Controllability of Deep Neural Networks | We investigate the expressive power of deep residual neural networks
idealized as continuous dynamical systems through control theory. Specifically,
we consider two properties that arise from supervised learning, namely
universal interpolation - the ability to match arbitrary input and target
training samples - and the closely related notion of universal approximation -
the ability to approximate input-target functional relationships via flow maps.
Under the assumption of affine invariance of the control family, we give a
characterisation of universal interpolation, showing that it holds for
essentially any architecture with non-linearity. Furthermore, we elucidate the
relationship between universal interpolation and universal approximation in the
context of general control systems, showing that the two properties cannot be
deduced from each other. At the same time, we identify conditions on the
control family and the target function that ensures the equivalence of the two
notions. | [
"Jingpu Cheng",
"Qianxiao Li",
"Ting Lin",
"Zuowei Shen"
] | 2023-09-12 07:29:47 | http://arxiv.org/abs/2309.06015v1 | http://arxiv.org/pdf/2309.06015v1 | 2309.06015v1 |
Goal Space Abstraction in Hierarchical Reinforcement Learning via Reachability Analysis | Open-ended learning benefits immensely from the use of symbolic methods for
goal representation as they offer ways to structure knowledge for efficient and
transferable learning. However, the existing Hierarchical Reinforcement
Learning (HRL) approaches relying on symbolic reasoning are often limited as
they require a manual goal representation. The challenge in autonomously
discovering a symbolic goal representation is that it must preserve critical
information, such as the environment dynamics. In this work, we propose a
developmental mechanism for subgoal discovery via an emergent representation
that abstracts (i.e., groups together) sets of environment states that have
similar roles in the task. We create a HRL algorithm that gradually learns this
representation along with the policies and evaluate it on navigation tasks to
show the learned representation is interpretable and results in data
efficiency. | [
"Mehdi Zadem",
"Sergio Mover",
"Sao Mai Nguyen"
] | 2023-09-12 06:53:11 | http://arxiv.org/abs/2309.07168v1 | http://arxiv.org/pdf/2309.07168v1 | 2309.07168v1 |
ATTA: Anomaly-aware Test-Time Adaptation for Out-of-Distribution Detection in Segmentation | Recent advancements in dense out-of-distribution (OOD) detection have
primarily focused on scenarios where the training and testing datasets share a
similar domain, with the assumption that no domain shift exists between them.
However, in real-world situations, domain shift often exits and significantly
affects the accuracy of existing out-of-distribution (OOD) detection models. In
this work, we propose a dual-level OOD detection framework to handle domain
shift and semantic shift jointly. The first level distinguishes whether domain
shift exists in the image by leveraging global low-level features, while the
second level identifies pixels with semantic shift by utilizing dense
high-level feature maps. In this way, we can selectively adapt the model to
unseen domains as well as enhance model's capacity in detecting novel classes.
We validate the efficacy of our proposed method on several OOD segmentation
benchmarks, including those with significant domain shifts and those without,
observing consistent performance improvements across various baseline models. | [
"Zhitong Gao",
"Shipeng Yan",
"Xuming He"
] | 2023-09-12 06:49:56 | http://arxiv.org/abs/2309.05994v1 | http://arxiv.org/pdf/2309.05994v1 | 2309.05994v1 |
Learning Unbiased News Article Representations: A Knowledge-Infused Approach | Quantification of the political leaning of online news articles can aid in
understanding the dynamics of political ideology in social groups and measures
to mitigating them. However, predicting the accurate political leaning of a
news article with machine learning models is a challenging task. This is due to
(i) the political ideology of a news article is defined by several factors, and
(ii) the innate nature of existing learning models to be biased with the
political bias of the news publisher during the model training. There is only a
limited number of methods to study the political leaning of news articles which
also do not consider the algorithmic political bias which lowers the
generalization of machine learning models to predict the political leaning of
news articles published by any new news publishers. In this work, we propose a
knowledge-infused deep learning model that utilizes relatively reliable
external data resources to learn unbiased representations of news articles
using their global and local contexts. We evaluate the proposed model by
setting the data in such a way that news domains or news publishers in the test
set are completely unseen during the training phase. With this setup we show
that the proposed model mitigates algorithmic political bias and outperforms
baseline methods to predict the political leaning of news articles with up to
73% accuracy. | [
"Sadia Kamal",
"Jimmy Hartford",
"Jeremy Willis",
"Arunkumar Bagavathi"
] | 2023-09-12 06:20:34 | http://arxiv.org/abs/2309.05981v1 | http://arxiv.org/pdf/2309.05981v1 | 2309.05981v1 |
CleanUNet 2: A Hybrid Speech Denoising Model on Waveform and Spectrogram | In this work, we present CleanUNet 2, a speech denoising model that combines
the advantages of waveform denoiser and spectrogram denoiser and achieves the
best of both worlds. CleanUNet 2 uses a two-stage framework inspired by popular
speech synthesis methods that consist of a waveform model and a spectrogram
model. Specifically, CleanUNet 2 builds upon CleanUNet, the state-of-the-art
waveform denoiser, and further boosts its performance by taking predicted
spectrograms from a spectrogram denoiser as the input. We demonstrate that
CleanUNet 2 outperforms previous methods in terms of various objective and
subjective evaluations. | [
"Zhifeng Kong",
"Wei Ping",
"Ambrish Dantrey",
"Bryan Catanzaro"
] | 2023-09-12 05:55:41 | http://arxiv.org/abs/2309.05975v1 | http://arxiv.org/pdf/2309.05975v1 | 2309.05975v1 |
Circuit Breaking: Removing Model Behaviors with Targeted Ablation | Language models often exhibit behaviors that improve performance on a
pre-training objective but harm performance on downstream tasks. We propose a
novel approach to removing undesirable behaviors by ablating a small number of
causal pathways between model components, with the intention of disabling the
computational circuit responsible for the bad behavior. Given a small dataset
of inputs where the model behaves poorly, we learn to ablate a small number of
important causal pathways. In the setting of reducing GPT-2 toxic language
generation, we find ablating just 12 of the 11.6K causal edges mitigates toxic
generation with minimal degradation of performance on other inputs. | [
"Maximilian Li",
"Xander Davies",
"Max Nadeau"
] | 2023-09-12 05:51:56 | http://arxiv.org/abs/2309.05973v1 | http://arxiv.org/pdf/2309.05973v1 | 2309.05973v1 |
Neural Network Layer Matrix Decomposition reveals Latent Manifold Encoding and Memory Capacity | We prove the converse of the universal approximation theorem, i.e. a neural
network (NN) encoding theorem which shows that for every stably converged NN of
continuous activation functions, its weight matrix actually encodes a
continuous function that approximates its training dataset to within a finite
margin of error over a bounded domain. We further show that using the
Eckart-Young theorem for truncated singular value decomposition of the weight
matrix for every NN layer, we can illuminate the nature of the latent space
manifold of the training dataset encoded and represented by every NN layer, and
the geometric nature of the mathematical operations performed by each NN layer.
Our results have implications for understanding how NNs break the curse of
dimensionality by harnessing memory capacity for expressivity, and that the two
are complementary. This Layer Matrix Decomposition (LMD) further suggests a
close relationship between eigen-decomposition of NN layers and the latest
advances in conceptualizations of Hopfield networks and Transformer NN models. | [
"Ng Shyh-Chang",
"A-Li Luo",
"Bo Qiu"
] | 2023-09-12 05:36:08 | http://arxiv.org/abs/2309.05968v1 | http://arxiv.org/pdf/2309.05968v1 | 2309.05968v1 |
Evaluating the Ebb and Flow: An In-depth Analysis of Question-Answering Trends across Diverse Platforms | Community Question Answering (CQA) platforms steadily gain popularity as they
provide users with fast responses to their queries. The swiftness of these
responses is contingent on a mixture of query-specific and user-related
elements. This paper scrutinizes these contributing factors within the context
of six highly popular CQA platforms, identified through their standout
answering speed. Our investigation reveals a correlation between the time taken
to yield the first response to a question and several variables: the metadata,
the formulation of the questions, and the level of interaction among users.
Additionally, by employing conventional machine learning models to analyze
these metadata and patterns of user interaction, we endeavor to predict which
queries will receive their initial responses promptly. | [
"Rima Hazra",
"Agnik Saha",
"Somnath Banerjee",
"Animesh Mukherjee"
] | 2023-09-12 05:03:28 | http://arxiv.org/abs/2309.05961v1 | http://arxiv.org/pdf/2309.05961v1 | 2309.05961v1 |
GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection | Logs play a crucial role in system monitoring and debugging by recording
valuable system information, including events and states. Although various
methods have been proposed to detect anomalies in log sequences, they often
overlook the significance of considering relations among system components,
such as services and users, which can be identified from log contents.
Understanding these relations is vital for detecting anomalies and their
underlying causes. To address this issue, we introduce GLAD, a Graph-based Log
Anomaly Detection framework designed to detect relational anomalies in system
logs. GLAD incorporates log semantics, relational patterns, and sequential
patterns into a unified framework for anomaly detection. Specifically, GLAD
first introduces a field extraction module that utilizes prompt-based few-shot
learning to identify essential fields from log contents. Then GLAD constructs
dynamic log graphs for sliding windows by interconnecting extracted fields and
log events parsed from the log parser. These graphs represent events and fields
as nodes and their relations as edges. Subsequently, GLAD utilizes a
temporal-attentive graph edge anomaly detection model for identifying anomalous
relations in these dynamic log graphs. This model employs a Graph Neural
Network (GNN)-based encoder enhanced with transformers to capture content,
structural and temporal features. We evaluate our proposed method on three
datasets, and the results demonstrate the effectiveness of GLAD in detecting
anomalies indicated by varying relational patterns. | [
"Yufei Li",
"Yanchi Liu",
"Haoyu Wang",
"Zhengzhang Chen",
"Wei Cheng",
"Yuncong Chen",
"Wenchao Yu",
"Haifeng Chen",
"Cong Liu"
] | 2023-09-12 04:21:30 | http://arxiv.org/abs/2309.05953v1 | http://arxiv.org/pdf/2309.05953v1 | 2309.05953v1 |
Language Models as Black-Box Optimizers for Vision-Language Models | Vision-language models (VLMs) pre-trained on web-scale datasets have
demonstrated remarkable capabilities across a variety of vision and multimodal
tasks. Currently, fine-tuning methods for VLMs mainly operate in a white-box
setting, requiring access to model parameters for backpropagation. However,
many VLMs rely on proprietary data and are not open-source, which restricts the
use of white-box approaches for fine-tuning. Given that popular private large
language models (LLMs) like ChatGPT still offer a language-based user
interface, we aim to develop a novel fine-tuning approach for VLMs through
natural language prompts, thereby avoiding the need to access model parameters,
feature embeddings, or output logits. In this setup, we propose employing
chat-based LLMs as black-box optimizers to search for the best text prompt on
the illustrative task of few-shot image classification using CLIP.
Specifically, we adopt an automatic "hill-climbing" procedure that converges on
an effective prompt by evaluating the accuracy of current prompts and asking
LLMs to refine them based on textual feedback, all within a conversational
process without human-in-the-loop. In a challenging 1-shot learning setup, our
simple approach surpasses the white-box continuous prompting method (CoOp) by
an average of 1.5% across 11 datasets including ImageNet. Our approach also
outperforms OpenAI's manually crafted prompts. Additionally, we highlight the
advantage of conversational feedback that incorporates both positive and
negative prompts, suggesting that LLMs can utilize the implicit "gradient"
direction in textual feedback for a more efficient search. Lastly, we find that
the text prompts generated through our strategy are not only more interpretable
but also transfer well across different CLIP architectures in a black-box
manner. | [
"Shihong Liu",
"Samuel Yu",
"Zhiqiu Lin",
"Deepak Pathak",
"Deva Ramanan"
] | 2023-09-12 04:03:41 | http://arxiv.org/abs/2309.05950v2 | http://arxiv.org/pdf/2309.05950v2 | 2309.05950v2 |
Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignals | Leveraging multimodal information from biosignals is vital for building a
comprehensive representation of people's physical and mental states. However,
multimodal biosignals often exhibit substantial distributional shifts between
pretraining and inference datasets, stemming from changes in task specification
or variations in modality compositions. To achieve effective pretraining in the
presence of potential distributional shifts, we propose a frequency-aware
masked autoencoder ($\texttt{bio}$FAME) that learns to parameterize the
representation of biosignals in the frequency space. $\texttt{bio}$FAME
incorporates a frequency-aware transformer, which leverages a fixed-size
Fourier-based operator for global token mixing, independent of the length and
sampling rate of inputs. To maintain the frequency components within each input
channel, we further employ a frequency-maintain pretraining strategy that
performs masked autoencoding in the latent space. The resulting architecture
effectively utilizes multimodal information during pretraining, and can be
seamlessly adapted to diverse tasks and modalities at test time, regardless of
input size and order. We evaluated our approach on a diverse set of transfer
experiments on unimodal time series, achieving an average of $\uparrow$5.5%
improvement in classification accuracy over the previous state-of-the-art.
Furthermore, we demonstrated that our architecture is robust in modality
mismatch scenarios, including unpredicted modality dropout or substitution,
proving its practical utility in real-world applications. Code will be
available soon. | [
"Ran Liu",
"Ellen L. Zippi",
"Hadi Pouransari",
"Chris Sandino",
"Jingping Nie",
"Hanlin Goh",
"Erdrin Azemi",
"Ali Moin"
] | 2023-09-12 02:59:26 | http://arxiv.org/abs/2309.05927v1 | http://arxiv.org/pdf/2309.05927v1 | 2309.05927v1 |
On Regularized Sparse Logistic Regression | Sparse logistic regression is for classification and feature selection
simultaneously. Although many studies have been done to solve
$\ell_1$-regularized logistic regression, there is no equivalently abundant
work on solving sparse logistic regression with nonconvex regularization term.
In this paper, we propose a unified framework to solve $\ell_1$-regularized
logistic regression, which can be naturally extended to nonconvex
regularization term, as long as certain requirement is satisfied. In addition,
we also utilize a different line search criteria to guarantee monotone
convergence for various regularization terms. Empirical experiments on binary
classification tasks with real-world datasets demonstrate our proposed
algorithms are capable of performing classification and feature selection
effectively at a lower computational cost. | [
"Mengyuan Zhang",
"Kai Liu"
] | 2023-09-12 02:52:40 | http://arxiv.org/abs/2309.05925v2 | http://arxiv.org/pdf/2309.05925v2 | 2309.05925v2 |
Backdoor Attack through Machine Unlearning | In recent years, the security issues of artificial intelligence have become
increasingly prominent due to the rapid development of deep learning research
and applications. Backdoor attack is an attack targeting the vulnerability of
deep learning models, where hidden backdoors are activated by triggers embedded
by the attacker, thereby outputting malicious predictions that may not align
with the intended output for a given input. In this work, we propose a novel
black-box backdoor attack based on machine unlearning. The attacker first
augments the training set with carefully designed samples, including poison and
mitigation data, to train a 'benign' model. Then, the attacker posts unlearning
requests for the mitigation samples to remove the impact of relevant data on
the model, gradually activating the hidden backdoor. Since backdoors are
implanted during the iterative unlearning process, it significantly increases
the computational overhead of existing defense methods for backdoor detection
or mitigation. To address this new security threat, we propose two methods for
detecting or mitigating such malicious unlearning requests. We conduct the
experiment in both naive unlearning and SISA settings. Experimental results
show that: 1) our attack can successfully implant backdoor into the model, and
sharding increases the difficulty of attack; 2) our detection algorithms are
effective in identifying the mitigation samples, while sharding reduces the
effectiveness of our detection algorithms. | [
"Peixin Zhang",
"Jun Sun",
"Mingtian Tan",
"Xinyu Wang"
] | 2023-09-12 02:42:39 | http://arxiv.org/abs/2310.10659v1 | http://arxiv.org/pdf/2310.10659v1 | 2310.10659v1 |
Quantized Non-Volatile Nanomagnetic Synapse based Autoencoder for Efficient Unsupervised Network Anomaly Detection | In the autoencoder based anomaly detection paradigm, implementing the
autoencoder in edge devices capable of learning in real-time is exceedingly
challenging due to limited hardware, energy, and computational resources. We
show that these limitations can be addressed by designing an autoencoder with
low-resolution non-volatile memory-based synapses and employing an effective
quantized neural network learning algorithm. We propose a ferromagnetic
racetrack with engineered notches hosting a magnetic domain wall (DW) as the
autoencoder synapses, where limited state (5-state) synaptic weights are
manipulated by spin orbit torque (SOT) current pulses. The performance of
anomaly detection of the proposed autoencoder model is evaluated on the NSL-KDD
dataset. Limited resolution and DW device stochasticity aware training of the
autoencoder is performed, which yields comparable anomaly detection performance
to the autoencoder having floating-point precision weights. While the limited
number of quantized states and the inherent stochastic nature of DW synaptic
weights in nanoscale devices are known to negatively impact the performance,
our hardware-aware training algorithm is shown to leverage these imperfect
device characteristics to generate an improvement in anomaly detection accuracy
(90.98%) compared to accuracy obtained with floating-point trained weights.
Furthermore, our DW-based approach demonstrates a remarkable reduction of at
least three orders of magnitude in weight updates during training compared to
the floating-point approach, implying substantial energy savings for our
method. This work could stimulate the development of extremely energy efficient
non-volatile multi-state synapse-based processors that can perform real-time
training and inference on the edge with unsupervised data. | [
"Muhammad Sabbir Alam",
"Walid Al Misba",
"Jayasimha Atulasimha"
] | 2023-09-12 02:29:09 | http://arxiv.org/abs/2309.06449v1 | http://arxiv.org/pdf/2309.06449v1 | 2309.06449v1 |
ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning | Decision Transformer (DT), which employs expressive sequence modeling
techniques to perform action generation, has emerged as a promising approach to
offline policy optimization. However, DT generates actions conditioned on a
desired future return, which is known to bear some weaknesses such as the
susceptibility to environmental stochasticity. To overcome DT's weaknesses, we
propose to empower DT with dynamic programming. Our method comprises three
steps. First, we employ in-sample value iteration to obtain approximated value
functions, which involves dynamic programming over the MDP structure. Second,
we evaluate action quality in context with estimated advantages. We introduce
two types of advantage estimators, IAE and GAE, which are suitable for
different tasks. Third, we train an Advantage-Conditioned Transformer (ACT) to
generate actions conditioned on the estimated advantages. Finally, during
testing, ACT generates actions conditioned on a desired advantage. Our
evaluation results validate that, by leveraging the power of dynamic
programming, ACT demonstrates effective trajectory stitching and robust action
generation in spite of the environmental stochasticity, outperforming baseline
methods across various benchmarks. Additionally, we conduct an in-depth
analysis of ACT's various design choices through ablation studies. | [
"Chenxiao Gao",
"Chenyang Wu",
"Mingjun Cao",
"Rui Kong",
"Zongzhang Zhang",
"Yang Yu"
] | 2023-09-12 02:05:43 | http://arxiv.org/abs/2309.05915v1 | http://arxiv.org/pdf/2309.05915v1 | 2309.05915v1 |
Adversarial Attacks Assessment of Salient Object Detection via Symbolic Learning | Machine learning is at the center of mainstream technology and outperforms
classical approaches to handcrafted feature design. Aside from its learning
process for artificial feature extraction, it has an end-to-end paradigm from
input to output, reaching outstandingly accurate results. However, security
concerns about its robustness to malicious and imperceptible perturbations have
drawn attention since its prediction can be changed entirely. Salient object
detection is a research area where deep convolutional neural networks have
proven effective but whose trustworthiness represents a significant issue
requiring analysis and solutions to hackers' attacks. Brain programming is a
kind of symbolic learning in the vein of good old-fashioned artificial
intelligence. This work provides evidence that symbolic learning robustness is
crucial in designing reliable visual attention systems since it can withstand
even the most intense perturbations. We test this evolutionary computation
methodology against several adversarial attacks and noise perturbations using
standard databases and a real-world problem of a shorebird called the Snowy
Plover portraying a visual attention task. We compare our methodology with five
different deep learning approaches, proving that they do not match the symbolic
paradigm regarding robustness. All neural networks suffer significant
performance losses, while brain programming stands its ground and remains
unaffected. Also, by studying the Snowy Plover, we remark on the importance of
security in surveillance activities regarding wildlife protection and
conservation. | [
"Gustavo Olague",
"Roberto Pineda",
"Gerardo Ibarra-Vazquez",
"Matthieu Olague",
"Axel Martinez",
"Sambit Bakshi",
"Jonathan Vargas",
"Isnardo Reducindo"
] | 2023-09-12 01:03:43 | http://arxiv.org/abs/2309.05900v1 | http://arxiv.org/pdf/2309.05900v1 | 2309.05900v1 |
Hierarchical Conditional Semi-Paired Image-to-Image Translation For Multi-Task Image Defect Correction On Shopping Websites | On shopping websites, product images of low quality negatively affect
customer experience. Although there are plenty of work in detecting images with
different defects, few efforts have been dedicated to correct those defects at
scale. A major challenge is that there are thousands of product types and each
has specific defects, therefore building defect specific models is unscalable.
In this paper, we propose a unified Image-to-Image (I2I) translation model to
correct multiple defects across different product types. Our model leverages an
attention mechanism to hierarchically incorporate high-level defect groups and
specific defect types to guide the network to focus on defect-related image
regions. Evaluated on eight public datasets, our model reduces the Frechet
Inception Distance (FID) by 24.6% in average compared with MoNCE, the
state-of-the-art I2I method. Unlike public data, another practical challenge on
shopping websites is that some paired images are of low quality. Therefore we
design our model to be semi-paired by combining the L1 loss of paired data with
the cycle loss of unpaired data. Tested on a shopping website dataset to
correct three image defects, our model reduces (FID) by 63.2% in average
compared with WS-I2I, the state-of-the art semi-paired I2I method. | [
"Moyan Li",
"Jinmiao Fu",
"Shaoyuan Xu",
"Huidong Liu",
"Jia Liu",
"Bryan Wang"
] | 2023-09-12 00:07:08 | http://arxiv.org/abs/2309.05883v1 | http://arxiv.org/pdf/2309.05883v1 | 2309.05883v1 |
Generalized Attacks on Face Verification Systems | Face verification (FV) using deep neural network models has made tremendous
progress in recent years, surpassing human accuracy and seeing deployment in
various applications such as border control and smartphone unlocking. However,
FV systems are vulnerable to Adversarial Attacks, which manipulate input images
to deceive these systems in ways usually unnoticeable to humans. This paper
provides an in-depth study of attacks on FV systems. We introduce the
DodgePersonation Attack that formulates the creation of face images that
impersonate a set of given identities while avoiding being identified as any of
the identities in a separate, disjoint set. A taxonomy is proposed to provide a
unified view of different types of Adversarial Attacks against FV systems,
including Dodging Attacks, Impersonation Attacks, and Master Face Attacks.
Finally, we propose the ''One Face to Rule Them All'' Attack which implements
the DodgePersonation Attack with state-of-the-art performance on a well-known
scenario (Master Face Attack) and which can also be used for the new scenarios
introduced in this paper. While the state-of-the-art Master Face Attack can
produce a set of 9 images to cover 43.82% of the identities in their test
database, with 9 images our attack can cover 57.27% to 58.5% of these
identifies while giving the attacker the choice of the identity to use to
create the impersonation. Moreover, the 9 generated attack images appear
identical to a casual observer. | [
"Ehsan Nazari",
"Paula Branco",
"Guy-Vincent Jourdan"
] | 2023-09-12 00:00:24 | http://arxiv.org/abs/2309.05879v1 | http://arxiv.org/pdf/2309.05879v1 | 2309.05879v1 |
Reaction coordinate flows for model reduction of molecular kinetics | In this work, we introduce a flow based machine learning approach, called
reaction coordinate (RC) flow, for discovery of low-dimensional kinetic models
of molecular systems. The RC flow utilizes a normalizing flow to design the
coordinate transformation and a Brownian dynamics model to approximate the
kinetics of RC, where all model parameters can be estimated in a data-driven
manner. In contrast to existing model reduction methods for molecular kinetics,
RC flow offers a trainable and tractable model of reduced kinetics in
continuous time and space due to the invertibility of the normalizing flow.
Furthermore, the Brownian dynamics-based reduced kinetic model investigated in
this work yields a readily discernible representation of metastable states
within the phase space of the molecular system. Numerical experiments
demonstrate how effectively the proposed method discovers interpretable and
accurate low-dimensional representations of given full-state kinetics from
simulations. | [
"Hao Wu",
"Frank Noé"
] | 2023-09-11 23:59:18 | http://arxiv.org/abs/2309.05878v1 | http://arxiv.org/pdf/2309.05878v1 | 2309.05878v1 |
Force-directed graph embedding with hops distance | Graph embedding has become an increasingly important technique for analyzing
graph-structured data. By representing nodes in a graph as vectors in a
low-dimensional space, graph embedding enables efficient graph processing and
analysis tasks like node classification, link prediction, and visualization. In
this paper, we propose a novel force-directed graph embedding method that
utilizes the steady acceleration kinetic formula to embed nodes in a way that
preserves graph topology and structural features. Our method simulates a set of
customized attractive and repulsive forces between all node pairs with respect
to their hop distance. These forces are then used in Newton's second law to
obtain the acceleration of each node. The method is intuitive, parallelizable,
and highly scalable. We evaluate our method on several graph analysis tasks and
show that it achieves competitive performance compared to state-of-the-art
unsupervised embedding techniques. | [
"Hamidreza Lotfalizadeh",
"Mohammad Al Hasan"
] | 2023-09-11 23:08:03 | http://arxiv.org/abs/2309.05865v1 | http://arxiv.org/pdf/2309.05865v1 | 2309.05865v1 |
The bionic neural network for external simulation of human locomotor system | Muscle forces and joint kinematics estimated with musculoskeletal (MSK)
modeling techniques offer useful metrics describing movement quality.
Model-based computational MSK models can interpret the dynamic interaction
between the neural drive to muscles, muscle dynamics, body and joint
kinematics, and kinetics. Still, such a set of solutions suffers from high
computational time and muscle recruitment problems, especially in complex
modeling. In recent years, data-driven methods have emerged as a promising
alternative due to the benefits of flexibility and adaptability. However, a
large amount of labeled training data is not easy to be acquired. This paper
proposes a physics-informed deep learning method based on MSK modeling to
predict joint motion and muscle forces. The MSK model is embedded into the
neural network as an ordinary differential equation (ODE) loss function with
physiological parameters of muscle activation dynamics and muscle contraction
dynamics to be identified. These parameters are automatically estimated during
the training process which guides the prediction of muscle forces combined with
the MSK forward dynamics model. Experimental validations on two groups of data,
including one benchmark dataset and one self-collected dataset from six healthy
subjects, are performed. The results demonstrate that the proposed deep
learning method can effectively identify subject-specific MSK physiological
parameters and the trained physics-informed forward-dynamics surrogate yields
accurate motion and muscle forces predictions. | [
"Yue Shi",
"Shuhao Ma",
"Yihui Zhao"
] | 2023-09-11 23:02:56 | http://arxiv.org/abs/2309.05863v1 | http://arxiv.org/pdf/2309.05863v1 | 2309.05863v1 |
Uncovering mesa-optimization algorithms in Transformers | Transformers have become the dominant model in deep learning, but the reason
for their superior performance is poorly understood. Here, we hypothesize that
the strong performance of Transformers stems from an architectural bias towards
mesa-optimization, a learned process running within the forward pass of a model
consisting of the following two steps: (i) the construction of an internal
learning objective, and (ii) its corresponding solution found through
optimization. To test this hypothesis, we reverse-engineer a series of
autoregressive Transformers trained on simple sequence modeling tasks,
uncovering underlying gradient-based mesa-optimization algorithms driving the
generation of predictions. Moreover, we show that the learned forward-pass
optimization algorithm can be immediately repurposed to solve supervised
few-shot tasks, suggesting that mesa-optimization might underlie the in-context
learning capabilities of large language models. Finally, we propose a novel
self-attention layer, the mesa-layer, that explicitly and efficiently solves
optimization problems specified in context. We find that this layer can lead to
improved performance in synthetic and preliminary language modeling
experiments, adding weight to our hypothesis that mesa-optimization is an
important operation hidden within the weights of trained Transformers. | [
"Johannes von Oswald",
"Eyvind Niklasson",
"Maximilian Schlegel",
"Seijin Kobayashi",
"Nicolas Zucchet",
"Nino Scherrer",
"Nolan Miller",
"Mark Sandler",
"Blaise Agüera y Arcas",
"Max Vladymyrov",
"Razvan Pascanu",
"João Sacramento"
] | 2023-09-11 22:42:50 | http://arxiv.org/abs/2309.05858v1 | http://arxiv.org/pdf/2309.05858v1 | 2309.05858v1 |
Instabilities in Convnets for Raw Audio | What makes waveform-based deep learning so hard? Despite numerous attempts at
training convolutional neural networks (convnets) for filterbank design, they
often fail to outperform hand-crafted baselines. These baselines are linear
time-invariant systems: as such, they can be approximated by convnets with wide
receptive fields. Yet, in practice, gradient-based optimization leads to
suboptimal approximations. In our article, we approach this phenomenon from the
perspective of initialization. We present a theory of large deviations for the
energy response of FIR filterbanks with random Gaussian weights. We find that
deviations worsen for large filters and locally periodic input signals, which
are both typical for audio signal processing applications. Numerical
simulations align with our theory and suggest that the condition number of a
convolutional layer follows a logarithmic scaling law between the number and
length of the filters, which is reminiscent of discrete wavelet bases. | [
"Daniel Haider",
"Vincent Lostanlen",
"Martin Ehler",
"Peter Balazs"
] | 2023-09-11 22:34:06 | http://arxiv.org/abs/2309.05855v2 | http://arxiv.org/pdf/2309.05855v2 | 2309.05855v2 |
ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation | The incredible capabilities of generative artificial intelligence models have
inevitably led to their application in the domain of drug discovery. It is
therefore of tremendous interest to develop methodologies that enhance the
abilities and applicability of these powerful tools. In this work, we present a
novel and efficient semi-supervised active learning methodology that allows for
the fine-tuning of a generative model with respect to an objective function by
strategically operating within a constructed representation of the sample
space. In the context of targeted molecular generation, we demonstrate the
ability to fine-tune a GPT-based molecular generator with respect to an
attractive interaction-based scoring function by strategically operating within
a chemical space proxy, thereby maximizing attractive interactions between the
generated molecules and a protein target. Importantly, our approach does not
require the individual evaluation of all data points that are used for
fine-tuning, enabling the incorporation of computationally expensive metrics.
We are hopeful that the inherent generality of this methodology ensures that it
will remain applicable as this exciting field evolves. To facilitate
implementation and reproducibility, we have made all of our software available
through the open-source ChemSpaceAL Python package. | [
"Gregory W. Kyro",
"Anton Morgunov",
"Rafael I. Brent",
"Victor S. Batista"
] | 2023-09-11 22:28:36 | http://arxiv.org/abs/2309.05853v1 | http://arxiv.org/pdf/2309.05853v1 | 2309.05853v1 |
Large Language Models for Compiler Optimization | We explore the novel application of Large Language Models to code
optimization. We present a 7B-parameter transformer model trained from scratch
to optimize LLVM assembly for code size. The model takes as input unoptimized
assembly and outputs a list of compiler options to best optimize the program.
Crucially, during training, we ask the model to predict the instruction counts
before and after optimization, and the optimized code itself. These auxiliary
learning tasks significantly improve the optimization performance of the model
and improve the model's depth of understanding.
We evaluate on a large suite of test programs. Our approach achieves a 3.0%
improvement in reducing instruction counts over the compiler, outperforming two
state-of-the-art baselines that require thousands of compilations. Furthermore,
the model shows surprisingly strong code reasoning abilities, generating
compilable code 91% of the time and perfectly emulating the output of the
compiler 70% of the time. | [
"Chris Cummins",
"Volker Seeker",
"Dejan Grubisic",
"Mostafa Elhoushi",
"Youwei Liang",
"Baptiste Roziere",
"Jonas Gehring",
"Fabian Gloeckle",
"Kim Hazelwood",
"Gabriel Synnaeve",
"Hugh Leather"
] | 2023-09-11 22:11:46 | http://arxiv.org/abs/2309.07062v1 | http://arxiv.org/pdf/2309.07062v1 | 2309.07062v1 |
Effective Abnormal Activity Detection on Multivariate Time Series Healthcare Data | Multivariate time series (MTS) data collected from multiple sensors provide
the potential for accurate abnormal activity detection in smart healthcare
scenarios. However, anomalies exhibit diverse patterns and become unnoticeable
in MTS data. Consequently, achieving accurate anomaly detection is challenging
since we have to capture both temporal dependencies of time series and
inter-relationships among variables. To address this problem, we propose a
Residual-based Anomaly Detection approach, Rs-AD, for effective representation
learning and abnormal activity detection. We evaluate our scheme on a
real-world gait dataset and the experimental results demonstrate an F1 score of
0.839. | [
"Mengjia Niu",
"Yuchen Zhao",
"Hamed Haddadi"
] | 2023-09-11 22:08:09 | http://arxiv.org/abs/2309.05845v1 | http://arxiv.org/pdf/2309.05845v1 | 2309.05845v1 |
Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals | Health-related acoustic signals, such as cough and breathing sounds, are
relevant for medical diagnosis and continuous health monitoring. Most existing
machine learning approaches for health acoustics are trained and evaluated on
specific tasks, limiting their generalizability across various healthcare
applications. In this paper, we leverage a self-supervised learning framework,
SimCLR with a Slowfast NFNet backbone, for contrastive learning of health
acoustics. A crucial aspect of optimizing Slowfast NFNet for this application
lies in identifying effective audio augmentations. We conduct an in-depth
analysis of various audio augmentation strategies and demonstrate that an
appropriate augmentation strategy enhances the performance of the Slowfast
NFNet audio encoder across a diverse set of health acoustic tasks. Our findings
reveal that when augmentations are combined, they can produce synergistic
effects that exceed the benefits seen when each is applied individually. | [
"Louis Blankemeier",
"Sebastien Baur",
"Wei-Hung Weng",
"Jake Garrison",
"Yossi Matias",
"Shruthi Prabhakara",
"Diego Ardila",
"Zaid Nabulsi"
] | 2023-09-11 22:03:34 | http://arxiv.org/abs/2309.05843v1 | http://arxiv.org/pdf/2309.05843v1 | 2309.05843v1 |
The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems | Recent years have seen significant progress in the realm of robot autonomy,
accompanied by the expanding reach of robotic technologies. However, the
emergence of new deployment domains brings unprecedented challenges in ensuring
safe operation of these systems, which remains as crucial as ever. While
traditional model-based safe control methods struggle with generalizability and
scalability, emerging data-driven approaches tend to lack well-understood
guarantees, which can result in unpredictable catastrophic failures. Successful
deployment of the next generation of autonomous robots will require integrating
the strengths of both paradigms. This article provides a review of safety
filter approaches, highlighting important connections between existing
techniques and proposing a unified technical framework to understand, compare,
and combine them. The new unified view exposes a shared modular structure
across a range of seemingly disparate safety filter classes and naturally
suggests directions for future progress towards more scalable synthesis, robust
monitoring, and efficient intervention. | [
"Kai-Chieh Hsu",
"Haimin Hu",
"Jaime Fernández Fisac"
] | 2023-09-11 21:34:16 | http://arxiv.org/abs/2309.05837v1 | http://arxiv.org/pdf/2309.05837v1 | 2309.05837v1 |
PACE-LM: Prompting and Augmentation for Calibrated Confidence Estimation with GPT-4 in Cloud Incident Root Cause Analysis | Major cloud providers have employed advanced AI-based solutions like large
language models to aid humans in identifying the root causes of cloud
incidents. Despite the growing prevalence of AI-driven assistants in the root
cause analysis process, their effectiveness in assisting on-call engineers is
constrained by low accuracy due to the intrinsic difficulty of the task, a
propensity for LLM-based approaches to hallucinate, and difficulties in
distinguishing these well-disguised hallucinations. To address this challenge,
we propose to perform confidence estimation for the predictions to help on-call
engineers make decisions on whether to adopt the model prediction. Considering
the black-box nature of many LLM-based root cause predictors, fine-tuning or
temperature-scaling-based approaches are inapplicable. We therefore design an
innovative confidence estimation framework based on prompting
retrieval-augmented large language models (LLMs) that demand a minimal amount
of information from the root cause predictor. This approach consists of two
scoring phases: the LLM-based confidence estimator first evaluates its
confidence in making judgments in the face of the current incident that
reflects its ``grounded-ness" level in reference data, then rates the root
cause prediction based on historical references. An optimization step combines
these two scores for a final confidence assignment. We show that our method is
able to produce calibrated confidence estimates for predicted root causes,
validate the usefulness of retrieved historical data and the prompting strategy
as well as the generalizability across different root cause prediction models.
Our study takes an important move towards reliably and effectively embedding
LLMs into cloud incident management systems. | [
"Dylan Zhang",
"Xuchao Zhang",
"Chetan Bansal",
"Pedro Las-Casas",
"Rodrigo Fonseca",
"Saravan Rajmohan"
] | 2023-09-11 21:24:00 | http://arxiv.org/abs/2309.05833v3 | http://arxiv.org/pdf/2309.05833v3 | 2309.05833v3 |
Instance-Agnostic Geometry and Contact Dynamics Learning | This work presents an instance-agnostic learning framework that fuses vision
with dynamics to simultaneously learn shape, pose trajectories, and physical
properties via the use of geometry as a shared representation. Unlike many
contact learning approaches that assume motion capture input and a known shape
prior for the collision model, our proposed framework learns an object's
geometric and dynamic properties from RGBD video, without requiring either
category-level or instance-level shape priors. We integrate a vision system,
BundleSDF, with a dynamics system, ContactNets, and propose a cyclic training
pipeline to use the output from the dynamics module to refine the poses and the
geometry from the vision module, using perspective reprojection. Experiments
demonstrate our framework's ability to learn the geometry and dynamics of rigid
and convex objects and improve upon the current tracking framework. | [
"Mengti Sun",
"Bowen Jiang",
"Bibit Bianchini",
"Camillo Jose Taylor",
"Michael Posa"
] | 2023-09-11 21:18:15 | http://arxiv.org/abs/2309.05832v2 | http://arxiv.org/pdf/2309.05832v2 | 2309.05832v2 |
Studying Accuracy of Machine Learning Models Trained on Lab Lifting Data in Solving Real-World Problems Using Wearable Sensors for Workplace Safety | Porting ML models trained on lab data to real-world situations has long been
a challenge. This paper discusses porting a lab-trained lifting identification
model to the real-world. With performance much lower than on training data, we
explored causes of the failure and proposed four potential solutions to
increase model performance | [
"Joseph Bertrand",
"Nick Griffey",
"Ming-Lun Lu",
"Rashmi Jha"
] | 2023-09-11 21:17:10 | http://arxiv.org/abs/2309.05831v1 | http://arxiv.org/pdf/2309.05831v1 | 2309.05831v1 |
Exploring Geometric Deep Learning For Precipitation Nowcasting | Precipitation nowcasting (up to a few hours) remains a challenge due to the
highly complex local interactions that need to be captured accurately.
Convolutional Neural Networks rely on convolutional kernels convolving with
grid data and the extracted features are trapped by limited receptive field,
typically expressed in excessively smooth output compared to ground truth. Thus
they lack the capacity to model complex spatial relationships among the grids.
Geometric deep learning aims to generalize neural network models to
non-Euclidean domains. Such models are more flexible in defining nodes and
edges and can effectively capture dynamic spatial relationship among
geographical grids. Motivated by this, we explore a geometric deep
learning-based temporal Graph Convolutional Network (GCN) for precipitation
nowcasting. The adjacency matrix that simulates the interactions among grid
cells is learned automatically by minimizing the L1 loss between prediction and
ground truth pixel value during the training procedure. Then, the spatial
relationship is refined by GCN layers while the temporal information is
extracted by 1D convolution with various kernel lengths. The neighboring
information is fed as auxiliary input layers to improve the final result. We
test the model on sequences of radar reflectivity maps over the Trento/Italy
area. The results show that GCNs improves the effectiveness of modeling the
local details of the cloud profile as well as the prediction accuracy by
achieving decreased error measures. | [
"Shan Zhao",
"Sudipan Saha",
"Zhitong Xiong",
"Niklas Boers",
"Xiao Xiang Zhu"
] | 2023-09-11 21:14:55 | http://arxiv.org/abs/2309.05828v1 | http://arxiv.org/pdf/2309.05828v1 | 2309.05828v1 |
KD-FixMatch: Knowledge Distillation Siamese Neural Networks | Semi-supervised learning (SSL) has become a crucial approach in deep learning
as a way to address the challenge of limited labeled data. The success of deep
neural networks heavily relies on the availability of large-scale high-quality
labeled data. However, the process of data labeling is time-consuming and
unscalable, leading to shortages in labeled data. SSL aims to tackle this
problem by leveraging additional unlabeled data in the training process. One of
the popular SSL algorithms, FixMatch, trains identical weight-sharing teacher
and student networks simultaneously using a siamese neural network (SNN).
However, it is prone to performance degradation when the pseudo labels are
heavily noisy in the early training stage. We present KD-FixMatch, a novel SSL
algorithm that addresses the limitations of FixMatch by incorporating knowledge
distillation. The algorithm utilizes a combination of sequential and
simultaneous training of SNNs to enhance performance and reduce performance
degradation. Firstly, an outer SNN is trained using labeled and unlabeled data.
After that, the network of the well-trained outer SNN generates pseudo labels
for the unlabeled data, from which a subset of unlabeled data with trusted
pseudo labels is then carefully created through high-confidence sampling and
deep embedding clustering. Finally, an inner SNN is trained with the labeled
data, the unlabeled data, and the subset of unlabeled data with trusted pseudo
labels. Experiments on four public data sets demonstrate that KD-FixMatch
outperforms FixMatch in all cases. Our results indicate that KD-FixMatch has a
better training starting point that leads to improved model performance
compared to FixMatch. | [
"Chien-Chih Wang",
"Shaoyuan Xu",
"Jinmiao Fu",
"Yang Liu",
"Bryan Wang"
] | 2023-09-11 21:11:48 | http://arxiv.org/abs/2309.05826v1 | http://arxiv.org/pdf/2309.05826v1 | 2309.05826v1 |
Ensemble-based modeling abstractions for modern self-optimizing systems | In this paper, we extend our ensemble-based component model DEECo with the
capability to use machine-learning and optimization heuristics in establishing
and reconfiguration of autonomic component ensembles. We show how to capture
these concepts on the model level and give an example of how such a model can
be beneficially used for modeling access-control related problem in the
Industry 4.0 settings. We argue that incorporating machine-learning and
optimization heuristics is a key feature for modern smart systems which are to
learn over the time and optimize their behavior at runtime to deal with
uncertainty in their environment. | [
"Michal Töpfer",
"Milad Abdullah",
"Tomáš Bureš",
"Petr Hnětynka",
"Martin Kruliš"
] | 2023-09-11 21:01:11 | http://arxiv.org/abs/2309.05823v1 | http://arxiv.org/pdf/2309.05823v1 | 2309.05823v1 |
Interpretable learning of effective dynamics for multiscale systems | The modeling and simulation of high-dimensional multiscale systems is a
critical challenge across all areas of science and engineering. It is broadly
believed that even with today's computer advances resolving all spatiotemporal
scales described by the governing equations remains a remote target. This
realization has prompted intense efforts to develop model order reduction
techniques. In recent years, techniques based on deep recurrent neural networks
have produced promising results for the modeling and simulation of complex
spatiotemporal systems and offer large flexibility in model development as they
can incorporate experimental and computational data. However, neural networks
lack interpretability, which limits their utility and generalizability across
complex systems. Here we propose a novel framework of Interpretable Learning
Effective Dynamics (iLED) that offers comparable accuracy to state-of-the-art
recurrent neural network-based approaches while providing the added benefit of
interpretability. The iLED framework is motivated by Mori-Zwanzig and Koopman
operator theory, which justifies the choice of the specific architecture. We
demonstrate the effectiveness of the proposed framework in simulations of three
benchmark multiscale systems. Our results show that the iLED framework can
generate accurate predictions and obtain interpretable dynamics, making it a
promising approach for solving high-dimensional multiscale systems. | [
"Emmanuel Menier",
"Sebastian Kaltenbach",
"Mouadh Yagoubi",
"Marc Schoenauer",
"Petros Koumoutsakos"
] | 2023-09-11 20:29:38 | http://arxiv.org/abs/2309.05812v1 | http://arxiv.org/pdf/2309.05812v1 | 2309.05812v1 |
Predicting the Radiation Field of Molecular Clouds using Denoising Diffusion Probabilistic Models | Accurately quantifying the impact of radiation feedback in star formation is
challenging. To address this complex problem, we employ deep learning
techniques, denoising diffusion probabilistic models (DDPMs), to predict the
interstellar radiation field (ISRF) strength based on three-band dust emission
at 4.5 \um, 24 \um, and 250 \um. We adopt magnetohydrodynamic simulations from
the STARFORGE (STAR FORmation in Gaseous Environments) project that model star
formation and giant molecular cloud (GMC) evolution. We generate synthetic dust
emission maps matching observed spectral energy distributions in the Monoceros
R2 (MonR2) GMC. We train DDPMs to estimate the ISRF using synthetic three-band
dust emission. The dispersion between the predictions and true values is within
a factor of 0.1 for the test set. We extended our assessment of the diffusion
model to include new simulations with varying physical parameters. While there
is a consistent offset observed in these out-of-distribution simulations, the
model effectively constrains the relative intensity to within a factor of 2.
Meanwhile, our analysis reveals weak correlation between the ISRF solely
derived from dust temperature and the actual ISRF. We apply our trained model
to predict the ISRF in MonR2, revealing a correspondence between intense ISRF,
bright sources, and high dust emission, confirming the model's ability to
capture ISRF variations. Our model robustly predicts radiation feedback
distribution, even in complex, poorly constrained ISRF environments like those
influenced by nearby star clusters. However, precise ISRF predictions require
an accurate training dataset mirroring the target molecular cloud's unique
physical conditions. | [
"Duo Xu",
"Stella Offner",
"Robert Gutermuth",
"Michael Grudic",
"David Guszejnov",
"Philip Hopkins"
] | 2023-09-11 20:28:43 | http://arxiv.org/abs/2309.05811v1 | http://arxiv.org/pdf/2309.05811v1 | 2309.05811v1 |
SHIFT3D: Synthesizing Hard Inputs For Tricking 3D Detectors | We present SHIFT3D, a differentiable pipeline for generating 3D shapes that
are structurally plausible yet challenging to 3D object detectors. In
safety-critical applications like autonomous driving, discovering such novel
challenging objects can offer insight into unknown vulnerabilities of 3D
detectors. By representing objects with a signed distanced function (SDF), we
show that gradient error signals allow us to smoothly deform the shape or pose
of a 3D object in order to confuse a downstream 3D detector. Importantly, the
objects generated by SHIFT3D physically differ from the baseline object yet
retain a semantically recognizable shape. Our approach provides interpretable
failure modes for modern 3D object detectors, and can aid in preemptive
discovery of potential safety risks within 3D perception systems before these
risks become critical failures. | [
"Hongge Chen",
"Zhao Chen",
"Gregory P. Meyer",
"Dennis Park",
"Carl Vondrick",
"Ashish Shrivastava",
"Yuning Chai"
] | 2023-09-11 20:28:18 | http://arxiv.org/abs/2309.05810v1 | http://arxiv.org/pdf/2309.05810v1 | 2309.05810v1 |
Divergences in Color Perception between Deep Neural Networks and Humans | Deep neural networks (DNNs) are increasingly proposed as models of human
vision, bolstered by their impressive performance on image classification and
object recognition tasks. Yet, the extent to which DNNs capture fundamental
aspects of human vision such as color perception remains unclear. Here, we
develop novel experiments for evaluating the perceptual coherence of color
embeddings in DNNs, and we assess how well these algorithms predict human color
similarity judgments collected via an online survey. We find that
state-of-the-art DNN architectures $-$ including convolutional neural networks
and vision transformers $-$ provide color similarity judgments that strikingly
diverge from human color judgments of (i) images with controlled color
properties, (ii) images generated from online searches, and (iii) real-world
images from the canonical CIFAR-10 dataset. We compare DNN performance against
an interpretable and cognitively plausible model of color perception based on
wavelet decomposition, inspired by foundational theories in computational
neuroscience. While one deep learning model $-$ a convolutional DNN trained on
a style transfer task $-$ captures some aspects of human color perception, our
wavelet algorithm provides more coherent color embeddings that better predict
human color judgments compared to all DNNs we examine. These results hold when
altering the high-level visual task used to train similar DNN architectures
(e.g., image classification versus image segmentation), as well as when
examining the color embeddings of different layers in a given DNN architecture.
These findings break new ground in the effort to analyze the perceptual
representations of machine learning algorithms and to improve their ability to
serve as cognitively plausible models of human vision. Implications for machine
learning, human perception, and embodied cognition are discussed. | [
"Ethan O. Nadler",
"Elise Darragh-Ford",
"Bhargav Srinivasa Desikan",
"Christian Conaway",
"Mark Chu",
"Tasker Hull",
"Douglas Guilbeault"
] | 2023-09-11 20:26:40 | http://arxiv.org/abs/2309.05809v1 | http://arxiv.org/pdf/2309.05809v1 | 2309.05809v1 |
Online ML Self-adaptation in Face of Traps | Online machine learning (ML) is often used in self-adaptive systems to
strengthen the adaptation mechanism and improve the system utility. Despite
such benefits, applying online ML for self-adaptation can be challenging, and
not many papers report its limitations. Recently, we experimented with applying
online ML for self-adaptation of a smart farming scenario and we had faced
several unexpected difficulties -- traps -- that, to our knowledge, are not
discussed enough in the community. In this paper, we report our experience with
these traps. Specifically, we discuss several traps that relate to the
specification and online training of the ML-based estimators, their impact on
self-adaptation, and the approach used to evaluate the estimators. Our overview
of these traps provides a list of lessons learned, which can serve as guidance
for other researchers and practitioners when applying online ML for
self-adaptation. | [
"Michal Töpfer",
"František Plášil",
"Tomáš Bureš",
"Petr Hnětynka",
"Martin Kruliš",
"Danny Weyns"
] | 2023-09-11 20:17:11 | http://arxiv.org/abs/2309.05805v1 | http://arxiv.org/pdf/2309.05805v1 | 2309.05805v1 |
Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models | A crucial design decision for any robot learning pipeline is the choice of
policy representation: what type of model should be used to generate the next
set of robot actions? Owing to the inherent multi-modal nature of many robotic
tasks, combined with the recent successes in generative modeling, researchers
have turned to state-of-the-art probabilistic models such as diffusion models
for policy representation. In this work, we revisit the choice of energy-based
models (EBM) as a policy class. We show that the prevailing folklore -- that
energy models in high dimensional continuous spaces are impractical to train --
is false. We develop a practical training objective and algorithm for energy
models which combines several key ingredients: (i) ranking noise contrastive
estimation (R-NCE), (ii) learnable negative samplers, and (iii) non-adversarial
joint training. We prove that our proposed objective function is asymptotically
consistent and quantify its limiting variance. On the other hand, we show that
the Implicit Behavior Cloning (IBC) objective is actually biased even at the
population level, providing a mathematical explanation for the poor performance
of IBC trained energy policies in several independent follow-up works. We
further extend our algorithm to learn a continuous stochastic process that
bridges noise and data, modeling this process with a family of EBMs indexed by
scale variable. In doing so, we demonstrate that the core idea behind recent
progress in generative modeling is actually compatible with EBMs. Altogether,
our proposed training algorithms enable us to train energy-based models as
policies which compete with -- and even outperform -- diffusion models and
other state-of-the-art approaches in several challenging multi-modal
benchmarks: obstacle avoidance path planning and contact-rich block pushing. | [
"Sumeet Singh",
"Stephen Tu",
"Vikas Sindhwani"
] | 2023-09-11 20:13:47 | http://arxiv.org/abs/2309.05803v1 | http://arxiv.org/pdf/2309.05803v1 | 2309.05803v1 |
Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning | Hypergraphs can naturally model group-wise relations (e.g., a group of users
who co-purchase an item) as hyperedges. Hyperedge prediction is to predict
future or unobserved hyperedges, which is a fundamental task in many real-world
applications (e.g., group recommendation). Despite the recent breakthrough of
hyperedge prediction methods, the following challenges have been rarely
studied: (C1) How to aggregate the nodes in each hyperedge candidate for
accurate hyperedge prediction? and (C2) How to mitigate the inherent data
sparsity problem in hyperedge prediction? To tackle both challenges together,
in this paper, we propose a novel hyperedge prediction framework (CASH) that
employs (1) context-aware node aggregation to precisely capture complex
relations among nodes in each hyperedge for (C1) and (2) self-supervised
contrastive learning in the context of hyperedge prediction to enhance
hypergraph representations for (C2). Furthermore, as for (C2), we propose a
hyperedge-aware augmentation method to fully exploit the latent semantics
behind the original hypergraph and consider both node-level and group-level
contrasts (i.e., dual contrasts) for better node and hyperedge representations.
Extensive experiments on six real-world hypergraphs reveal that CASH
consistently outperforms all competing methods in terms of the accuracy in
hyperedge prediction and each of the proposed strategies is effective in
improving the model accuracy of CASH. For the detailed information of CASH, we
provide the code and datasets at: https://github.com/yy-ko/cash. | [
"Yunyong Ko",
"Hanghang Tong",
"Sang-Wook Kim"
] | 2023-09-11 20:06:00 | http://arxiv.org/abs/2309.05798v1 | http://arxiv.org/pdf/2309.05798v1 | 2309.05798v1 |
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