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The Calysto Scheme Project | Calysto Scheme is written in Scheme in Continuation-Passing Style, and
converted through a series of correctness-preserving program transformations
into Python. It has support for standard Scheme functionality, including
call/cc, as well as syntactic extensions, a nondeterministic operator for
automatic backtracking, and many extensions to allow Python interoperation.
Because of its Python foundation, it can take advantage of modern Python
libraries, including those for machine learning and other pedagogical contexts.
Although Calysto Scheme was developed with educational purposes in mind, it has
proven to be generally useful due to its simplicity and ease of installation.
It has been integrated into the Jupyter Notebook ecosystem and used in the
classroom to teach introductory Programming Languages with some interesting and
unique twists. | [
"Douglas S. Blank",
"James B. Marshall"
] | 2023-10-16 23:41:21 | http://arxiv.org/abs/2310.10886v1 | http://arxiv.org/pdf/2310.10886v1 | 2310.10886v1 |
BLoad: Enhancing Neural Network Training with Efficient Sequential Data Handling | The increasing complexity of modern deep neural network models and the
expanding sizes of datasets necessitate the development of optimized and
scalable training methods. In this white paper, we addressed the challenge of
efficiently training neural network models using sequences of varying sizes. To
address this challenge, we propose a novel training scheme that enables
efficient distributed data-parallel training on sequences of different sizes
with minimal overhead. By using this scheme we were able to reduce the padding
amount by more than 100$x$ while not deleting a single frame, resulting in an
overall increased performance on both training time and Recall in our
experiments. | [
"Raphael Ruschel",
"A. S. M. Iftekhar",
"B. S. Manjunath",
"Suya You"
] | 2023-10-16 23:14:56 | http://arxiv.org/abs/2310.10879v1 | http://arxiv.org/pdf/2310.10879v1 | 2310.10879v1 |
Eco-Driving Control of Connected and Automated Vehicles using Neural Network based Rollout | Connected and autonomous vehicles have the potential to minimize energy
consumption by optimizing the vehicle velocity and powertrain dynamics with
Vehicle-to-Everything info en route. Existing deterministic and stochastic
methods created to solve the eco-driving problem generally suffer from high
computational and memory requirements, which makes online implementation
challenging.
This work proposes a hierarchical multi-horizon optimization framework
implemented via a neural network. The neural network learns a full-route value
function to account for the variability in route information and is then used
to approximate the terminal cost in a receding horizon optimization.
Simulations over real-world routes demonstrate that the proposed approach
achieves comparable performance to a stochastic optimization solution obtained
via reinforcement learning, while requiring no sophisticated training paradigm
and negligible on-board memory. | [
"Jacob Paugh",
"Zhaoxuan Zhu",
"Shobhit Gupta",
"Marcello Canova",
"Stephanie Stockar"
] | 2023-10-16 23:13:51 | http://arxiv.org/abs/2310.10878v1 | http://arxiv.org/pdf/2310.10878v1 | 2310.10878v1 |
Religious Affiliation in the Twenty-First Century: A Machine Learning Perspective on the World Value Survey | This paper is a quantitative analysis of the data collected globally by the
World Value Survey. The data is used to study the trajectories of change in
individuals' religious beliefs, values, and behaviors in societies. Utilizing
random forest, we aim to identify the key factors of religiosity and classify
respondents of the survey as religious and non religious using country level
data. We use resampling techniques to balance the data and improve imbalanced
learning performance metrics. The results of the variable importance analysis
suggest that Age and Income are the most important variables in the majority of
countries. The results are discussed with fundamental sociological theories
regarding religion and human behavior. This study is an application of machine
learning in identifying the underlying patterns in the data of 30 countries
participating in the World Value Survey. The results from variable importance
analysis and classification of imbalanced data provide valuable insights
beneficial to theoreticians and researchers of social sciences. | [
"Elaheh Jafarigol",
"William Keely",
"Tess Hartog",
"Tom Welborn",
"Peyman Hekmatpour",
"Theodore B. Trafalis"
] | 2023-10-16 23:01:16 | http://arxiv.org/abs/2310.10874v1 | http://arxiv.org/pdf/2310.10874v1 | 2310.10874v1 |
Joint Optimization of Traffic Signal Control and Vehicle Routing in Signalized Road Networks using Multi-Agent Deep Reinforcement Learning | Urban traffic congestion is a critical predicament that plagues modern road
networks. To alleviate this issue and enhance traffic efficiency, traffic
signal control and vehicle routing have proven to be effective measures. In
this paper, we propose a joint optimization approach for traffic signal control
and vehicle routing in signalized road networks. The objective is to enhance
network performance by simultaneously controlling signal timings and route
choices using Multi-Agent Deep Reinforcement Learning (MADRL). Signal control
agents (SAs) are employed to establish signal timings at intersections, whereas
vehicle routing agents (RAs) are responsible for selecting vehicle routes. By
establishing relevance between agents and enabling them to share observations
and rewards, interaction and cooperation among agents are fostered, which
enhances individual training. The Multi-Agent Advantage Actor-Critic algorithm
is used to handle multi-agent environments, and Deep Neural Network (DNN)
structures are designed to facilitate the algorithm's convergence. Notably, our
work is the first to utilize MADRL in determining the optimal joint policy for
signal control and vehicle routing. Numerical experiments conducted on the
modified Sioux network demonstrate that our integration of signal control and
vehicle routing outperforms controlling signal timings or vehicles' routes
alone in enhancing traffic efficiency. | [
"Xianyue Peng",
"Hang Gao",
"Gengyue Han",
"Hao Wang",
"Michael Zhang"
] | 2023-10-16 22:10:47 | http://arxiv.org/abs/2310.10856v1 | http://arxiv.org/pdf/2310.10856v1 | 2310.10856v1 |
CoTFormer: More Tokens With Attention Make Up For Less Depth | The race to continually develop ever larger and deeper foundational models is
underway. However, techniques like the Chain-of-Thought (CoT) method continue
to play a pivotal role in achieving optimal downstream performance. In this
work, we establish an approximate parallel between using chain-of-thought and
employing a deeper transformer. Building on this insight, we introduce
CoTFormer, a transformer variant that employs an implicit CoT-like mechanism to
achieve capacity comparable to a deeper model. Our empirical findings
demonstrate the effectiveness of CoTFormers, as they significantly outperform
larger standard transformers. | [
"Amirkeivan Mohtashami",
"Matteo Pagliardini",
"Martin Jaggi"
] | 2023-10-16 21:37:34 | http://arxiv.org/abs/2310.10845v1 | http://arxiv.org/pdf/2310.10845v1 | 2310.10845v1 |
Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks | Large Language Models (LLMs) are swiftly advancing in architecture and
capability, and as they integrate more deeply into complex systems, the urgency
to scrutinize their security properties grows. This paper surveys research in
the emerging interdisciplinary field of adversarial attacks on LLMs, a subfield
of trustworthy ML, combining the perspectives of Natural Language Processing
and Security. Prior work has shown that even safety-aligned LLMs (via
instruction tuning and reinforcement learning through human feedback) can be
susceptible to adversarial attacks, which exploit weaknesses and mislead AI
systems, as evidenced by the prevalence of `jailbreak' attacks on models like
ChatGPT and Bard. In this survey, we first provide an overview of large
language models, describe their safety alignment, and categorize existing
research based on various learning structures: textual-only attacks,
multi-modal attacks, and additional attack methods specifically targeting
complex systems, such as federated learning or multi-agent systems. We also
offer comprehensive remarks on works that focus on the fundamental sources of
vulnerabilities and potential defenses. To make this field more accessible to
newcomers, we present a systematic review of existing works, a structured
typology of adversarial attack concepts, and additional resources, including
slides for presentations on related topics at the 62nd Annual Meeting of the
Association for Computational Linguistics (ACL'24). | [
"Erfan Shayegani",
"Md Abdullah Al Mamun",
"Yu Fu",
"Pedram Zaree",
"Yue Dong",
"Nael Abu-Ghazaleh"
] | 2023-10-16 21:37:24 | http://arxiv.org/abs/2310.10844v1 | http://arxiv.org/pdf/2310.10844v1 | 2310.10844v1 |
Probabilistic Classification by Density Estimation Using Gaussian Mixture Model and Masked Autoregressive Flow | Density estimation, which estimates the distribution of data, is an important
category of probabilistic machine learning. A family of density estimators is
mixture models, such as Gaussian Mixture Model (GMM) by expectation
maximization. Another family of density estimators is the generative models
which generate data from input latent variables. One of the generative models
is the Masked Autoregressive Flow (MAF) which makes use of normalizing flows
and autoregressive networks. In this paper, we use the density estimators for
classification, although they are often used for estimating the distribution of
data. We model the likelihood of classes of data by density estimation,
specifically using GMM and MAF. The proposed classifiers outperform simpler
classifiers such as linear discriminant analysis which model the likelihood
using only a single Gaussian distribution. This work opens the research door
for proposing other probabilistic classifiers based on joint density
estimation. | [
"Benyamin Ghojogh",
"Milad Amir Toutounchian"
] | 2023-10-16 21:37:22 | http://arxiv.org/abs/2310.10843v1 | http://arxiv.org/pdf/2310.10843v1 | 2310.10843v1 |
A Machine Learning-based Algorithm for Automated Detection of Frequency-based Events in Recorded Time Series of Sensor Data | Automated event detection has emerged as one of the fundamental practices to
monitor the behavior of technical systems by means of sensor data. In the
automotive industry, these methods are in high demand for tracing events in
time series data. For assessing the active vehicle safety systems, a diverse
range of driving scenarios is conducted. These scenarios involve the recording
of the vehicle's behavior using external sensors, enabling the evaluation of
operational performance. In such setting, automated detection methods not only
accelerate but also standardize and objectify the evaluation by avoiding
subjective, human-based appraisals in the data inspection. This work proposes a
novel event detection method that allows to identify frequency-based events in
time series data. To this aim, the time series data is mapped to
representations in the time-frequency domain, known as scalograms. After
filtering scalograms to enhance relevant parts of the signal, an object
detection model is trained to detect the desired event objects in the
scalograms. For the analysis of unseen time series data, events can be detected
in their scalograms with the trained object detection model and are thereafter
mapped back to the time series data to mark the corresponding time interval.
The algorithm, evaluated on unseen datasets, achieves a precision rate of 0.97
in event detection, providing sharp time interval boundaries whose accurate
indication by human visual inspection is challenging. Incorporating this method
into the vehicle development process enhances the accuracy and reliability of
event detection, which holds major importance for rapid testing analysis. | [
"Bahareh Medghalchi",
"Andreas Vogel"
] | 2023-10-16 21:35:23 | http://arxiv.org/abs/2310.10841v1 | http://arxiv.org/pdf/2310.10841v1 | 2310.10841v1 |
Approximating Two-Layer Feedforward Networks for Efficient Transformers | How to reduce compute and memory requirements of neural networks (NNs)
without sacrificing performance? Many recent works use sparse Mixtures of
Experts (MoEs) to build resource-efficient large language models (LMs). Here we
introduce several novel perspectives on MoEs, presenting a general framework
that unifies various methods to approximate two-layer NNs (e.g., feedforward
blocks of Transformers), including product-key memories (PKMs). Leveraging
insights from this framework, we propose methods to improve both MoEs and PKMs.
Unlike prior work that compares MoEs with dense baselines under the
compute-equal condition, our evaluation condition is parameter-equal, which is
crucial to properly evaluate LMs. We show that our MoEs are competitive with
the dense Transformer-XL on both the WikiText-103 and enwiki8 datasets at two
different scales, while being much more resource efficient. This demonstrates
that MoEs are relevant not only to extremely large LMs but also to any-scale
resource-efficient LMs. Our code is public. | [
"Róbert Csordás",
"Kazuki Irie",
"Jürgen Schmidhuber"
] | 2023-10-16 21:23:16 | http://arxiv.org/abs/2310.10837v2 | http://arxiv.org/pdf/2310.10837v2 | 2310.10837v2 |
Gaussian processes based data augmentation and expected signature for time series classification | The signature is a fundamental object that describes paths (that is,
continuous functions from an interval to a Euclidean space). Likewise, the
expected signature provides a statistical description of the law of stochastic
processes. We propose a feature extraction model for time series built upon the
expected signature. This is computed through a Gaussian processes based data
augmentation. One of the main features is that an optimal feature extraction is
learnt through the supervised task that uses the model. | [
"Marco Romito",
"Francesco Triggiano"
] | 2023-10-16 21:18:51 | http://arxiv.org/abs/2310.10836v1 | http://arxiv.org/pdf/2310.10836v1 | 2310.10836v1 |
Provable Probabilistic Imaging using Score-Based Generative Priors | Estimating high-quality images while also quantifying their uncertainty are
two desired features in an image reconstruction algorithm for solving ill-posed
inverse problems. In this paper, we propose plug-and-play Monte Carlo (PMC) as
a principled framework for characterizing the space of possible solutions to a
general inverse problem. PMC is able to incorporate expressive score-based
generative priors for high-quality image reconstruction while also performing
uncertainty quantification via posterior sampling. In particular, we introduce
two PMC algorithms which can be viewed as the sampling analogues of the
traditional plug-and-play priors (PnP) and regularization by denoising (RED)
algorithms. We also establish a theoretical analysis for characterizing the
convergence of the PMC algorithms. Our analysis provides non-asymptotic
stationarity guarantees for both algorithms, even in the presence of
non-log-concave likelihoods and imperfect score networks. We demonstrate the
performance of the PMC algorithms on multiple representative inverse problems
with both linear and nonlinear forward models. Experimental results show that
PMC significantly improves reconstruction quality and enables high-fidelity
uncertainty quantification. | [
"Yu Sun",
"Zihui Wu",
"Yifan Chen",
"Berthy T. Feng",
"Katherine L. Bouman"
] | 2023-10-16 21:17:29 | http://arxiv.org/abs/2310.10835v1 | http://arxiv.org/pdf/2310.10835v1 | 2310.10835v1 |
Proper Laplacian Representation Learning | The ability to learn good representations of states is essential for solving
large reinforcement learning problems, where exploration, generalization, and
transfer are particularly challenging. The Laplacian representation is a
promising approach to address these problems by inducing intrinsic rewards for
temporally-extended action discovery and reward shaping, and informative state
encoding. To obtain the Laplacian representation one needs to compute the
eigensystem of the graph Laplacian, which is often approximated through
optimization objectives compatible with deep learning approaches. These
approximations, however, depend on hyperparameters that are impossible to tune
efficiently, converge to arbitrary rotations of the desired eigenvectors, and
are unable to accurately recover the corresponding eigenvalues. In this paper
we introduce a theoretically sound objective and corresponding optimization
algorithm for approximating the Laplacian representation. Our approach
naturally recovers both the true eigenvectors and eigenvalues while eliminating
the hyperparameter dependence of previous approximations. We provide
theoretical guarantees for our method and we show that those results translate
empirically into robust learning across multiple environments. | [
"Diego Gomez",
"Michael Bowling",
"Marlos C. Machado"
] | 2023-10-16 21:14:50 | http://arxiv.org/abs/2310.10833v1 | http://arxiv.org/pdf/2310.10833v1 | 2310.10833v1 |
Accurate Data-Driven Surrogates of Dynamical Systems for Forward Propagation of Uncertainty | Stochastic collocation (SC) is a well-known non-intrusive method of
constructing surrogate models for uncertainty quantification. In dynamical
systems, SC is especially suited for full-field uncertainty propagation that
characterizes the distributions of the high-dimensional primary solution fields
of a model with stochastic input parameters. However, due to the highly
nonlinear nature of the parameter-to-solution map in even the simplest
dynamical systems, the constructed SC surrogates are often inaccurate. This
work presents an alternative approach, where we apply the SC approximation over
the dynamics of the model, rather than the solution. By combining the
data-driven sparse identification of nonlinear dynamics (SINDy) framework with
SC, we construct dynamics surrogates and integrate them through time to
construct the surrogate solutions. We demonstrate that the SC-over-dynamics
framework leads to smaller errors, both in terms of the approximated system
trajectories as well as the model state distributions, when compared against
full-field SC applied to the solutions directly. We present numerical evidence
of this improvement using three test problems: a chaotic ordinary differential
equation, and two partial differential equations from solid mechanics. | [
"Saibal De",
"Reese E. Jones",
"Hemanth Kolla"
] | 2023-10-16 21:07:54 | http://arxiv.org/abs/2310.10831v1 | http://arxiv.org/pdf/2310.10831v1 | 2310.10831v1 |
Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning | Sample efficiency is central to developing practical reinforcement learning
(RL) for complex and large-scale decision-making problems. The ability to
transfer and generalize knowledge gained from previous experiences to
downstream tasks can significantly improve sample efficiency. Recent research
indicates that successor feature (SF) RL algorithms enable knowledge
generalization between tasks with different rewards but identical transition
dynamics. It has recently been hypothesized that combining model-based (MB)
methods with SF algorithms can alleviate the limitation of fixed transition
dynamics. Furthermore, uncertainty-aware exploration is widely recognized as
another appealing approach for improving sample efficiency. Putting together
two ideas of hybrid model-based successor feature (MB-SF) and uncertainty leads
to an approach to the problem of sample efficient uncertainty-aware knowledge
transfer across tasks with different transition dynamics or/and reward
functions. In this paper, the uncertainty of the value of each action is
approximated by a Kalman filter (KF)-based multiple-model adaptive estimation.
This KF-based framework treats the parameters of a model as random variables.
To the best of our knowledge, this is the first attempt at formulating a hybrid
MB-SF algorithm capable of generalizing knowledge across large or continuous
state space tasks with various transition dynamics while requiring less
computation at decision time than MB methods. The number of samples required to
learn the tasks was compared to recent SF and MB baselines. The results show
that our algorithm generalizes its knowledge across different transition
dynamics, learns downstream tasks with significantly fewer samples than
starting from scratch, and outperforms existing approaches. | [
"Parvin Malekzadeh",
"Ming Hou",
"Konstantinos N. Plataniotis"
] | 2023-10-16 20:37:36 | http://arxiv.org/abs/2310.10818v1 | http://arxiv.org/pdf/2310.10818v1 | 2310.10818v1 |
Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms | Multi-Agent Reinforcement Learning (MARL) has shown promising results across
several domains. Despite this promise, MARL policies often lack robustness and
are therefore sensitive to small changes in their environment. This presents a
serious concern for the real world deployment of MARL algorithms, where the
testing environment may slightly differ from the training environment. In this
work we show that we can gain robustness by controlling a policy's Lipschitz
constant, and under mild conditions, establish the existence of a Lipschitz and
close-to-optimal policy. Based on these insights, we propose a new robust MARL
framework, ERNIE, that promotes the Lipschitz continuity of the policies with
respect to the state observations and actions by adversarial regularization.
The ERNIE framework provides robustness against noisy observations, changing
transition dynamics, and malicious actions of agents. However, ERNIE's
adversarial regularization may introduce some training instability. To reduce
this instability, we reformulate adversarial regularization as a Stackelberg
game. We demonstrate the effectiveness of the proposed framework with extensive
experiments in traffic light control and particle environments. In addition, we
extend ERNIE to mean-field MARL with a formulation based on distributionally
robust optimization that outperforms its non-robust counterpart and is of
independent interest. Our code is available at
https://github.com/abukharin3/ERNIE. | [
"Alexander Bukharin",
"Yan Li",
"Yue Yu",
"Qingru Zhang",
"Zhehui Chen",
"Simiao Zuo",
"Chao Zhang",
"Songan Zhang",
"Tuo Zhao"
] | 2023-10-16 20:14:06 | http://arxiv.org/abs/2310.10810v1 | http://arxiv.org/pdf/2310.10810v1 | 2310.10810v1 |
Regularization properties of adversarially-trained linear regression | State-of-the-art machine learning models can be vulnerable to very small
input perturbations that are adversarially constructed. Adversarial training is
an effective approach to defend against it. Formulated as a min-max problem, it
searches for the best solution when the training data were corrupted by the
worst-case attacks. Linear models are among the simple models where
vulnerabilities can be observed and are the focus of our study. In this case,
adversarial training leads to a convex optimization problem which can be
formulated as the minimization of a finite sum. We provide a comparative
analysis between the solution of adversarial training in linear regression and
other regularization methods. Our main findings are that: (A) Adversarial
training yields the minimum-norm interpolating solution in the
overparameterized regime (more parameters than data), as long as the maximum
disturbance radius is smaller than a threshold. And, conversely, the
minimum-norm interpolator is the solution to adversarial training with a given
radius. (B) Adversarial training can be equivalent to parameter shrinking
methods (ridge regression and Lasso). This happens in the underparametrized
region, for an appropriate choice of adversarial radius and zero-mean
symmetrically distributed covariates. (C) For $\ell_\infty$-adversarial
training -- as in square-root Lasso -- the choice of adversarial radius for
optimal bounds does not depend on the additive noise variance. We confirm our
theoretical findings with numerical examples. | [
"Antônio H. Ribeiro",
"Dave Zachariah",
"Francis Bach",
"Thomas B. Schön"
] | 2023-10-16 20:09:58 | http://arxiv.org/abs/2310.10807v1 | http://arxiv.org/pdf/2310.10807v1 | 2310.10807v1 |
Convolutional Neural Network Model for Diabetic Retinopathy Feature Extraction and Classification | The application of Artificial Intelligence in the medical market brings up
increasing concerns but aids in more timely diagnosis of silent progressing
diseases like Diabetic Retinopathy. In order to diagnose Diabetic Retinopathy
(DR), ophthalmologists use color fundus images, or pictures of the back of the
retina, to identify small distinct features through a difficult and
time-consuming process. Our work creates a novel CNN model and identifies the
severity of DR through fundus image input. We classified 4 known DR features,
including micro-aneurysms, cotton wools, exudates, and hemorrhages, through
convolutional layers and were able to provide an accurate diagnostic without
additional user input. The proposed model is more interpretable and robust to
overfitting. We present initial results with a sensitivity of 97% and an
accuracy of 71%. Our contribution is an interpretable model with similar
accuracy to more complex models. With that, our model advances the field of DR
detection and proves to be a key step towards AI-focused medical diagnosis. | [
"Sharan Subramanian",
"Leilani H. Gilpin"
] | 2023-10-16 20:09:49 | http://arxiv.org/abs/2310.10806v1 | http://arxiv.org/pdf/2310.10806v1 | 2310.10806v1 |
Neural Tangent Kernels Motivate Graph Neural Networks with Cross-Covariance Graphs | Neural tangent kernels (NTKs) provide a theoretical regime to analyze the
learning and generalization behavior of over-parametrized neural networks. For
a supervised learning task, the association between the eigenvectors of the NTK
kernel and given data (a concept referred to as alignment in this paper) can
govern the rate of convergence of gradient descent, as well as generalization
to unseen data. Building upon this concept, we investigate NTKs and alignment
in the context of graph neural networks (GNNs), where our analysis reveals that
optimizing alignment translates to optimizing the graph representation or the
graph shift operator in a GNN. Our results further establish the theoretical
guarantees on the optimality of the alignment for a two-layer GNN and these
guarantees are characterized by the graph shift operator being a function of
the cross-covariance between the input and the output data. The theoretical
insights drawn from the analysis of NTKs are validated by our experiments
focused on a multi-variate time series prediction task for a publicly available
dataset. Specifically, they demonstrate that GNNs with cross-covariance as the
graph shift operator indeed outperform those that operate on the covariance
matrix from only the input data. | [
"Shervin Khalafi",
"Saurabh Sihag",
"Alejandro Ribeiro"
] | 2023-10-16 19:54:21 | http://arxiv.org/abs/2310.10791v1 | http://arxiv.org/pdf/2310.10791v1 | 2310.10791v1 |
Demystifying Poisoning Backdoor Attacks from a Statistical Perspective | The growing dependence on machine learning in real-world applications
emphasizes the importance of understanding and ensuring its safety. Backdoor
attacks pose a significant security risk due to their stealthy nature and
potentially serious consequences. Such attacks involve embedding triggers
within a learning model with the intention of causing malicious behavior when
an active trigger is present while maintaining regular functionality without
it. This paper evaluates the effectiveness of any backdoor attack incorporating
a constant trigger, by establishing tight lower and upper boundaries for the
performance of the compromised model on both clean and backdoor test data. The
developed theory answers a series of fundamental but previously underexplored
problems, including (1) what are the determining factors for a backdoor
attack's success, (2) what is the direction of the most effective backdoor
attack, and (3) when will a human-imperceptible trigger succeed. Our derived
understanding applies to both discriminative and generative models. We also
demonstrate the theory by conducting experiments using benchmark datasets and
state-of-the-art backdoor attack scenarios. | [
"Ganghua Wang",
"Xun Xian",
"Jayanth Srinivasa",
"Ashish Kundu",
"Xuan Bi",
"Mingyi Hong",
"Jie Ding"
] | 2023-10-16 19:35:01 | http://arxiv.org/abs/2310.10780v2 | http://arxiv.org/pdf/2310.10780v2 | 2310.10780v2 |
Correcting model misspecification in physics-informed neural networks (PINNs) | Data-driven discovery of governing equations in computational science has
emerged as a new paradigm for obtaining accurate physical models and as a
possible alternative to theoretical derivations. The recently developed
physics-informed neural networks (PINNs) have also been employed to learn
governing equations given data across diverse scientific disciplines. Despite
the effectiveness of PINNs for discovering governing equations, the physical
models encoded in PINNs may be misspecified in complex systems as some of the
physical processes may not be fully understood, leading to the poor accuracy of
PINN predictions. In this work, we present a general approach to correct the
misspecified physical models in PINNs for discovering governing equations,
given some sparse and/or noisy data. Specifically, we first encode the assumed
physical models, which may be misspecified, then employ other deep neural
networks (DNNs) to model the discrepancy between the imperfect models and the
observational data. Due to the expressivity of DNNs, the proposed method is
capable of reducing the computational errors caused by the model
misspecification and thus enables the applications of PINNs in complex systems
where the physical processes are not exactly known. Furthermore, we utilize the
Bayesian PINNs (B-PINNs) and/or ensemble PINNs to quantify uncertainties
arising from noisy and/or gappy data in the discovered governing equations. A
series of numerical examples including non-Newtonian channel and cavity flows
demonstrate that the added DNNs are capable of correcting the model
misspecification in PINNs and thus reduce the discrepancy between the physical
models and the observational data. We envision that the proposed approach will
extend the applications of PINNs for discovering governing equations in
problems where the physico-chemical or biological processes are not well
understood. | [
"Zongren Zou",
"Xuhui Meng",
"George Em Karniadakis"
] | 2023-10-16 19:25:52 | http://arxiv.org/abs/2310.10776v1 | http://arxiv.org/pdf/2310.10776v1 | 2310.10776v1 |
Gotta be SAFE: A New Framework for Molecular Design | Traditional molecular string representations, such as SMILES, often pose
challenges for AI-driven molecular design due to their non-sequential depiction
of molecular substructures. To address this issue, we introduce Sequential
Attachment-based Fragment Embedding (SAFE), a novel line notation for chemical
structures. SAFE reimagines SMILES strings as an unordered sequence of
interconnected fragment blocks while maintaining full compatibility with
existing SMILES parsers. It streamlines complex generative tasks, including
scaffold decoration, fragment linking, polymer generation, and scaffold
hopping, while facilitating autoregressive generation for fragment-constrained
design, thereby eliminating the need for intricate decoding or graph-based
models. We demonstrate the effectiveness of SAFE by training an
87-million-parameter GPT2-like model on a dataset containing 1.1 billion SAFE
representations. Through extensive experimentation, we show that our SAFE-GPT
model exhibits versatile and robust optimization performance. SAFE opens up new
avenues for the rapid exploration of chemical space under various constraints,
promising breakthroughs in AI-driven molecular design. | [
"Emmanuel Noutahi",
"Cristian Gabellini",
"Michael Craig",
"Jonathan S. C Lim",
"Prudencio Tossou"
] | 2023-10-16 19:12:56 | http://arxiv.org/abs/2310.10773v1 | http://arxiv.org/pdf/2310.10773v1 | 2310.10773v1 |
Unsupervised Lead Sheet Generation via Semantic Compression | Lead sheets have become commonplace in generative music research, being used
as an initial compressed representation for downstream tasks like multitrack
music generation and automatic arrangement. Despite this, researchers have
often fallen back on deterministic reduction methods (such as the skyline
algorithm) to generate lead sheets when seeking paired lead sheets and full
scores, with little attention being paid toward the quality of the lead sheets
themselves and how they accurately reflect their orchestrated counterparts. To
address these issues, we propose the problem of conditional lead sheet
generation (i.e. generating a lead sheet given its full score version), and
show that this task can be formulated as an unsupervised music compression
task, where the lead sheet represents a compressed latent version of the score.
We introduce a novel model, called Lead-AE, that models the lead sheets as a
discrete subselection of the original sequence, using a differentiable top-k
operator to allow for controllable local sparsity constraints. Across both
automatic proxy tasks and direct human evaluations, we find that our method
improves upon the established deterministic baseline and produces coherent
reductions of large multitrack scores. | [
"Zachary Novack",
"Nikita Srivatsan",
"Taylor Berg-Kirkpatrick",
"Julian McAuley"
] | 2023-10-16 19:12:20 | http://arxiv.org/abs/2310.10772v1 | http://arxiv.org/pdf/2310.10772v1 | 2310.10772v1 |
Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models | Neural networks with wide layers have attracted significant attention due to
their equivalence to Gaussian processes, enabling perfect fitting of training
data while maintaining generalization performance, known as benign overfitting.
However, existing results mainly focus on shallow or finite-depth networks,
necessitating a comprehensive analysis of wide neural networks with
infinite-depth layers, such as neural ordinary differential equations (ODEs)
and deep equilibrium models (DEQs). In this paper, we specifically investigate
the deep equilibrium model (DEQ), an infinite-depth neural network with shared
weight matrices across layers. Our analysis reveals that as the width of DEQ
layers approaches infinity, it converges to a Gaussian process, establishing
what is known as the Neural Network and Gaussian Process (NNGP) correspondence.
Remarkably, this convergence holds even when the limits of depth and width are
interchanged, which is not observed in typical infinite-depth Multilayer
Perceptron (MLP) networks. Furthermore, we demonstrate that the associated
Gaussian vector remains non-degenerate for any pairwise distinct input data,
ensuring a strictly positive smallest eigenvalue of the corresponding kernel
matrix using the NNGP kernel. These findings serve as fundamental elements for
studying the training and generalization of DEQs, laying the groundwork for
future research in this area. | [
"Tianxiang Gao",
"Xiaokai Huo",
"Hailiang Liu",
"Hongyang Gao"
] | 2023-10-16 19:00:43 | http://arxiv.org/abs/2310.10767v1 | http://arxiv.org/pdf/2310.10767v1 | 2310.10767v1 |
Exploring hyperelastic material model discovery for human brain cortex: multivariate analysis vs. artificial neural network approaches | Traditional computational methods, such as the finite element analysis, have
provided valuable insights into uncovering the underlying mechanisms of brain
physical behaviors. However, precise predictions of brain physics require
effective constitutive models to represent the intricate mechanical properties
of brain tissue. In this study, we aimed to identify the most favorable
constitutive material model for human brain tissue. To achieve this, we applied
artificial neural network and multiple regression methods to a generalization
of widely accepted classic models, and compared the results obtained from these
two approaches. To evaluate the applicability and efficacy of the model, all
setups were kept consistent across both methods, except for the approach to
prevent potential overfitting. Our results demonstrate that artificial neural
networks are capable of automatically identifying accurate constitutive models
from given admissible estimators. Nonetheless, the five-term and two-term
neural network models trained under single-mode and multi-mode loading
scenarios, were found to be suboptimal and could be further simplified into
two-term and single-term, respectively, with higher accuracy using multiple
regression. Our findings highlight the importance of hyperparameters for the
artificial neural network and emphasize the necessity for detailed
cross-validations of regularization parameters to ensure optimal selection at a
global level in the development of material constitutive models. This study
validates the applicability and accuracy of artificial neural network to
automatically discover constitutive material models with proper regularization
as well as the benefits in model simplification without compromising accuracy
for traditional multivariable regression. | [
"Jixin Hou",
"Nicholas Filla",
"Xianyan Chen",
"Mir Jalil Razavi",
"Tianming Liu",
"Xianqiao Wang"
] | 2023-10-16 18:49:59 | http://arxiv.org/abs/2310.10762v1 | http://arxiv.org/pdf/2310.10762v1 | 2310.10762v1 |
Statistical Barriers to Affine-equivariant Estimation | We investigate the quantitative performance of affine-equivariant estimators
for robust mean estimation. As a natural stability requirement, the
construction of such affine-equivariant estimators has been extensively studied
in the statistics literature. We quantitatively evaluate these estimators under
two outlier models which have been the subject of much recent work: the
heavy-tailed and adversarial corruption settings. We establish lower bounds
which show that affine-equivariance induces a strict degradation in recovery
error with quantitative rates degrading by a factor of $\sqrt{d}$ in both
settings. We find that classical estimators such as the Tukey median (Tukey
'75) and Stahel-Donoho estimator (Stahel '81 and Donoho '82) are either
quantitatively sub-optimal even within the class of affine-equivariant
estimators or lack any quantitative guarantees. On the other hand, recent
estimators with strong quantitative guarantees are not affine-equivariant or
require additional distributional assumptions to achieve it. We remedy this by
constructing a new affine-equivariant estimator which nearly matches our lower
bound. Our estimator is based on a novel notion of a high-dimensional median
which may be of independent interest. Notably, our results are applicable more
broadly to any estimator whose performance is evaluated in the Mahalanobis norm
which, for affine-equivariant estimators, corresponds to an evaluation in
Euclidean norm on isotropic distributions. | [
"Zihao Chen",
"Yeshwanth Cherapanamjeri"
] | 2023-10-16 18:42:00 | http://arxiv.org/abs/2310.10758v1 | http://arxiv.org/pdf/2310.10758v1 | 2310.10758v1 |
Deep Conditional Shape Models for 3D cardiac image segmentation | Delineation of anatomical structures is often the first step of many medical
image analysis workflows. While convolutional neural networks achieve high
performance, these do not incorporate anatomical shape information. We
introduce a novel segmentation algorithm that uses Deep Conditional Shape
models (DCSMs) as a core component. Using deep implicit shape representations,
the algorithm learns a modality-agnostic shape model that can generate the
signed distance functions for any anatomy of interest. To fit the generated
shape to the image, the shape model is conditioned on anatomic landmarks that
can be automatically detected or provided by the user. Finally, we add a
modality-dependent, lightweight refinement network to capture any fine details
not represented by the implicit function. The proposed DCSM framework is
evaluated on the problem of cardiac left ventricle (LV) segmentation from
multiple 3D modalities (contrast-enhanced CT, non-contrasted CT, 3D
echocardiography-3DE). We demonstrate that the automatic DCSM outperforms the
baseline for non-contrasted CT without the local refinement, and with the
refinement for contrasted CT and 3DE, especially with significant improvement
in the Hausdorff distance. The semi-automatic DCSM with user-input landmarks,
while only trained on contrasted CT, achieves greater than 92% Dice for all
modalities. Both automatic DCSM with refinement and semi-automatic DCSM achieve
equivalent or better performance compared to inter-user variability for these
modalities. | [
"Athira J Jacob",
"Puneet Sharma",
"Daniel Ruckert"
] | 2023-10-16 18:38:26 | http://arxiv.org/abs/2310.10756v1 | http://arxiv.org/pdf/2310.10756v1 | 2310.10756v1 |
Mori-Zwanzig latent space Koopman closure for nonlinear autoencoder | The Koopman operator presents an attractive approach to achieve global
linearization of nonlinear systems, making it a valuable method for simplifying
the understanding of complex dynamics. While data-driven methodologies have
exhibited promise in approximating finite Koopman operators, they grapple with
various challenges, such as the judicious selection of observables,
dimensionality reduction, and the ability to predict complex system behaviours
accurately. This study presents a novel approach termed Mori-Zwanzig
autoencoder (MZ-AE) to robustly approximate the Koopman operator in
low-dimensional spaces. The proposed method leverages a nonlinear autoencoder
to extract key observables for approximating a finite invariant Koopman
subspace and integrates a non-Markovian correction mechanism using the
Mori-Zwanzig formalism. Consequently, this approach yields a closed
representation of dynamics within the latent manifold of the nonlinear
autoencoder, thereby enhancing the precision and stability of the Koopman
operator approximation. Demonstrations showcase the technique's ability to
capture regime transitions in the flow around a circular cylinder. It also
provided a low dimensional approximation for chaotic Kuramoto-Sivashinsky with
promising short-term predictability and robust long-term statistical
performance. By bridging the gap between data-driven techniques and the
mathematical foundations of Koopman theory, MZ-AE offers a promising avenue for
improved understanding and prediction of complex nonlinear dynamics. | [
"Priyam Gupta",
"Peter J. Schmid",
"Denis Sipp",
"Taraneh Sayadi",
"Georgios Rigas"
] | 2023-10-16 18:22:02 | http://arxiv.org/abs/2310.10745v1 | http://arxiv.org/pdf/2310.10745v1 | 2310.10745v1 |
Fast Adversarial Label-Flipping Attack on Tabular Data | Machine learning models are increasingly used in fields that require high
reliability such as cybersecurity. However, these models remain vulnerable to
various attacks, among which the adversarial label-flipping attack poses
significant threats. In label-flipping attacks, the adversary maliciously flips
a portion of training labels to compromise the machine learning model. This
paper raises significant concerns as these attacks can camouflage a highly
skewed dataset as an easily solvable classification problem, often misleading
machine learning practitioners into lower defenses and miscalculations of
potential risks. This concern amplifies in tabular data settings, where
identifying true labels requires expertise, allowing malicious label-flipping
attacks to easily slip under the radar. To demonstrate this risk is inherited
in the adversary's objective, we propose FALFA (Fast Adversarial Label-Flipping
Attack), a novel efficient attack for crafting adversarial labels. FALFA is
based on transforming the adversary's objective and employs linear programming
to reduce computational complexity. Using ten real-world tabular datasets, we
demonstrate FALFA's superior attack potential, highlighting the need for robust
defenses against such threats. | [
"Xinglong Chang",
"Gillian Dobbie",
"Jörg Wicker"
] | 2023-10-16 18:20:44 | http://arxiv.org/abs/2310.10744v1 | http://arxiv.org/pdf/2310.10744v1 | 2310.10744v1 |
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design | Metal-organic frameworks (MOFs) are of immense interest in applications such
as gas storage and carbon capture due to their exceptional porosity and tunable
chemistry. Their modular nature has enabled the use of template-based methods
to generate hypothetical MOFs by combining molecular building blocks in
accordance with known network topologies. However, the ability of these methods
to identify top-performing MOFs is often hindered by the limited diversity of
the resulting chemical space. In this work, we propose MOFDiff: a
coarse-grained (CG) diffusion model that generates CG MOF structures through a
denoising diffusion process over the coordinates and identities of the building
blocks. The all-atom MOF structure is then determined through a novel assembly
algorithm. Equivariant graph neural networks are used for the diffusion model
to respect the permutational and roto-translational symmetries. We
comprehensively evaluate our model's capability to generate valid and novel MOF
structures and its effectiveness in designing outstanding MOF materials for
carbon capture applications with molecular simulations. | [
"Xiang Fu",
"Tian Xie",
"Andrew S. Rosen",
"Tommi Jaakkola",
"Jake Smith"
] | 2023-10-16 18:00:15 | http://arxiv.org/abs/2310.10732v1 | http://arxiv.org/pdf/2310.10732v1 | 2310.10732v1 |
A representation learning approach to probe for dynamical dark energy in matter power spectra | We present DE-VAE, a variational autoencoder (VAE) architecture to search for
a compressed representation of dynamical dark energy (DE) models in
observational studies of the cosmic large-scale structure. DE-VAE is trained on
matter power spectra boosts generated at wavenumbers $k\in(0.01-2.5) \
h/\rm{Mpc}$ and at four redshift values $z\in(0.1,0.48,0.78,1.5)$ for the most
typical dynamical DE parametrization with two extra parameters describing an
evolving DE equation of state. The boosts are compressed to a lower-dimensional
representation, which is concatenated with standard cold dark matter (CDM)
parameters and then mapped back to reconstructed boosts; both the compression
and the reconstruction components are parametrized as neural networks.
Remarkably, we find that a single latent parameter is sufficient to predict 95%
(99%) of DE power spectra generated over a broad range of cosmological
parameters within $1\sigma$ ($2\sigma$) of a Gaussian error which includes
cosmic variance, shot noise and systematic effects for a Stage IV-like survey.
This single parameter shows a high mutual information with the two DE
parameters, and these three variables can be linked together with an explicit
equation through symbolic regression. Considering a model with two latent
variables only marginally improves the accuracy of the predictions, and adding
a third latent variable has no significant impact on the model's performance.
We discuss how the DE-VAE architecture can be extended from a proof of concept
to a general framework to be employed in the search for a common
lower-dimensional parametrization of a wide range of beyond-$\Lambda$CDM models
and for different cosmological datasets. Such a framework could then both
inform the development of cosmological surveys by targeting optimal probes, and
provide theoretical insight into the common phenomenological aspects of
beyond-$\Lambda$CDM models. | [
"Davide Piras",
"Lucas Lombriser"
] | 2023-10-16 18:00:01 | http://arxiv.org/abs/2310.10717v1 | http://arxiv.org/pdf/2310.10717v1 | 2310.10717v1 |
A Computational Framework for Solving Wasserstein Lagrangian Flows | The dynamical formulation of the optimal transport can be extended through
various choices of the underlying geometry ($\textit{kinetic energy}$), and the
regularization of density paths ($\textit{potential energy}$). These
combinations yield different variational problems ($\textit{Lagrangians}$),
encompassing many variations of the optimal transport problem such as the
Schr\"odinger bridge, unbalanced optimal transport, and optimal transport with
physical constraints, among others. In general, the optimal density path is
unknown, and solving these variational problems can be computationally
challenging. Leveraging the dual formulation of the Lagrangians, we propose a
novel deep learning based framework approaching all of these problems from a
unified perspective. Our method does not require simulating or backpropagating
through the trajectories of the learned dynamics, and does not need access to
optimal couplings. We showcase the versatility of the proposed framework by
outperforming previous approaches for the single-cell trajectory inference,
where incorporating prior knowledge into the dynamics is crucial for correct
predictions. | [
"Kirill Neklyudov",
"Rob Brekelmans",
"Alexander Tong",
"Lazar Atanackovic",
"Qiang Liu",
"Alireza Makhzani"
] | 2023-10-16 17:59:54 | http://arxiv.org/abs/2310.10649v2 | http://arxiv.org/pdf/2310.10649v2 | 2310.10649v2 |
A Survey on Video Diffusion Models | The recent wave of AI-generated content (AIGC) has witnessed substantial
success in computer vision, with the diffusion model playing a crucial role in
this achievement. Due to their impressive generative capabilities, diffusion
models are gradually superseding methods based on GANs and auto-regressive
Transformers, demonstrating exceptional performance not only in image
generation and editing, but also in the realm of video-related research.
However, existing surveys mainly focus on diffusion models in the context of
image generation, with few up-to-date reviews on their application in the video
domain. To address this gap, this paper presents a comprehensive review of
video diffusion models in the AIGC era. Specifically, we begin with a concise
introduction to the fundamentals and evolution of diffusion models.
Subsequently, we present an overview of research on diffusion models in the
video domain, categorizing the work into three key areas: video generation,
video editing, and other video understanding tasks. We conduct a thorough
review of the literature in these three key areas, including further
categorization and practical contributions in the field. Finally, we discuss
the challenges faced by research in this domain and outline potential future
developmental trends. A comprehensive list of video diffusion models studied in
this survey is available at
https://github.com/ChenHsing/Awesome-Video-Diffusion-Models. | [
"Zhen Xing",
"Qijun Feng",
"Haoran Chen",
"Qi Dai",
"Han Hu",
"Hang Xu",
"Zuxuan Wu",
"Yu-Gang Jiang"
] | 2023-10-16 17:59:28 | http://arxiv.org/abs/2310.10647v1 | http://arxiv.org/pdf/2310.10647v1 | 2310.10647v1 |
In-Context Pretraining: Language Modeling Beyond Document Boundaries | Large language models (LMs) are currently trained to predict tokens given
document prefixes, enabling them to directly perform long-form generation and
prompting-style tasks which can be reduced to document completion. Existing
pretraining pipelines train LMs by concatenating random sets of short documents
to create input contexts but the prior documents provide no signal for
predicting the next document. We instead present In-Context Pretraining, a new
approach where language models are pretrained on a sequence of related
documents, thereby explicitly encouraging them to read and reason across
document boundaries. We can do In-Context Pretraining by simply changing the
document ordering so that each context contains related documents, and directly
applying existing pretraining pipelines. However, this document sorting problem
is challenging. There are billions of documents and we would like the sort to
maximize contextual similarity for every document without repeating any data.
To do this, we introduce approximate algorithms for finding related documents
with efficient nearest neighbor search and constructing coherent input contexts
with a graph traversal algorithm. Our experiments show In-Context Pretraining
offers a simple and scalable approach to significantly enhance LMs'performance:
we see notable improvements in tasks that require more complex contextual
reasoning, including in-context learning (+8%), reading comprehension (+15%),
faithfulness to previous contexts (+16%), long-context reasoning (+5%), and
retrieval augmentation (+9%). | [
"Weijia Shi",
"Sewon Min",
"Maria Lomeli",
"Chunting Zhou",
"Margaret Li",
"Xi Victoria Lin",
"Noah A. Smith",
"Luke Zettlemoyer",
"Scott Yih",
"Mike Lewis"
] | 2023-10-16 17:57:12 | http://arxiv.org/abs/2310.10638v3 | http://arxiv.org/pdf/2310.10638v3 | 2310.10638v3 |
Efficacy of Dual-Encoders for Extreme Multi-Label Classification | Dual-encoder models have demonstrated significant success in dense retrieval
tasks for open-domain question answering that mostly involves zero-shot and
few-shot scenarios. However, their performance in many-shot retrieval problems
where training data is abundant, such as extreme multi-label classification
(XMC), remains under-explored. Existing empirical evidence suggests that, for
such problems, the dual-encoder method's accuracies lag behind the performance
of state-of-the-art (SOTA) extreme classification methods that grow the number
of learnable parameters linearly with the number of classes. As a result, some
recent extreme classification techniques use a combination of dual-encoders and
a learnable classification head for each class to excel on these tasks. In this
paper, we investigate the potential of "pure" DE models in XMC tasks. Our
findings reveal that when trained correctly standard dual-encoders can match or
outperform SOTA extreme classification methods by up to 2% at Precision@1 even
on the largest XMC datasets while being 20x smaller in terms of the number of
trainable parameters. We further propose a differentiable topk error-based loss
function, which can be used to specifically optimize for Recall@k metrics. We
include our PyTorch implementation along with other resources for reproducing
the results in the supplementary material. | [
"Nilesh Gupta",
"Devvrit Khatri",
"Ankit S Rawat",
"Srinadh Bhojanapalli",
"Prateek Jain",
"Inderjit S Dhillon"
] | 2023-10-16 17:55:43 | http://arxiv.org/abs/2310.10636v1 | http://arxiv.org/pdf/2310.10636v1 | 2310.10636v1 |
Towards Scenario-based Safety Validation for Autonomous Trains with Deep Generative Models | Modern AI techniques open up ever-increasing possibilities for autonomous
vehicles, but how to appropriately verify the reliability of such systems
remains unclear. A common approach is to conduct safety validation based on a
predefined Operational Design Domain (ODD) describing specific conditions under
which a system under test is required to operate properly. However, collecting
sufficient realistic test cases to ensure comprehensive ODD coverage is
challenging. In this paper, we report our practical experiences regarding the
utility of data simulation with deep generative models for scenario-based ODD
validation. We consider the specific use case of a camera-based rail-scene
segmentation system designed to support autonomous train operation. We
demonstrate the capabilities of semantically editing railway scenes with deep
generative models to make a limited amount of test data more representative. We
also show how our approach helps to analyze the degree to which a system
complies with typical ODD requirements. Specifically, we focus on evaluating
proper operation under different lighting and weather conditions as well as
while transitioning between them. | [
"Thomas Decker",
"Ananta R. Bhattarai",
"Michael Lebacher"
] | 2023-10-16 17:55:14 | http://arxiv.org/abs/2310.10635v1 | http://arxiv.org/pdf/2310.10635v1 | 2310.10635v1 |
Certainty In, Certainty Out: REVQCs for Quantum Machine Learning | The field of Quantum Machine Learning (QML) has emerged recently in the hopes
of finding new machine learning protocols or exponential speedups for classical
ones. Apart from problems with vanishing gradients and efficient encoding
methods, these speedups are hard to find because the sampling nature of quantum
computers promotes either simulating computations classically or running them
many times on quantum computers in order to use approximate expectation values
in gradient calculations. In this paper, we make a case for setting high
single-sample accuracy as a primary goal. We discuss the statistical theory
which enables highly accurate and precise sample inference, and propose a
method of reversed training towards this end. We show the effectiveness of this
training method by assessing several effective variational quantum circuits
(VQCs), trained in both the standard and reversed directions, on random binary
subsets of the MNIST and MNIST Fashion datasets, on which our method provides
an increase of $10-15\%$ in single-sample inference accuracy. | [
"Hannah Helgesen",
"Michael Felsberg",
"Jan-Åke Larsson"
] | 2023-10-16 17:53:30 | http://arxiv.org/abs/2310.10629v1 | http://arxiv.org/pdf/2310.10629v1 | 2310.10629v1 |
Video Language Planning | We are interested in enabling visual planning for complex long-horizon tasks
in the space of generated videos and language, leveraging recent advances in
large generative models pretrained on Internet-scale data. To this end, we
present video language planning (VLP), an algorithm that consists of a tree
search procedure, where we train (i) vision-language models to serve as both
policies and value functions, and (ii) text-to-video models as dynamics models.
VLP takes as input a long-horizon task instruction and current image
observation, and outputs a long video plan that provides detailed multimodal
(video and language) specifications that describe how to complete the final
task. VLP scales with increasing computation budget where more computation time
results in improved video plans, and is able to synthesize long-horizon video
plans across different robotics domains: from multi-object rearrangement, to
multi-camera bi-arm dexterous manipulation. Generated video plans can be
translated into real robot actions via goal-conditioned policies, conditioned
on each intermediate frame of the generated video. Experiments show that VLP
substantially improves long-horizon task success rates compared to prior
methods on both simulated and real robots (across 3 hardware platforms). | [
"Yilun Du",
"Mengjiao Yang",
"Pete Florence",
"Fei Xia",
"Ayzaan Wahid",
"Brian Ichter",
"Pierre Sermanet",
"Tianhe Yu",
"Pieter Abbeel",
"Joshua B. Tenenbaum",
"Leslie Kaelbling",
"Andy Zeng",
"Jonathan Tompson"
] | 2023-10-16 17:48:45 | http://arxiv.org/abs/2310.10625v1 | http://arxiv.org/pdf/2310.10625v1 | 2310.10625v1 |
Generating Summaries with Controllable Readability Levels | Readability refers to how easily a reader can understand a written text.
Several factors affect the readability level, such as the complexity of the
text, its subject matter, and the reader's background knowledge. Generating
summaries based on different readability levels is critical for enabling
knowledge consumption by diverse audiences. However, current text generation
approaches lack refined control, resulting in texts that are not customized to
readers' proficiency levels. In this work, we bridge this gap and study
techniques to generate summaries at specified readability levels. Unlike
previous methods that focus on a specific readability level (e.g., lay
summarization), we generate summaries with fine-grained control over their
readability. We develop three text generation techniques for controlling
readability: (1) instruction-based readability control, (2) reinforcement
learning to minimize the gap between requested and observed readability and (3)
a decoding approach that uses lookahead to estimate the readability of upcoming
decoding steps. We show that our generation methods significantly improve
readability control on news summarization (CNN/DM dataset), as measured by
various readability metrics and human judgement, establishing strong baselines
for controllable readability in summarization. | [
"Leonardo F. R. Ribeiro",
"Mohit Bansal",
"Markus Dreyer"
] | 2023-10-16 17:46:26 | http://arxiv.org/abs/2310.10623v1 | http://arxiv.org/pdf/2310.10623v1 | 2310.10623v1 |
How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations | While large language models based on the transformer architecture have
demonstrated remarkable in-context learning (ICL) capabilities, understandings
of such capabilities are still in an early stage, where existing theory and
mechanistic understanding focus mostly on simple scenarios such as learning
simple function classes. This paper takes initial steps on understanding ICL in
more complex scenarios, by studying learning with representations. Concretely,
we construct synthetic in-context learning problems with a compositional
structure, where the label depends on the input through a possibly complex but
fixed representation function, composed with a linear function that differs in
each instance. By construction, the optimal ICL algorithm first transforms the
inputs by the representation function, and then performs linear ICL on top of
the transformed dataset. We show theoretically the existence of transformers
that approximately implement such algorithms with mild depth and size.
Empirically, we find trained transformers consistently achieve near-optimal ICL
performance in this setting, and exhibit the desired dissection where lower
layers transforms the dataset and upper layers perform linear ICL. Through
extensive probing and a new pasting experiment, we further reveal several
mechanisms within the trained transformers, such as concrete copying behaviors
on both the inputs and the representations, linear ICL capability of the upper
layers alone, and a post-ICL representation selection mechanism in a harder
mixture setting. These observed mechanisms align well with our theory and may
shed light on how transformers perform ICL in more realistic scenarios. | [
"Tianyu Guo",
"Wei Hu",
"Song Mei",
"Huan Wang",
"Caiming Xiong",
"Silvio Savarese",
"Yu Bai"
] | 2023-10-16 17:40:49 | http://arxiv.org/abs/2310.10616v1 | http://arxiv.org/pdf/2310.10616v1 | 2310.10616v1 |
IW-GAE: Importance weighted group accuracy estimation for improved calibration and model selection in unsupervised domain adaptation | Reasoning about a model's accuracy on a test sample from its confidence is a
central problem in machine learning, being connected to important applications
such as uncertainty representation, model selection, and exploration. While
these connections have been well-studied in the i.i.d. settings, distribution
shifts pose significant challenges to the traditional methods. Therefore, model
calibration and model selection remain challenging in the unsupervised domain
adaptation problem--a scenario where the goal is to perform well in a
distribution shifted domain without labels. In this work, we tackle
difficulties coming from distribution shifts by developing a novel importance
weighted group accuracy estimator. Specifically, we formulate an optimization
problem for finding an importance weight that leads to an accurate group
accuracy estimation in the distribution shifted domain with theoretical
analyses. Extensive experiments show the effectiveness of group accuracy
estimation on model calibration and model selection. Our results emphasize the
significance of group accuracy estimation for addressing challenges in
unsupervised domain adaptation, as an orthogonal improvement direction with
improving transferability of accuracy. | [
"Taejong Joo",
"Diego Klabjan"
] | 2023-10-16 17:35:29 | http://arxiv.org/abs/2310.10611v1 | http://arxiv.org/pdf/2310.10611v1 | 2310.10611v1 |
Quantifying Assistive Robustness Via the Natural-Adversarial Frontier | Our ultimate goal is to build robust policies for robots that assist people.
What makes this hard is that people can behave unexpectedly at test time,
potentially interacting with the robot outside its training distribution and
leading to failures. Even just measuring robustness is a challenge. Adversarial
perturbations are the default, but they can paint the wrong picture: they can
correspond to human motions that are unlikely to occur during natural
interactions with people. A robot policy might fail under small adversarial
perturbations but work under large natural perturbations. We propose that
capturing robustness in these interactive settings requires constructing and
analyzing the entire natural-adversarial frontier: the Pareto-frontier of human
policies that are the best trade-offs between naturalness and low robot
performance. We introduce RIGID, a method for constructing this frontier by
training adversarial human policies that trade off between minimizing robot
reward and acting human-like (as measured by a discriminator). On an Assistive
Gym task, we use RIGID to analyze the performance of standard collaborative
Reinforcement Learning, as well as the performance of existing methods meant to
increase robustness. We also compare the frontier RIGID identifies with the
failures identified in expert adversarial interaction, and with
naturally-occurring failures during user interaction. Overall, we find evidence
that RIGID can provide a meaningful measure of robustness predictive of
deployment performance, and uncover failure cases in human-robot interaction
that are difficult to find manually. https://ood-human.github.io. | [
"Jerry Zhi-Yang He",
"Zackory Erickson",
"Daniel S. Brown",
"Anca D. Dragan"
] | 2023-10-16 17:34:54 | http://arxiv.org/abs/2310.10610v1 | http://arxiv.org/pdf/2310.10610v1 | 2310.10610v1 |
BayRnTune: Adaptive Bayesian Domain Randomization via Strategic Fine-tuning | Domain randomization (DR), which entails training a policy with randomized
dynamics, has proven to be a simple yet effective algorithm for reducing the
gap between simulation and the real world. However, DR often requires careful
tuning of randomization parameters. Methods like Bayesian Domain Randomization
(Bayesian DR) and Active Domain Randomization (Adaptive DR) address this issue
by automating parameter range selection using real-world experience. While
effective, these algorithms often require long computation time, as a new
policy is trained from scratch every iteration. In this work, we propose
Adaptive Bayesian Domain Randomization via Strategic Fine-tuning (BayRnTune),
which inherits the spirit of BayRn but aims to significantly accelerate the
learning processes by fine-tuning from previously learned policy. This idea
leads to a critical question: which previous policy should we use as a prior
during fine-tuning? We investigated four different fine-tuning strategies and
compared them against baseline algorithms in five simulated environments,
ranging from simple benchmark tasks to more complex legged robot environments.
Our analysis demonstrates that our method yields better rewards in the same
amount of timesteps compared to vanilla domain randomization or Bayesian DR. | [
"Tianle Huang",
"Nitish Sontakke",
"K. Niranjan Kumar",
"Irfan Essa",
"Stefanos Nikolaidis",
"Dennis W. Hong",
"Sehoon Ha"
] | 2023-10-16 17:32:23 | http://arxiv.org/abs/2310.10606v1 | http://arxiv.org/pdf/2310.10606v1 | 2310.10606v1 |
ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model | Through evolution, nature has presented a set of remarkable protein
materials, including elastins, silks, keratins and collagens with superior
mechanical performances that play crucial roles in mechanobiology. However,
going beyond natural designs to discover proteins that meet specified
mechanical properties remains challenging. Here we report a generative model
that predicts protein designs to meet complex nonlinear mechanical
property-design objectives. Our model leverages deep knowledge on protein
sequences from a pre-trained protein language model and maps mechanical
unfolding responses to create novel proteins. Via full-atom molecular
simulations for direct validation, we demonstrate that the designed proteins
are novel, and fulfill the targeted mechanical properties, including unfolding
energy and mechanical strength, as well as the detailed unfolding
force-separation curves. Our model offers rapid pathways to explore the
enormous mechanobiological protein sequence space unconstrained by biological
synthesis, using mechanical features as target to enable the discovery of
protein materials with superior mechanical properties. | [
"Bo Ni",
"David L. Kaplan",
"Markus J. Buehler"
] | 2023-10-16 17:31:34 | http://arxiv.org/abs/2310.10605v1 | http://arxiv.org/pdf/2310.10605v1 | 2310.10605v1 |
Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems | Recently, machine learning, particularly message-passing graph neural
networks (MPNNs), has gained traction in enhancing exact optimization
algorithms. For example, MPNNs speed up solving mixed-integer optimization
problems by imitating computational intensive heuristics like strong branching,
which entails solving multiple linear optimization problems (LPs). Despite the
empirical success, the reasons behind MPNNs' effectiveness in emulating linear
optimization remain largely unclear. Here, we show that MPNNs can simulate
standard interior-point methods for LPs, explaining their practical success.
Furthermore, we highlight how MPNNs can serve as a lightweight proxy for
solving LPs, adapting to a given problem instance distribution. Empirically, we
show that MPNNs solve LP relaxations of standard combinatorial optimization
problems close to optimality, often surpassing conventional solvers and
competing approaches in solving time. | [
"Chendi Qian",
"Didier Chételat",
"Christopher Morris"
] | 2023-10-16 17:31:25 | http://arxiv.org/abs/2310.10603v1 | http://arxiv.org/pdf/2310.10603v1 | 2310.10603v1 |
Pareto Optimization to Accelerate Multi-Objective Virtual Screening | The discovery of therapeutic molecules is fundamentally a multi-objective
optimization problem. One formulation of the problem is to identify molecules
that simultaneously exhibit strong binding affinity for a target protein,
minimal off-target interactions, and suitable pharmacokinetic properties.
Inspired by prior work that uses active learning to accelerate the
identification of strong binders, we implement multi-objective Bayesian
optimization to reduce the computational cost of multi-property virtual
screening and apply it to the identification of ligands predicted to be
selective based on docking scores to on- and off-targets. We demonstrate the
superiority of Pareto optimization over scalarization across three case
studies. Further, we use the developed optimization tool to search a virtual
library of over 4M molecules for those predicted to be selective dual
inhibitors of EGFR and IGF1R, acquiring 100% of the molecules that form the
library's Pareto front after exploring only 8% of the library. This workflow
and associated open source software can reduce the screening burden of
molecular design projects and is complementary to research aiming to improve
the accuracy of binding predictions and other molecular properties. | [
"Jenna C. Fromer",
"David E. Graff",
"Connor W. Coley"
] | 2023-10-16 17:19:46 | http://arxiv.org/abs/2310.10598v1 | http://arxiv.org/pdf/2310.10598v1 | 2310.10598v1 |
Who Are All The Stochastic Parrots Imitating? They Should Tell Us! | Both standalone language models (LMs) as well as LMs within downstream-task
systems have been shown to generate statements which are factually untrue. This
problem is especially severe for low-resource languages, where training data is
scarce and of worse quality than for high-resource languages. In this opinion
piece, we argue that LMs in their current state will never be fully trustworthy
in critical settings and suggest a possible novel strategy to handle this
issue: by building LMs such that can cite their sources - i.e., point a user to
the parts of their training data that back up their outputs. We first discuss
which current NLP tasks would or would not benefit from such models. We then
highlight the expected benefits such models would bring, e.g., quick
verifiability of statements. We end by outlining the individual tasks that
would need to be solved on the way to developing LMs with the ability to cite.
We hope to start a discussion about the field's current approach to building
LMs, especially for low-resource languages, and the role of the training data
in explaining model generations. | [
"Sagi Shaier",
"Lawrence E. Hunter",
"Katharina von der Wense"
] | 2023-10-16 16:57:55 | http://arxiv.org/abs/2310.10583v1 | http://arxiv.org/pdf/2310.10583v1 | 2310.10583v1 |
Emerging Challenges in Personalized Medicine: Assessing Demographic Effects on Biomedical Question Answering Systems | State-of-the-art question answering (QA) models exhibit a variety of social
biases (e.g., with respect to sex or race), generally explained by similar
issues in their training data. However, what has been overlooked so far is that
in the critical domain of biomedicine, any unjustified change in model output
due to patient demographics is problematic: it results in the unfair treatment
of patients. Selecting only questions on biomedical topics whose answers do not
depend on ethnicity, sex, or sexual orientation, we ask the following research
questions: (RQ1) Do the answers of QA models change when being provided with
irrelevant demographic information? (RQ2) Does the answer of RQ1 differ between
knowledge graph (KG)-grounded and text-based QA systems? We find that
irrelevant demographic information change up to 15% of the answers of a
KG-grounded system and up to 23% of the answers of a text-based system,
including changes that affect accuracy. We conclude that unjustified answer
changes caused by patient demographics are a frequent phenomenon, which raises
fairness concerns and should be paid more attention to. | [
"Sagi Shaier",
"Kevin Bennett",
"Lawrence Hunter",
"Katharina von der Wense"
] | 2023-10-16 16:45:52 | http://arxiv.org/abs/2310.10571v1 | http://arxiv.org/pdf/2310.10571v1 | 2310.10571v1 |
HelmSim: Learning Helmholtz Dynamics for Interpretable Fluid Simulation | Fluid simulation is a long-standing challenge due to the intrinsic
high-dimensional non-linear dynamics. Previous methods usually utilize the
non-linear modeling capability of deep models to directly estimate velocity
fields for future prediction. However, skipping over inherent physical
properties but directly learning superficial velocity fields will overwhelm the
model from generating precise or physics-reliable results. In this paper, we
propose the HelmSim toward an accurate and interpretable simulator for fluid.
Inspired by the Helmholtz theorem, we design a HelmDynamic block to learn the
Helmholtz dynamics, which decomposes fluid dynamics into more solvable
curl-free and divergence-free parts, physically corresponding to potential and
stream functions of fluid. By embedding the HelmDynamic block into a Multiscale
Integration Network, HelmSim can integrate learned Helmholtz dynamics along
temporal dimension in multiple spatial scales to yield future fluid. Comparing
with previous velocity estimating methods, HelmSim is faithfully derived from
Helmholtz theorem and ravels out complex fluid dynamics with physically
interpretable evidence. Experimentally, our proposed HelmSim achieves the
consistent state-of-the-art in both numerical simulated and real-world observed
benchmarks, even for scenarios with complex boundaries. | [
"Lanxiang Xing",
"Haixu Wu",
"Yuezhou Ma",
"Jianmin Wang",
"Mingsheng Long"
] | 2023-10-16 16:38:32 | http://arxiv.org/abs/2310.10565v1 | http://arxiv.org/pdf/2310.10565v1 | 2310.10565v1 |
RefConv: Re-parameterized Refocusing Convolution for Powerful ConvNets | We propose Re-parameterized Refocusing Convolution (RefConv) as a replacement
for regular convolutional layers, which is a plug-and-play module to improve
the performance without any inference costs. Specifically, given a pre-trained
model, RefConv applies a trainable Refocusing Transformation to the basis
kernels inherited from the pre-trained model to establish connections among the
parameters. For example, a depth-wise RefConv can relate the parameters of a
specific channel of convolution kernel to the parameters of the other kernel,
i.e., make them refocus on the other parts of the model they have never
attended to, rather than focus on the input features only. From another
perspective, RefConv augments the priors of existing model structures by
utilizing the representations encoded in the pre-trained parameters as the
priors and refocusing on them to learn novel representations, thus further
enhancing the representational capacity of the pre-trained model. Experimental
results validated that RefConv can improve multiple CNN-based models by a clear
margin on image classification (up to 1.47% higher top-1 accuracy on ImageNet),
object detection and semantic segmentation without introducing any extra
inference costs or altering the original model structure. Further studies
demonstrated that RefConv can reduce the redundancy of channels and smooth the
loss landscape, which explains its effectiveness. | [
"Zhicheng Cai",
"Xiaohan Ding",
"Qiu Shen",
"Xun Cao"
] | 2023-10-16 16:36:54 | http://arxiv.org/abs/2310.10563v1 | http://arxiv.org/pdf/2310.10563v1 | 2310.10563v1 |
Towards the Imagenets of ML4EDA | Despite the growing interest in ML-guided EDA tools from RTL to GDSII, there
are no standard datasets or prototypical learning tasks defined for the EDA
problem domain. Experience from the computer vision community suggests that
such datasets are crucial to spur further progress in ML for EDA. Here we
describe our experience curating two large-scale, high-quality datasets for
Verilog code generation and logic synthesis. The first, VeriGen, is a dataset
of Verilog code collected from GitHub and Verilog textbooks. The second,
OpenABC-D, is a large-scale, labeled dataset designed to aid ML for logic
synthesis tasks. The dataset consists of 870,000 And-Inverter-Graphs (AIGs)
produced from 1500 synthesis runs on a large number of open-source hardware
projects. In this paper we will discuss challenges in curating, maintaining and
growing the size and scale of these datasets. We will also touch upon questions
of dataset quality and security, and the use of novel data augmentation tools
that are tailored for the hardware domain. | [
"Animesh Basak Chowdhury",
"Shailja Thakur",
"Hammond Pearce",
"Ramesh Karri",
"Siddharth Garg"
] | 2023-10-16 16:35:03 | http://arxiv.org/abs/2310.10560v1 | http://arxiv.org/pdf/2310.10560v1 | 2310.10560v1 |
Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data | Estimating treatment effects over time is relevant in many real-world
applications, such as precision medicine, epidemiology, economy, and marketing.
Many state-of-the-art methods either assume the observations of all confounders
or seek to infer the unobserved ones. We take a different perspective by
assuming unobserved risk factors, i.e., adjustment variables that affect only
the sequence of outcomes. Under unconfoundedness, we target the Individual
Treatment Effect (ITE) estimation with unobserved heterogeneity in the
treatment response due to missing risk factors. We address the challenges posed
by time-varying effects and unobserved adjustment variables. Led by theoretical
results over the validity of the learned adjustment variables and
generalization bounds over the treatment effect, we devise Causal DVAE (CDVAE).
This model combines a Dynamic Variational Autoencoder (DVAE) framework with a
weighting strategy using propensity scores to estimate counterfactual
responses. The CDVAE model allows for accurate estimation of ITE and captures
the underlying heterogeneity in longitudinal data. Evaluations of our model
show superior performance over state-of-the-art models. | [
"Mouad El Bouchattaoui",
"Myriam Tami",
"Benoit Lepetit",
"Paul-Henry Cournède"
] | 2023-10-16 16:32:35 | http://arxiv.org/abs/2310.10559v1 | http://arxiv.org/pdf/2310.10559v1 | 2310.10559v1 |
Sample Complexity of Preference-Based Nonparametric Off-Policy Evaluation with Deep Networks | A recently popular approach to solving reinforcement learning is with data
from human preferences. In fact, human preference data are now used with
classic reinforcement learning algorithms such as actor-critic methods, which
involve evaluating an intermediate policy over a reward learned from human
preference data with distribution shift, known as off-policy evaluation (OPE).
Such algorithm includes (i) learning reward function from human preference
dataset, and (ii) learning expected cumulative reward of a target policy.
Despite the huge empirical success, existing OPE methods with preference data
often lack theoretical understanding and rely heavily on heuristics. In this
paper, we study the sample efficiency of OPE with human preference and
establish a statistical guarantee for it. Specifically, we approach OPE by
learning the value function by fitted-Q-evaluation with a deep neural network.
By appropriately selecting the size of a ReLU network, we show that one can
leverage any low-dimensional manifold structure in the Markov decision process
and obtain a sample-efficient estimator without suffering from the curse of
high data ambient dimensionality. Under the assumption of high reward
smoothness, our results \textit{almost align with the classical OPE results
with observable reward data}. To the best of our knowledge, this is the first
result that establishes a \textit{provably efficient} guarantee for off-policy
evaluation with RLHF. | [
"Zihao Li",
"Xiang Ji",
"Minshuo Chen",
"Mengdi Wang"
] | 2023-10-16 16:27:06 | http://arxiv.org/abs/2310.10556v1 | http://arxiv.org/pdf/2310.10556v1 | 2310.10556v1 |
Population-based wind farm monitoring based on a spatial autoregressive approach | An important challenge faced by wind farm operators is to reduce operation
and maintenance cost. Structural health monitoring provides a means of cost
reduction through minimising unnecessary maintenance trips as well as
prolonging turbine service life. Population-based structural health monitoring
can further reduce the cost of health monitoring systems by implementing one
system for multiple structures (i.e.~turbines). At the same time, shared data
within a population of structures may improve the predictions of structural
behaviour. To monitor turbine performance at a population/farm level, an
important initial step is to construct a model that describes the behaviour of
all turbines under normal conditions. This paper proposes a population-level
model that explicitly captures the spatial and temporal correlations (between
turbines) induced by the wake effect. The proposed model is a Gaussian
process-based spatial autoregressive model, named here a GP-SPARX model. This
approach is developed since (a) it reflects our physical understanding of the
wake effect, and (b) it benefits from a stochastic data-based learner. A case
study is provided to demonstrate the capability of the GP-SPARX model in
capturing spatial and temporal variations as well as its potential
applicability in a health monitoring system. | [
"W. Lin",
"K. Worden",
"E. J. Cross"
] | 2023-10-16 16:26:40 | http://arxiv.org/abs/2310.10555v1 | http://arxiv.org/pdf/2310.10555v1 | 2310.10555v1 |
TacticAI: an AI assistant for football tactics | Identifying key patterns of tactics implemented by rival teams, and
developing effective responses, lies at the heart of modern football. However,
doing so algorithmically remains an open research challenge. To address this
unmet need, we propose TacticAI, an AI football tactics assistant developed and
evaluated in close collaboration with domain experts from Liverpool FC. We
focus on analysing corner kicks, as they offer coaches the most direct
opportunities for interventions and improvements. TacticAI incorporates both a
predictive and a generative component, allowing the coaches to effectively
sample and explore alternative player setups for each corner kick routine and
to select those with the highest predicted likelihood of success. We validate
TacticAI on a number of relevant benchmark tasks: predicting receivers and shot
attempts and recommending player position adjustments. The utility of TacticAI
is validated by a qualitative study conducted with football domain experts at
Liverpool FC. We show that TacticAI's model suggestions are not only
indistinguishable from real tactics, but also favoured over existing tactics
90% of the time, and that TacticAI offers an effective corner kick retrieval
system. TacticAI achieves these results despite the limited availability of
gold-standard data, achieving data efficiency through geometric deep learning. | [
"Zhe Wang",
"Petar Veličković",
"Daniel Hennes",
"Nenad Tomašev",
"Laurel Prince",
"Michael Kaisers",
"Yoram Bachrach",
"Romuald Elie",
"Li Kevin Wenliang",
"Federico Piccinini",
"William Spearman",
"Ian Graham",
"Jerome Connor",
"Yi Yang",
"Adrià Recasens",
"Mina Khan",
"Nathalie Beauguerlange",
"Pablo Sprechmann",
"Pol Moreno",
"Nicolas Heess",
"Michael Bowling",
"Demis Hassabis",
"Karl Tuyls"
] | 2023-10-16 16:25:15 | http://arxiv.org/abs/2310.10553v2 | http://arxiv.org/pdf/2310.10553v2 | 2310.10553v2 |
Deep learning applied to EEG data with different montages using spatial attention | The ability of Deep Learning to process and extract relevant information in
complex brain dynamics from raw EEG data has been demonstrated in various
recent works. Deep learning models, however, have also been shown to perform
best on large corpora of data. When processing EEG, a natural approach is to
combine EEG datasets from different experiments to train large deep-learning
models. However, most EEG experiments use custom channel montages, requiring
the data to be transformed into a common space. Previous methods have used the
raw EEG signal to extract features of interest and focused on using a common
feature space across EEG datasets. While this is a sensible approach, it
underexploits the potential richness of EEG raw data. Here, we explore using
spatial attention applied to EEG electrode coordinates to perform channel
harmonization of raw EEG data, allowing us to train deep learning on EEG data
using different montages. We test this model on a gender classification task.
We first show that spatial attention increases model performance. Then, we show
that a deep learning model trained on data using different channel montages
performs significantly better than deep learning models trained on fixed 23-
and 128-channel data montages. | [
"Dung Truong",
"Muhammad Abdullah Khalid",
"Arnaud Delorme"
] | 2023-10-16 16:17:33 | http://arxiv.org/abs/2310.10550v1 | http://arxiv.org/pdf/2310.10550v1 | 2310.10550v1 |
Optimal vintage factor analysis with deflation varimax | Vintage factor analysis is one important type of factor analysis that aims to
first find a low-dimensional representation of the original data, and then to
seek a rotation such that the rotated low-dimensional representation is
scientifically meaningful. Perhaps the most widely used vintage factor analysis
is the Principal Component Analysis (PCA) followed by the varimax rotation.
Despite its popularity, little theoretical guarantee can be provided mainly
because varimax rotation requires to solve a non-convex optimization over the
set of orthogonal matrices.
In this paper, we propose a deflation varimax procedure that solves each row
of an orthogonal matrix sequentially. In addition to its net computational gain
and flexibility, we are able to fully establish theoretical guarantees for the
proposed procedure in a broad context.
Adopting this new varimax approach as the second step after PCA, we further
analyze this two step procedure under a general class of factor models. Our
results show that it estimates the factor loading matrix in the optimal rate
when the signal-to-noise-ratio (SNR) is moderate or large. In the low SNR
regime, we offer possible improvement over using PCA and the deflation
procedure when the additive noise under the factor model is structured. The
modified procedure is shown to be optimal in all SNR regimes. Our theory is
valid for finite sample and allows the number of the latent factors to grow
with the sample size as well as the ambient dimension to grow with, or even
exceed, the sample size.
Extensive simulation and real data analysis further corroborate our
theoretical findings. | [
"Xin Bing",
"Dian Jin",
"Yuqian Zhang"
] | 2023-10-16 16:14:43 | http://arxiv.org/abs/2310.10545v1 | http://arxiv.org/pdf/2310.10545v1 | 2310.10545v1 |
Efficient Dataset Distillation through Alignment with Smooth and High-Quality Expert Trajectories | Training a large and state-of-the-art machine learning model typically
necessitates the use of large-scale datasets, which, in turn, makes the
training and parameter-tuning process expensive and time-consuming. Some
researchers opt to distil information from real-world datasets into tiny and
compact synthetic datasets while maintaining their ability to train a
well-performing model, hence proposing a data-efficient method known as Dataset
Distillation (DD). Despite recent progress in this field, existing methods
still underperform and cannot effectively replace large datasets. In this
paper, unlike previous methods that focus solely on improving the efficacy of
student distillation, we are the first to recognize the important interplay
between expert and student. We argue the significant impact of expert
smoothness when employing more potent expert trajectories in subsequent dataset
distillation. Based on this, we introduce the integration of clipping loss and
gradient penalty to regulate the rate of parameter changes in expert
trajectories. Furthermore, in response to the sensitivity exhibited towards
randomly initialized variables during distillation, we propose representative
initialization for synthetic dataset and balanced inner-loop loss. Finally, we
present two enhancement strategies, namely intermediate matching loss and
weight perturbation, to mitigate the potential occurrence of cumulative errors.
We conduct extensive experiments on datasets of different scales, sizes, and
resolutions. The results demonstrate that the proposed method significantly
outperforms prior methods. | [
"Jiyuan Shen",
"Wenzhuo Yang",
"Kwok-Yan Lam"
] | 2023-10-16 16:13:53 | http://arxiv.org/abs/2310.10541v1 | http://arxiv.org/pdf/2310.10541v1 | 2310.10541v1 |
Microscaling Data Formats for Deep Learning | Narrow bit-width data formats are key to reducing the computational and
storage costs of modern deep learning applications. This paper evaluates
Microscaling (MX) data formats that combine a per-block scaling factor with
narrow floating-point and integer types for individual elements. MX formats
balance the competing needs of hardware efficiency, model accuracy, and user
friction. Empirical results on over two dozen benchmarks demonstrate
practicality of MX data formats as a drop-in replacement for baseline FP32 for
AI inference and training with low user friction. We also show the first
instance of training generative language models at sub-8-bit weights,
activations, and gradients with minimal accuracy loss and no modifications to
the training recipe. | [
"Bita Darvish Rouhani",
"Ritchie Zhao",
"Ankit More",
"Mathew Hall",
"Alireza Khodamoradi",
"Summer Deng",
"Dhruv Choudhary",
"Marius Cornea",
"Eric Dellinger",
"Kristof Denolf",
"Stosic Dusan",
"Venmugil Elango",
"Maximilian Golub",
"Alexander Heinecke",
"Phil James-Roxby",
"Dharmesh Jani",
"Gaurav Kolhe",
"Martin Langhammer",
"Ada Li",
"Levi Melnick",
"Maral Mesmakhosroshahi",
"Andres Rodriguez",
"Michael Schulte",
"Rasoul Shafipour",
"Lei Shao",
"Michael Siu",
"Pradeep Dubey",
"Paulius Micikevicius",
"Maxim Naumov",
"Colin Verrilli",
"Ralph Wittig",
"Doug Burger",
"Eric Chung"
] | 2023-10-16 16:07:41 | http://arxiv.org/abs/2310.10537v3 | http://arxiv.org/pdf/2310.10537v3 | 2310.10537v3 |
Comparing Comparators in Generalization Bounds | We derive generic information-theoretic and PAC-Bayesian generalization
bounds involving an arbitrary convex comparator function, which measures the
discrepancy between the training and population loss. The bounds hold under the
assumption that the cumulant-generating function (CGF) of the comparator is
upper-bounded by the corresponding CGF within a family of bounding
distributions. We show that the tightest possible bound is obtained with the
comparator being the convex conjugate of the CGF of the bounding distribution,
also known as the Cram\'er function. This conclusion applies more broadly to
generalization bounds with a similar structure. This confirms the
near-optimality of known bounds for bounded and sub-Gaussian losses and leads
to novel bounds under other bounding distributions. | [
"Fredrik Hellström",
"Benjamin Guedj"
] | 2023-10-16 16:00:58 | http://arxiv.org/abs/2310.10534v1 | http://arxiv.org/pdf/2310.10534v1 | 2310.10534v1 |
Learning optimal integration of spatial and temporal information in noisy chemotaxis | We investigate the boundary between chemotaxis driven by spatial estimation
of gradients and chemotaxis driven by temporal estimation. While it is well
known that spatial chemotaxis becomes disadvantageous for small organisms at
high noise levels, it is unclear whether there is a discontinuous switch of
optimal strategies or a continuous transition exists. Here, we employ deep
reinforcement learning to study the possible integration of spatial and
temporal information in an a priori unconstrained manner. We parameterize such
a combined chemotactic policy by a recurrent neural network and evaluate it
using a minimal theoretical model of a chemotactic cell. By comparing with
constrained variants of the policy, we show that it converges to purely
temporal and spatial strategies at small and large cell sizes, respectively. We
find that the transition between the regimes is continuous, with the combined
strategy outperforming in the transition region both the constrained variants
as well as models that explicitly integrate spatial and temporal information.
Finally, by utilizing the attribution method of integrated gradients, we show
that the policy relies on a non-trivial combination of spatially and temporally
derived gradient information in a ratio that varies dynamically during the
chemotactic trajectories. | [
"Albert Alonso",
"Julius B. Kirkegaard"
] | 2023-10-16 15:50:23 | http://arxiv.org/abs/2310.10531v1 | http://arxiv.org/pdf/2310.10531v1 | 2310.10531v1 |
From Spectral Theorem to Statistical Independence with Application to System Identification | High dimensional random dynamical systems are ubiquitous, including -- but
not limited to -- cyber-physical systems, daily return on different stocks of
S&P 1500 and velocity profile of interacting particle systems around
McKeanVlasov limit. Mathematically, underlying phenomenon can be captured via a
stable $n$-dimensional linear transformation `$A$' and additive randomness.
System identification aims at extracting useful information about underlying
dynamical system, given a length $N$ trajectory from it (corresponds to an $n
\times N$ dimensional data matrix). We use spectral theorem for non-Hermitian
operators to show that spatio-temperal correlations are dictated by the
discrepancy between algebraic and geometric multiplicity of distinct
eigenvalues corresponding to state transition matrix. Small discrepancies imply
that original trajectory essentially comprises of multiple lower dimensional
random dynamical systems living on $A$ invariant subspaces and are
statistically independent of each other. In the process, we provide first
quantitative handle on decay rate of finite powers of state transition matrix
$\|A^{k}\|$ . It is shown that when a stable dynamical system has only one
distinct eigenvalue and discrepancy of $n-1$: $\|A\|$ has a dependence on $n$,
resulting dynamics are spatially inseparable and consequently there exist at
least one row with covariates of typical size $\Theta\big(\sqrt{N-n+1}$
$e^{n}\big)$ i.e., even under stability assumption, covariates can suffer from
curse of dimensionality. In the light of these findings we set the stage for
non-asymptotic error analysis in estimation of state transition matrix $A$ via
least squares regression on observed trajectory by showing that element-wise
error is essentially a variant of well-know Littlewood-Offord problem. | [
"Muhammad Abdullah Naeem",
"Amir Khazraei",
"Miroslav Pajic"
] | 2023-10-16 15:40:43 | http://arxiv.org/abs/2310.10523v1 | http://arxiv.org/pdf/2310.10523v1 | 2310.10523v1 |
Reproducing Bayesian Posterior Distributions for Exoplanet Atmospheric Parameter Retrievals with a Machine Learning Surrogate Model | We describe a machine-learning-based surrogate model for reproducing the
Bayesian posterior distributions for exoplanet atmospheric parameters derived
from transmission spectra of transiting planets with typical retrieval software
such as TauRex. The model is trained on ground truth distributions for seven
parameters: the planet radius, the atmospheric temperature, and the mixing
ratios for five common absorbers: $H_2O$, $CH_4$, $NH_3$, $CO$ and $CO_2$. The
model performance is enhanced by domain-inspired preprocessing of the features
and the use of semi-supervised learning in order to leverage the large amount
of unlabelled training data available. The model was among the winning
solutions in the 2023 Ariel Machine Learning Data Challenge. | [
"Eyup B. Unlu",
"Roy T. Forestano",
"Konstantin T. Matchev",
"Katia Matcheva"
] | 2023-10-16 15:39:05 | http://arxiv.org/abs/2310.10521v1 | http://arxiv.org/pdf/2310.10521v1 | 2310.10521v1 |
Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking | Zero-shot Dialogue State Tracking (DST) addresses the challenge of acquiring
and annotating task-oriented dialogues, which can be time consuming and costly.
However, DST extends beyond simple slot-filling and requires effective updating
strategies for tracking dialogue state as conversations progress. In this
paper, we propose ParsingDST, a new In-Context Learning (ICL) method, to
introduce additional intricate updating strategies in zero-shot DST. Our
approach reformulates the DST task by leveraging powerful Large Language Models
(LLMs) and translating the original dialogue text to JSON through semantic
parsing as an intermediate state. We also design a novel framework that
includes more modules to ensure the effectiveness of updating strategies in the
text-to-JSON process. Experimental results demonstrate that our approach
outperforms existing zero-shot DST methods on MultiWOZ, exhibiting significant
improvements in Joint Goal Accuracy (JGA) and slot accuracy compared to
existing ICL methods. | [
"Yuxiang Wu",
"Guanting Dong",
"Weiran Xu"
] | 2023-10-16 15:38:02 | http://arxiv.org/abs/2310.10520v2 | http://arxiv.org/pdf/2310.10520v2 | 2310.10520v2 |
ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models | Alignment is of critical importance for training large language models
(LLMs). The predominant strategy to address this is through Reinforcement
Learning from Human Feedback (RLHF), where PPO serves as the de-facto
algorithm. Yet, PPO is known to suffer from computational inefficiency, which
is a challenge that this paper aims to address. We identify three important
properties in RLHF tasks: fast simulation, deterministic transitions, and
trajectory-level rewards, which are not leveraged in PPO. Based on such
observations, we develop a new algorithm tailored for RLHF, called ReMax. The
algorithm design of ReMax is built on a celebrated algorithm REINFORCE but is
equipped with a new variance-reduction technique.
Our method has three-fold advantages over PPO: first, ReMax is simple to
implement and removes many hyper-parameters in PPO, which are scale-sensitive
and laborious to tune. Second, ReMax saves about 50% memory usage in principle.
As a result, PPO runs out-of-memory when fine-tuning a Llama2 (7B) model on
8xA100-40GB GPUs, whereas ReMax can afford training. This memory improvement is
achieved by removing the value model in PPO. Third, based on our calculations,
we find that even assuming PPO can afford the training of Llama2 (7B), it would
still run about 2x slower than ReMax. This is due to the computational overhead
of the value model, which does not exist in ReMax. Importantly, the above
computational improvements do not sacrifice the performance. We hypothesize
these advantages can be maintained in larger-scaled models. Our implementation
of ReMax is available at https://github.com/liziniu/ReMax | [
"Ziniu Li",
"Tian Xu",
"Yushun Zhang",
"Yang Yu",
"Ruoyu Sun",
"Zhi-Quan Luo"
] | 2023-10-16 15:25:14 | http://arxiv.org/abs/2310.10505v2 | http://arxiv.org/pdf/2310.10505v2 | 2310.10505v2 |
Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies | Forecasting models for systematic trading strategies do not adapt quickly
when financial market conditions change, as was seen in the advent of the
COVID-19 pandemic in 2020, when market conditions changed dramatically causing
many forecasting models to take loss-making positions. To deal with such
situations, we propose a novel time-series trend-following forecaster that is
able to quickly adapt to new market conditions, referred to as regimes. We
leverage recent developments from the deep learning community and use few-shot
learning. We propose the Cross Attentive Time-Series Trend Network - X-Trend -
which takes positions attending over a context set of financial time-series
regimes. X-Trend transfers trends from similar patterns in the context set to
make predictions and take positions for a new distinct target regime. X-Trend
is able to quickly adapt to new financial regimes with a Sharpe ratio increase
of 18.9% over a neural forecaster and 10-fold over a conventional Time-series
Momentum strategy during the turbulent market period from 2018 to 2023. Our
strategy recovers twice as quickly from the COVID-19 drawdown compared to the
neural-forecaster. X-Trend can also take zero-shot positions on novel unseen
financial assets obtaining a 5-fold Sharpe ratio increase versus a neural
time-series trend forecaster over the same period. X-Trend both forecasts
next-day prices and outputs a trading signal. Furthermore, the cross-attention
mechanism allows us to interpret the relationship between forecasts and
patterns in the context set. | [
"Kieran Wood",
"Samuel Kessler",
"Stephen J. Roberts",
"Stefan Zohren"
] | 2023-10-16 15:20:12 | http://arxiv.org/abs/2310.10500v1 | http://arxiv.org/pdf/2310.10500v1 | 2310.10500v1 |
Type-aware Decoding via Explicitly Aggregating Event Information for Document-level Event Extraction | Document-level event extraction (DEE) faces two main challenges:
arguments-scattering and multi-event. Although previous methods attempt to
address these challenges, they overlook the interference of event-unrelated
sentences during event detection and neglect the mutual interference of
different event roles during argument extraction. Therefore, this paper
proposes a novel Schema-based Explicitly Aggregating~(SEA) model to address
these limitations. SEA aggregates event information into event type and role
representations, enabling the decoding of event records based on specific
type-aware representations. By detecting each event based on its event type
representation, SEA mitigates the interference caused by event-unrelated
information. Furthermore, SEA extracts arguments for each role based on its
role-aware representations, reducing mutual interference between different
roles. Experimental results on the ChFinAnn and DuEE-fin datasets show that SEA
outperforms the SOTA methods. | [
"Gang Zhao",
"Yidong Shi",
"Shudong Lu",
"Xinjie Yang",
"Guanting Dong",
"Jian Xu",
"Xiaocheng Gong",
"Si Li"
] | 2023-10-16 15:10:42 | http://arxiv.org/abs/2310.10487v1 | http://arxiv.org/pdf/2310.10487v1 | 2310.10487v1 |
ManyQuadrupeds: Learning a Single Locomotion Policy for Diverse Quadruped Robots | Learning a locomotion policy for quadruped robots has traditionally been
constrained to specific robot morphology, mass, and size. The learning process
must usually be repeated for every new robot, where hyperparameters and reward
function weights must be re-tuned to maximize performance for each new system.
Alternatively, attempting to train a single policy to accommodate different
robot sizes, while maintaining the same degrees of freedom (DoF) and
morphology, requires either complex learning frameworks, or mass, inertia, and
dimension randomization, which leads to prolonged training periods. In our
study, we show that drawing inspiration from animal motor control allows us to
effectively train a single locomotion policy capable of controlling a diverse
range of quadruped robots. These differences encompass a variable number of
DoFs, (i.e. 12 or 16 joints), three distinct morphologies, a broad mass range
spanning from 2 kg to 200 kg, and nominal standing heights ranging from 16 cm
to 100 cm. Our policy modulates a representation of the Central Pattern
Generator (CPG) in the spinal cord, effectively coordinating both frequencies
and amplitudes of the CPG to produce rhythmic output (Rhythm Generation), which
is then mapped to a Pattern Formation (PF) layer. Across different robots, the
only varying component is the PF layer, which adjusts the scaling parameters
for the stride height and length. Subsequently, we evaluate the sim-to-real
transfer by testing the single policy on both the Unitree Go1 and A1 robots.
Remarkably, we observe robust performance, even when adding a 15 kg load,
equivalent to 125% of the A1 robot's nominal mass. | [
"Milad Shafiee",
"Guillaume Bellegarda",
"Auke Ijspeert"
] | 2023-10-16 15:06:16 | http://arxiv.org/abs/2310.10486v1 | http://arxiv.org/pdf/2310.10486v1 | 2310.10486v1 |
Passive Inference Attacks on Split Learning via Adversarial Regularization | Split Learning (SL) has emerged as a practical and efficient alternative to
traditional federated learning. While previous attempts to attack SL have often
relied on overly strong assumptions or targeted easily exploitable models, we
seek to develop more practical attacks. We introduce SDAR, a novel attack
framework against SL with an honest-but-curious server. SDAR leverages
auxiliary data and adversarial regularization to learn a decodable simulator of
the client's private model, which can effectively infer the client's private
features under the vanilla SL, and both features and labels under the U-shaped
SL. We perform extensive experiments in both configurations to validate the
effectiveness of our proposed attacks. Notably, in challenging but practical
scenarios where existing passive attacks struggle to reconstruct the client's
private data effectively, SDAR consistently achieves attack performance
comparable to active attacks. On CIFAR-10, at the deep split level of 7, SDAR
achieves private feature reconstruction with less than 0.025 mean squared error
in both the vanilla and the U-shaped SL, and attains a label inference accuracy
of over 98% in the U-shaped setting, while existing attacks fail to produce
non-trivial results. | [
"Xiaochen Zhu",
"Xinjian Luo",
"Yuncheng Wu",
"Yangfan Jiang",
"Xiaokui Xiao",
"Beng Chin Ooi"
] | 2023-10-16 15:03:55 | http://arxiv.org/abs/2310.10483v1 | http://arxiv.org/pdf/2310.10483v1 | 2310.10483v1 |
DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource Event Extraction | Most current Event Extraction (EE) methods focus on the high-resource
scenario, which requires a large amount of annotated data and can hardly be
applied to low-resource domains. To address EE more effectively with limited
resources, we propose the Demonstration-enhanced Schema-guided Generation
(DemoSG) model, which benefits low-resource EE from two aspects: Firstly, we
propose the demonstration-based learning paradigm for EE to fully use the
annotated data, which transforms them into demonstrations to illustrate the
extraction process and help the model learn effectively. Secondly, we formulate
EE as a natural language generation task guided by schema-based prompts,
thereby leveraging label semantics and promoting knowledge transfer in
low-resource scenarios. We conduct extensive experiments under in-domain and
domain adaptation low-resource settings on three datasets, and study the
robustness of DemoSG. The results show that DemoSG significantly outperforms
current methods in low-resource scenarios. | [
"Gang Zhao",
"Xiaocheng Gong",
"Xinjie Yang",
"Guanting Dong",
"Shudong Lu",
"Si Li"
] | 2023-10-16 15:02:37 | http://arxiv.org/abs/2310.10481v1 | http://arxiv.org/pdf/2310.10481v1 | 2310.10481v1 |
Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysis | The rapid advancement of large language models (LLMs) presents both
opportunities and challenges, particularly concerning unintentional generation
of harmful and toxic responses. While the traditional alignment methods strive
to steer LLMs towards desired performance and shield them from malicious
content, this study proposes a novel alignment strategy rooted in mistake
analysis by exposing LLMs to flawed outputs purposefully and then conducting a
thorough assessment to fully comprehend internal reasons via natural language
analysis. Thus, toxic responses can be transformed into instruction tuning
corpus for model alignment, and LLMs can not only be deterred from generating
flawed responses but also trained to self-criticize, leveraging its innate
ability to discriminate toxic content. Experimental results demonstrate that
the proposed method outperforms conventional alignment techniques for safety
instruction following, while maintaining superior efficiency. | [
"Kai Chen",
"Chunwei Wang",
"Kuo Yang",
"Jianhua Han",
"Lanqing Hong",
"Fei Mi",
"Hang Xu",
"Zhengying Liu",
"Wenyong Huang",
"Zhenguo Li",
"Dit-Yan Yeung",
"Lifeng Shang",
"Xin Jiang",
"Qun Liu"
] | 2023-10-16 14:59:10 | http://arxiv.org/abs/2310.10477v2 | http://arxiv.org/pdf/2310.10477v2 | 2310.10477v2 |
Machine Learning Techniques for Identifying the Defective Patterns in Semiconductor Wafer Maps: A Survey, Empirical, and Experimental Evaluations | This survey paper offers a comprehensive review of methodologies utilizing
machine learning (ML) techniques for identifying wafer defects in semiconductor
manufacturing. Despite the growing body of research demonstrating the
effectiveness of ML in wafer defect identification, there is a noticeable
absence of comprehensive reviews on this subject. This survey attempts to fill
this void by amalgamating available literature and providing an in-depth
analysis of the advantages, limitations, and potential applications of various
ML algorithms in the realm of wafer defect detection. An innovative taxonomy of
methodologies that we present provides a detailed classification of algorithms
into more refined categories and techniques. This taxonomy follows a four-tier
structure, starting from broad methodology categories and ending with specific
sub-techniques. It aids researchers in comprehending the complex relationships
between different algorithms and their techniques. We employ a rigorous
empirical and experimental evaluation to rank these varying techniques. For the
empirical evaluation, we assess techniques based on a set of four criteria. The
experimental evaluation ranks the algorithms employing the same sub-techniques,
techniques, sub-categories, and categories. This integration of a multi-layered
taxonomy, empirical evaluations, and comparative experiments provides a
detailed and holistic understanding of ML techniques and algorithms for
identifying wafer defects. This approach guides researchers towards making more
informed decisions in their work. Additionally, the paper illuminates the
future prospects of ML techniques for wafer defect identification, underscoring
potential advancements and opportunities for further research in this field | [
"Kamal Taha"
] | 2023-10-16 14:46:45 | http://arxiv.org/abs/2310.10705v1 | http://arxiv.org/pdf/2310.10705v1 | 2310.10705v1 |
Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems | Cascade ranking is widely used for large-scale top-k selection problems in
online advertising and recommendation systems, and learning-to-rank is an
important way to optimize the models in cascade ranking systems. Previous works
on learning-to-rank usually focus on letting the model learn the complete order
or pay more attention to the order of top materials, and adopt the
corresponding rank metrics as optimization targets. However, these optimization
targets can not adapt to various cascade ranking scenarios with varying data
complexities and model capabilities; and the existing metric-driven methods
such as the Lambda framework can only optimize a rough upper bound of the
metric, potentially resulting in performance misalignment. To address these
issues, we first propose a novel perspective on optimizing cascade ranking
systems by highlighting the adaptability of optimization targets to data
complexities and model capabilities. Concretely, we employ multi-task learning
framework to adaptively combine the optimization of relaxed and full targets,
which refers to metrics Recall@m@k and OAP respectively. Then we introduce a
permutation matrix to represent the rank metrics and employ differentiable
sorting techniques to obtain a relaxed permutation matrix with controllable
approximate error bound. This enables us to optimize both the relaxed and full
targets directly and more appropriately using the proposed surrogate losses
within the deep learning framework. We named this method as Adaptive Neural
Ranking Framework. We use the NeuralSort method to obtain the relaxed
permutation matrix and draw on the uncertainty weight method in multi-task
learning to optimize the proposed losses jointly. Experiments on a total of 4
public and industrial benchmarks show the effectiveness and generalization of
our method, and online experiment shows that our method has significant
application value. | [
"Yunli Wang",
"Zhiqiang Wang",
"Jian Yang",
"Shiyang Wen",
"Dongying Kong",
"Han Li",
"Kun Gai"
] | 2023-10-16 14:43:02 | http://arxiv.org/abs/2310.10462v1 | http://arxiv.org/pdf/2310.10462v1 | 2310.10462v1 |
Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data | Anomaly detection requires detecting abnormal samples in large unlabeled
datasets. While progress in deep learning and the advent of foundation models
has produced powerful unsupervised anomaly detection methods, their deployment
in practice is often hindered by the lack of labeled data -- without it, the
detection accuracy of an anomaly detector cannot be evaluated reliably. In this
work, we propose a general-purpose framework for evaluating image-based anomaly
detectors with synthetically generated validation data. Our method assumes
access to a small support set of normal images which are processed with a
pre-trained diffusion model (our proposed method requires no training or
fine-tuning) to produce synthetic anomalies. When mixed with normal samples
from the support set, the synthetic anomalies create detection tasks that
compose a validation framework for anomaly detection evaluation and model
selection. In an extensive empirical study, ranging from natural images to
industrial applications, we find that our synthetic validation framework
selects the same models and hyper-parameters as selection with a ground-truth
validation set. In addition, we find that prompts selected by our method for
CLIP-based anomaly detection outperforms all other prompt selection strategies,
and leads to the overall best detection accuracy, even on the challenging
MVTec-AD dataset. | [
"Clement Fung",
"Chen Qiu",
"Aodong Li",
"Maja Rudolph"
] | 2023-10-16 14:42:22 | http://arxiv.org/abs/2310.10461v1 | http://arxiv.org/pdf/2310.10461v1 | 2310.10461v1 |
Text Summarization Using Large Language Models: A Comparative Study of MPT-7b-instruct, Falcon-7b-instruct, and OpenAI Chat-GPT Models | Text summarization is a critical Natural Language Processing (NLP) task with
applications ranging from information retrieval to content generation.
Leveraging Large Language Models (LLMs) has shown remarkable promise in
enhancing summarization techniques. This paper embarks on an exploration of
text summarization with a diverse set of LLMs, including MPT-7b-instruct,
falcon-7b-instruct, and OpenAI ChatGPT text-davinci-003 models. The experiment
was performed with different hyperparameters and evaluated the generated
summaries using widely accepted metrics such as the Bilingual Evaluation
Understudy (BLEU) Score, Recall-Oriented Understudy for Gisting Evaluation
(ROUGE) Score, and Bidirectional Encoder Representations from Transformers
(BERT) Score. According to the experiment, text-davinci-003 outperformed the
others. This investigation involved two distinct datasets: CNN Daily Mail and
XSum. Its primary objective was to provide a comprehensive understanding of the
performance of Large Language Models (LLMs) when applied to different datasets.
The assessment of these models' effectiveness contributes valuable insights to
researchers and practitioners within the NLP domain. This work serves as a
resource for those interested in harnessing the potential of LLMs for text
summarization and lays the foundation for the development of advanced
Generative AI applications aimed at addressing a wide spectrum of business
challenges. | [
"Lochan Basyal",
"Mihir Sanghvi"
] | 2023-10-16 14:33:02 | http://arxiv.org/abs/2310.10449v2 | http://arxiv.org/pdf/2310.10449v2 | 2310.10449v2 |
A Geometric Insight into Equivariant Message Passing Neural Networks on Riemannian Manifolds | This work proposes a geometric insight into equivariant message passing on
Riemannian manifolds. As previously proposed, numerical features on Riemannian
manifolds are represented as coordinate-independent feature fields on the
manifold. To any coordinate-independent feature field on a manifold comes
attached an equivariant embedding of the principal bundle to the space of
numerical features. We argue that the metric this embedding induces on the
numerical feature space should optimally preserve the principal bundle's
original metric. This optimality criterion leads to the minimization of a
twisted form of the Polyakov action with respect to the graph of this
embedding, yielding an equivariant diffusion process on the associated vector
bundle. We obtain a message passing scheme on the manifold by discretizing the
diffusion equation flow for a fixed time step. We propose a higher-order
equivariant diffusion process equivalent to diffusion on the cartesian product
of the base manifold. The discretization of the higher-order diffusion process
on a graph yields a new general class of equivariant GNN, generalizing the ACE
and MACE formalism to data on Riemannian manifolds. | [
"Ilyes Batatia"
] | 2023-10-16 14:31:13 | http://arxiv.org/abs/2310.10448v1 | http://arxiv.org/pdf/2310.10448v1 | 2310.10448v1 |
Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification | Sigmoid output layers are widely used in multi-label classification (MLC)
tasks, in which multiple labels can be assigned to any input. In many practical
MLC tasks, the number of possible labels is in the thousands, often exceeding
the number of input features and resulting in a low-rank output layer. In
multi-class classification, it is known that such a low-rank output layer is a
bottleneck that can result in unargmaxable classes: classes which cannot be
predicted for any input. In this paper, we show that for MLC tasks, the
analogous sigmoid bottleneck results in exponentially many unargmaxable label
combinations. We explain how to detect these unargmaxable outputs and
demonstrate their presence in three widely used MLC datasets. We then show that
they can be prevented in practice by introducing a Discrete Fourier Transform
(DFT) output layer, which guarantees that all sparse label combinations with up
to $k$ active labels are argmaxable. Our DFT layer trains faster and is more
parameter efficient, matching the F1@k score of a sigmoid layer while using up
to 50% fewer trainable parameters. Our code is publicly available at
https://github.com/andreasgrv/sigmoid-bottleneck. | [
"Andreas Grivas",
"Antonio Vergari",
"Adam Lopez"
] | 2023-10-16 14:25:50 | http://arxiv.org/abs/2310.10443v1 | http://arxiv.org/pdf/2310.10443v1 | 2310.10443v1 |
Equivariant Matrix Function Neural Networks | Graph Neural Networks (GNNs), especially message-passing neural networks
(MPNNs), have emerged as powerful architectures for learning on graphs in
diverse applications. However, MPNNs face challenges when modeling non-local
interactions in systems such as large conjugated molecules, metals, or
amorphous materials. Although Spectral GNNs and traditional neural networks
such as recurrent neural networks and transformers mitigate these challenges,
they often lack extensivity, adaptability, generalizability, computational
efficiency, or fail to capture detailed structural relationships or symmetries
in the data. To address these concerns, we introduce Matrix Function Neural
Networks (MFNs), a novel architecture that parameterizes non-local interactions
through analytic matrix equivariant functions. Employing resolvent expansions
offers a straightforward implementation and the potential for linear scaling
with system size. The MFN architecture achieves state-of-the-art performance in
standard graph benchmarks, such as the ZINC and TU datasets, and is able to
capture intricate non-local interactions in quantum systems, paving the way to
new state-of-the-art force fields. | [
"Ilyes Batatia",
"Lars L. Schaaf",
"Huajie Chen",
"Gábor Csányi",
"Christoph Ortner",
"Felix A. Faber"
] | 2023-10-16 14:17:00 | http://arxiv.org/abs/2310.10434v1 | http://arxiv.org/pdf/2310.10434v1 | 2310.10434v1 |
Object Detection in Aerial Images in Scarce Data Regimes | Most contributions on Few-Shot Object Detection (FSOD) evaluate their methods
on natural images only, yet the transferability of the announced performance is
not guaranteed for applications on other kinds of images. We demonstrate this
with an in-depth analysis of existing FSOD methods on aerial images and
observed a large performance gap compared to natural images. Small objects,
more numerous in aerial images, are the cause for the apparent performance gap
between natural and aerial images. As a consequence, we improve FSOD
performance on small objects with a carefully designed attention mechanism. In
addition, we also propose a scale-adaptive box similarity criterion, that
improves the training and evaluation of FSOD methods, particularly for small
objects. We also contribute to generic FSOD with two distinct approaches based
on metric learning and fine-tuning. Impressive results are achieved with the
fine-tuning method, which encourages tackling more complex scenarios such as
Cross-Domain FSOD. We conduct preliminary experiments in this direction and
obtain promising results. Finally, we address the deployment of the detection
models inside COSE's systems. Detection must be done in real-time in extremely
large images (more than 100 megapixels), with limited computation power.
Leveraging existing optimization tools such as TensorRT, we successfully tackle
this engineering challenge. | [
"Pierre Le Jeune"
] | 2023-10-16 14:16:47 | http://arxiv.org/abs/2310.10433v1 | http://arxiv.org/pdf/2310.10433v1 | 2310.10433v1 |
Continuously Adapting Random Sampling (CARS) for Power Electronics Parameter Design | To date, power electronics parameter design tasks are usually tackled using
detailed optimization approaches with detailed simulations or using brute force
grid search grid search with very fast simulations. A new method, named
"Continuously Adapting Random Sampling" (CARS) is proposed, which provides a
continuous method in between. This allows for very fast, and / or large amounts
of simulations, but increasingly focuses on the most promising parameter
ranges. Inspirations are drawn from multi-armed bandit research and lead to
prioritized sampling of sub-domains in one high-dimensional parameter tensor.
Performance has been evaluated on three exemplary power electronic use-cases,
where resulting designs appear competitive to genetic algorithms, but
additionally allow for highly parallelizable simulation, as well as continuous
progression between explorative and exploitative settings. | [
"Dominik Happel",
"Philipp Brendel",
"Andreas Rosskopf",
"Stefan Ditze"
] | 2023-10-16 14:09:59 | http://arxiv.org/abs/2310.10425v1 | http://arxiv.org/pdf/2310.10425v1 | 2310.10425v1 |
LMT: Longitudinal Mixing Training, a Framework to Predict Disease Progression from a Single Image | Longitudinal imaging is able to capture both static anatomical structures and
dynamic changes in disease progression toward earlier and better
patient-specific pathology management. However, conventional approaches rarely
take advantage of longitudinal information for detection and prediction
purposes, especially for Diabetic Retinopathy (DR). In the past years, Mix-up
training and pretext tasks with longitudinal context have effectively enhanced
DR classification results and captured disease progression. In the meantime, a
novel type of neural network named Neural Ordinary Differential Equation (NODE)
has been proposed for solving ordinary differential equations, with a neural
network treated as a black box. By definition, NODE is well suited for solving
time-related problems. In this paper, we propose to combine these three aspects
to detect and predict DR progression. Our framework, Longitudinal Mixing
Training (LMT), can be considered both as a regularizer and as a pretext task
that encodes the disease progression in the latent space. Additionally, we
evaluate the trained model weights on a downstream task with a longitudinal
context using standard and longitudinal pretext tasks. We introduce a new way
to train time-aware models using $t_{mix}$, a weighted average time between two
consecutive examinations. We compare our approach to standard mixing training
on DR classification using OPHDIAT a longitudinal retinal Color Fundus
Photographs (CFP) dataset. We were able to predict whether an eye would develop
a severe DR in the following visit using a single image, with an AUC of 0.798
compared to baseline results of 0.641. Our results indicate that our
longitudinal pretext task can learn the progression of DR disease and that
introducing $t_{mix}$ augmentation is beneficial for time-aware models. | [
"Rachid Zeghlache",
"Pierre-Henri Conze",
"Mostafa El Habib Daho",
"Yihao Li",
"Hugo Le boite",
"Ramin Tadayoni",
"Pascal Massin",
"Béatrice Cochener",
"Ikram Brahim",
"Gwenolé Quellec",
"Mathieu Lamard"
] | 2023-10-16 14:01:20 | http://arxiv.org/abs/2310.10420v1 | http://arxiv.org/pdf/2310.10420v1 | 2310.10420v1 |
Reading Books is Great, But Not if You Are Driving! Visually Grounded Reasoning about Defeasible Commonsense Norms | Commonsense norms are defeasible by context: reading books is usually great,
but not when driving a car. While contexts can be explicitly described in
language, in embodied scenarios, contexts are often provided visually. This
type of visually grounded reasoning about defeasible commonsense norms is
generally easy for humans, but (as we show) poses a challenge for machines, as
it necessitates both visual understanding and reasoning about commonsense
norms. We construct a new multimodal benchmark for studying visual-grounded
commonsense norms: NORMLENS. NORMLENS consists of 10K human judgments
accompanied by free-form explanations covering 2K multimodal situations, and
serves as a probe to address two questions: (1) to what extent can models align
with average human judgment? and (2) how well can models explain their
predicted judgments? We find that state-of-the-art model judgments and
explanations are not well-aligned with human annotation. Additionally, we
present a new approach to better align models with humans by distilling social
commonsense knowledge from large language models. The data and code are
released at https://seungjuhan.me/normlens. | [
"Seungju Han",
"Junhyeok Kim",
"Jack Hessel",
"Liwei Jiang",
"Jiwan Chung",
"Yejin Son",
"Yejin Choi",
"Youngjae Yu"
] | 2023-10-16 14:00:07 | http://arxiv.org/abs/2310.10418v1 | http://arxiv.org/pdf/2310.10418v1 | 2310.10418v1 |
Prior-Free Continual Learning with Unlabeled Data in the Wild | Continual Learning (CL) aims to incrementally update a trained model on new
tasks without forgetting the acquired knowledge of old ones. Existing CL
methods usually reduce forgetting with task priors, \ie using task identity or
a subset of previously seen samples for model training. However, these methods
would be infeasible when such priors are unknown in real-world applications. To
address this fundamental but seldom-studied problem, we propose a Prior-Free
Continual Learning (PFCL) method, which learns new tasks without knowing the
task identity or any previous data. First, based on a fixed single-head
architecture, we eliminate the need for task identity to select the
task-specific output head. Second, we employ a regularization-based strategy
for consistent predictions between the new and old models, avoiding revisiting
previous samples. However, using this strategy alone often performs poorly in
class-incremental scenarios, particularly for a long sequence of tasks. By
analyzing the effectiveness and limitations of conventional
regularization-based methods, we propose enhancing model consistency with an
auxiliary unlabeled dataset additionally. Moreover, since some auxiliary data
may degrade the performance, we further develop a reliable sample selection
strategy to obtain consistent performance improvement. Extensive experiments on
multiple image classification benchmark datasets show that our PFCL method
significantly mitigates forgetting in all three learning scenarios.
Furthermore, when compared to the most recent rehearsal-based methods that
replay a limited number of previous samples, PFCL achieves competitive
accuracy. Our code is available at: https://github.com/visiontao/pfcl | [
"Tao Zhuo",
"Zhiyong Cheng",
"Hehe Fan",
"Mohan Kankanhalli"
] | 2023-10-16 13:59:56 | http://arxiv.org/abs/2310.10417v1 | http://arxiv.org/pdf/2310.10417v1 | 2310.10417v1 |
A cross Transformer for image denoising | Deep convolutional neural networks (CNNs) depend on feedforward and feedback
ways to obtain good performance in image denoising. However, how to obtain
effective structural information via CNNs to efficiently represent given noisy
images is key for complex scenes. In this paper, we propose a cross Transformer
denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a
residual block (RB) to obtain clean images for complex scenes. A SB uses an
enhanced residual architecture to deeply search structural information for
image denoising. To avoid loss of key information, PB uses three heterogeneous
networks to implement multiple interactions of multi-level features to broadly
search for extra information for improving the adaptability of an obtained
denoiser for complex scenes. Also, to improve denoising performance,
Transformer mechanisms are embedded into the SB and PB to extract complementary
salient features for effectively removing noise in terms of pixel relations.
Finally, a RB is applied to acquire clean images. Experiments illustrate that
our CTNet is superior to some popular denoising methods in terms of real and
synthetic image denoising. It is suitable to mobile digital devices, i.e.,
phones. Codes can be obtained at https://github.com/hellloxiaotian/CTNet. | [
"Chunwei Tian",
"Menghua Zheng",
"Wangmeng Zuo",
"Shichao Zhang",
"Yanning Zhang",
"Chia-Wen Ling"
] | 2023-10-16 13:53:19 | http://arxiv.org/abs/2310.10408v1 | http://arxiv.org/pdf/2310.10408v1 | 2310.10408v1 |
Real-Fake: Effective Training Data Synthesis Through Distribution Matching | Synthetic training data has gained prominence in numerous learning tasks and
scenarios, offering advantages such as dataset augmentation, generalization
evaluation, and privacy preservation. Despite these benefits, the efficiency of
synthetic data generated by current methodologies remains inferior when
training advanced deep models exclusively, limiting its practical utility. To
address this challenge, we analyze the principles underlying training data
synthesis for supervised learning and elucidate a principled theoretical
framework from the distribution-matching perspective that explicates the
mechanisms governing synthesis efficacy. Through extensive experiments, we
demonstrate the effectiveness of our synthetic data across diverse image
classification tasks, both as a replacement for and augmentation to real
datasets, while also benefits challenging tasks such as out-of-distribution
generalization and privacy preservation. | [
"Jianhao Yuan",
"Jie Zhang",
"Shuyang Sun",
"Philip Torr",
"Bo Zhao"
] | 2023-10-16 13:45:26 | http://arxiv.org/abs/2310.10402v1 | http://arxiv.org/pdf/2310.10402v1 | 2310.10402v1 |
Can Word Sense Distribution Detect Semantic Changes of Words? | Semantic Change Detection (SCD) of words is an important task for various NLP
applications that must make time-sensitive predictions. Some words are used
over time in novel ways to express new meanings, and these new meanings
establish themselves as novel senses of existing words. On the other hand, Word
Sense Disambiguation (WSD) methods associate ambiguous words with sense ids,
depending on the context in which they occur. Given this relationship between
WSD and SCD, we explore the possibility of predicting whether a target word has
its meaning changed between two corpora collected at different time steps, by
comparing the distributions of senses of that word in each corpora. For this
purpose, we use pretrained static sense embeddings to automatically annotate
each occurrence of the target word in a corpus with a sense id. Next, we
compute the distribution of sense ids of a target word in a given corpus.
Finally, we use different divergence or distance measures to quantify the
semantic change of the target word across the two given corpora. Our
experimental results on SemEval 2020 Task 1 dataset show that word sense
distributions can be accurately used to predict semantic changes of words in
English, German, Swedish and Latin. | [
"Xiaohang Tang",
"Yi Zhou",
"Taichi Aida",
"Procheta Sen",
"Danushka Bollegala"
] | 2023-10-16 13:41:27 | http://arxiv.org/abs/2310.10400v1 | http://arxiv.org/pdf/2310.10400v1 | 2310.10400v1 |
Towards Fair and Calibrated Models | Recent literature has seen a significant focus on building machine learning
models with specific properties such as fairness, i.e., being non-biased with
respect to a given set of attributes, calibration i.e., model confidence being
aligned with its predictive accuracy, and explainability, i.e., ability to be
understandable to humans. While there has been work focusing on each of these
aspects individually, researchers have shied away from simultaneously
addressing more than one of these dimensions. In this work, we address the
problem of building models which are both fair and calibrated. We work with a
specific definition of fairness, which closely matches [Biswas et. al. 2019],
and has the nice property that Bayes optimal classifier has the maximum
possible fairness under our definition. We show that an existing negative
result towards achieving a fair and calibrated model [Kleinberg et. al. 2017]
does not hold for our definition of fairness. Further, we show that ensuring
group-wise calibration with respect to the sensitive attributes automatically
results in a fair model under our definition. Using this result, we provide a
first cut approach for achieving fair and calibrated models, via a simple
post-processing technique based on temperature scaling. We then propose
modifications of existing calibration losses to perform group-wise calibration,
as a way of achieving fair and calibrated models in a variety of settings.
Finally, we perform extensive experimentation of these techniques on a diverse
benchmark of datasets, and present insights on the pareto-optimality of the
resulting solutions. | [
"Anand Brahmbhatt",
"Vipul Rathore",
"Mausam",
"Parag Singla"
] | 2023-10-16 13:41:09 | http://arxiv.org/abs/2310.10399v1 | http://arxiv.org/pdf/2310.10399v1 | 2310.10399v1 |
Towards Open World Active Learning for 3D Object Detection | Significant strides have been made in closed world 3D object detection,
testing systems in environments with known classes. However, the challenge
arises in open world scenarios where new object classes appear. Existing
efforts sequentially learn novel classes from streams of labeled data at a
significant annotation cost, impeding efficient deployment to the wild. To seek
effective solutions, we investigate a more practical yet challenging research
task: Open World Active Learning for 3D Object Detection (OWAL-3D), aiming at
selecting a small number of 3D boxes to annotate while maximizing detection
performance on both known and unknown classes. The core difficulty centers on
striking a balance between mining more unknown instances and minimizing the
labeling expenses of point clouds. Empirically, our study finds the harmonious
and inverse relationship between box quantities and their confidences can help
alleviate the dilemma, avoiding the repeated selection of common known
instances and focusing on uncertain objects that are potentially unknown. We
unify both relational constraints into a simple and effective AL strategy
namely OpenCRB, which guides to acquisition of informative point clouds with
the least amount of boxes to label. Furthermore, we develop a comprehensive
codebase for easy reproducing and future research, supporting 15 baseline
methods (i.e., active learning, out-of-distribution detection and open world
detection), 2 types of modern 3D detectors (i.e., one-stage SECOND and
two-stage PV-RCNN) and 3 benchmark 3D datasets (i.e., KITTI, nuScenes and
Waymo). Extensive experiments evidence that the proposed Open-CRB demonstrates
superiority and flexibility in recognizing both novel and shared categories
with very limited labeling costs, compared to state-of-the-art baselines. | [
"Zhuoxiao Chen",
"Yadan Luo",
"Zixin Wang",
"Zijian Wang",
"Xin Yu",
"Zi Huang"
] | 2023-10-16 13:32:53 | http://arxiv.org/abs/2310.10391v1 | http://arxiv.org/pdf/2310.10391v1 | 2310.10391v1 |
Towards a Better Understanding of Variations in Zero-Shot Neural Machine Translation Performance | Multilingual Neural Machine Translation (MNMT) facilitates knowledge sharing
but often suffers from poor zero-shot (ZS) translation qualities. While prior
work has explored the causes of overall low ZS performance, our work introduces
a fresh perspective: the presence of high variations in ZS performance. This
suggests that MNMT does not uniformly exhibit poor ZS capability; instead,
certain translation directions yield reasonable results. Through systematic
experimentation involving 1,560 language directions spanning 40 languages, we
identify three key factors contributing to high variations in ZS NMT
performance: 1) target side translation capability 2) vocabulary overlap 3)
linguistic properties. Our findings highlight that the target side translation
quality is the most influential factor, with vocabulary overlap consistently
impacting ZS performance. Additionally, linguistic properties, such as language
family and writing system, play a role, particularly with smaller models.
Furthermore, we suggest that the off-target issue is a symptom of inadequate ZS
performance, emphasizing that zero-shot translation challenges extend beyond
addressing the off-target problem. We release the data and models serving as a
benchmark to study zero-shot for future research at
https://github.com/Smu-Tan/ZS-NMT-Variations | [
"Shaomu Tan",
"Christof Monz"
] | 2023-10-16 13:26:05 | http://arxiv.org/abs/2310.10385v1 | http://arxiv.org/pdf/2310.10385v1 | 2310.10385v1 |
Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification | Meta-learning has demonstrated promising results in few-shot classification
(FSC) by learning to solve new problems using prior knowledge. Bayesian methods
are effective at characterizing uncertainty in FSC, which is crucial in
high-risk fields. In this context, the logistic-softmax likelihood is often
employed as an alternative to the softmax likelihood in multi-class Gaussian
process classification due to its conditional conjugacy property. However, the
theoretical property of logistic-softmax is not clear and previous research
indicated that the inherent uncertainty of logistic-softmax leads to suboptimal
performance. To mitigate these issues, we revisit and redesign the
logistic-softmax likelihood, which enables control of the \textit{a priori}
confidence level through a temperature parameter. Furthermore, we theoretically
and empirically show that softmax can be viewed as a special case of
logistic-softmax and logistic-softmax induces a larger family of data
distribution than softmax. Utilizing modified logistic-softmax, we integrate
the data augmentation technique into the deep kernel based Gaussian process
meta-learning framework, and derive an analytical mean-field approximation for
task-specific updates. Our approach yields well-calibrated uncertainty
estimates and achieves comparable or superior results on standard benchmark
datasets. Code is publicly available at
\url{https://github.com/keanson/revisit-logistic-softmax}. | [
"Tianjun Ke",
"Haoqun Cao",
"Zenan Ling",
"Feng Zhou"
] | 2023-10-16 13:20:13 | http://arxiv.org/abs/2310.10379v1 | http://arxiv.org/pdf/2310.10379v1 | 2310.10379v1 |
Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models | Multilingual large-scale Pretrained Language Models (PLMs) have been shown to
store considerable amounts of factual knowledge, but large variations are
observed across languages. With the ultimate goal of ensuring that users with
different language backgrounds obtain consistent feedback from the same model,
we study the cross-lingual consistency (CLC) of factual knowledge in various
multilingual PLMs. To this end, we propose a Ranking-based Consistency (RankC)
metric to evaluate knowledge consistency across languages independently from
accuracy. Using this metric, we conduct an in-depth analysis of the determining
factors for CLC, both at model level and at language-pair level. Among other
results, we find that increasing model size leads to higher factual probing
accuracy in most languages, but does not improve cross-lingual consistency.
Finally, we conduct a case study on CLC when new factual associations are
inserted in the PLMs via model editing. Results on a small sample of facts
inserted in English reveal a clear pattern whereby the new piece of knowledge
transfers only to languages with which English has a high RankC score. | [
"Jirui Qi",
"Raquel Fernández",
"Arianna Bisazza"
] | 2023-10-16 13:19:17 | http://arxiv.org/abs/2310.10378v3 | http://arxiv.org/pdf/2310.10378v3 | 2310.10378v3 |
GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers | As transformers are equivariant to the permutation of input tokens, encoding
the positional information of tokens is necessary for many tasks. However,
since existing positional encoding schemes have been initially designed for NLP
tasks, their suitability for vision tasks, which typically exhibit different
structural properties in their data, is questionable. We argue that existing
positional encoding schemes are suboptimal for 3D vision tasks, as they do not
respect their underlying 3D geometric structure. Based on this hypothesis, we
propose a geometry-aware attention mechanism that encodes the geometric
structure of tokens as relative transformation determined by the geometric
relationship between queries and key-value pairs. By evaluating on multiple
novel view synthesis (NVS) datasets in the sparse wide-baseline multi-view
setting, we show that our attention, called Geometric Transform Attention
(GTA), improves learning efficiency and performance of state-of-the-art
transformer-based NVS models without any additional learned parameters and only
minor computational overhead. | [
"Takeru Miyato",
"Bernhard Jaeger",
"Max Welling",
"Andreas Geiger"
] | 2023-10-16 13:16:09 | http://arxiv.org/abs/2310.10375v1 | http://arxiv.org/pdf/2310.10375v1 | 2310.10375v1 |
Multi-Factor Spatio-Temporal Prediction based on Graph Decomposition Learning | Spatio-temporal (ST) prediction is an important and widely used technique in
data mining and analytics, especially for ST data in urban systems such as
transportation data. In practice, the ST data generation is usually influenced
by various latent factors tied to natural phenomena or human socioeconomic
activities, impacting specific spatial areas selectively. However, existing ST
prediction methods usually do not refine the impacts of different factors, but
directly model the entangled impacts of multiple factors. This amplifies the
modeling complexity of ST data and compromises model interpretability. To this
end, we propose a multi-factor ST prediction task that predicts partial ST data
evolution under different factors, and combines them for a final prediction. We
make two contributions to this task: an effective theoretical solution and a
portable instantiation framework. Specifically, we first propose a theoretical
solution called decomposed prediction strategy and prove its effectiveness from
the perspective of information entropy theory. On top of that, we instantiate a
novel model-agnostic framework, named spatio-temporal graph decomposition
learning (STGDL), for multi-factor ST prediction. The framework consists of two
main components: an automatic graph decomposition module that decomposes the
original graph structure inherent in ST data into subgraphs corresponding to
different factors, and a decomposed learning network that learns the partial ST
data on each subgraph separately and integrates them for the final prediction.
We conduct extensive experiments on four real-world ST datasets of two types of
graphs, i.e., grid graph and network graph. Results show that our framework
significantly reduces prediction errors of various ST models by 9.41% on
average (35.36% at most). Furthermore, a case study reveals the
interpretability potential of our framework. | [
"Jiahao Ji",
"Jingyuan Wang",
"Yu Mou",
"Cheng Long"
] | 2023-10-16 13:12:27 | http://arxiv.org/abs/2310.10374v1 | http://arxiv.org/pdf/2310.10374v1 | 2310.10374v1 |
Machine learning in physics: a short guide | Machine learning is a rapidly growing field with the potential to
revolutionize many areas of science, including physics. This review provides a
brief overview of machine learning in physics, covering the main concepts of
supervised, unsupervised, and reinforcement learning, as well as more
specialized topics such as causal inference, symbolic regression, and deep
learning. We present some of the principal applications of machine learning in
physics and discuss the associated challenges and perspectives. | [
"Francisco A. Rodrigues"
] | 2023-10-16 13:05:47 | http://arxiv.org/abs/2310.10368v1 | http://arxiv.org/pdf/2310.10368v1 | 2310.10368v1 |
Prompt Tuning for Multi-View Graph Contrastive Learning | In recent years, "pre-training and fine-tuning" has emerged as a promising
approach in addressing the issues of label dependency and poor generalization
performance in traditional GNNs. To reduce labeling requirement, the
"pre-train, fine-tune" and "pre-train, prompt" paradigms have become
increasingly common. In particular, prompt tuning is a popular alternative to
"pre-training and fine-tuning" in natural language processing, which is
designed to narrow the gap between pre-training and downstream objectives.
However, existing study of prompting on graphs is still limited, lacking a
framework that can accommodate commonly used graph pre-training methods and
downstream tasks. In this paper, we propose a multi-view graph contrastive
learning method as pretext and design a prompting tuning for it. Specifically,
we first reformulate graph pre-training and downstream tasks into a common
format. Second, we construct multi-view contrasts to capture relevant
information of graphs by GNN. Third, we design a prompting tuning method for
our multi-view graph contrastive learning method to bridge the gap between
pretexts and downsteam tasks. Finally, we conduct extensive experiments on
benchmark datasets to evaluate and analyze our proposed method. | [
"Chenghua Gong",
"Xiang Li",
"Jianxiang Yu",
"Cheng Yao",
"Jiaqi Tan",
"Chengcheng Yu",
"Dawei Yin"
] | 2023-10-16 12:58:04 | http://arxiv.org/abs/2310.10362v1 | http://arxiv.org/pdf/2310.10362v1 | 2310.10362v1 |
An Anytime Algorithm for Good Arm Identification | In good arm identification (GAI), the goal is to identify one arm whose
average performance exceeds a given threshold, referred to as good arm, if it
exists. Few works have studied GAI in the fixed-budget setting, when the
sampling budget is fixed beforehand, or the anytime setting, when a
recommendation can be asked at any time. We propose APGAI, an anytime and
parameter-free sampling rule for GAI in stochastic bandits. APGAI can be
straightforwardly used in fixed-confidence and fixed-budget settings. First, we
derive upper bounds on its probability of error at any time. They show that
adaptive strategies are more efficient in detecting the absence of good arms
than uniform sampling. Second, when APGAI is combined with a stopping rule, we
prove upper bounds on the expected sampling complexity, holding at any
confidence level. Finally, we show good empirical performance of APGAI on
synthetic and real-world data. Our work offers an extensive overview of the GAI
problem in all settings. | [
"Marc Jourdan",
"Clémence Réda"
] | 2023-10-16 12:51:26 | http://arxiv.org/abs/2310.10359v1 | http://arxiv.org/pdf/2310.10359v1 | 2310.10359v1 |
Compressed Sensing of Generative Sparse-latent (GSL) Signals | We consider reconstruction of an ambient signal in a compressed sensing (CS)
setup where the ambient signal has a neural network based generative model. The
generative model has a sparse-latent input and we refer to the generated
ambient signal as generative sparse-latent signal (GSL). The proposed sparsity
inducing reconstruction algorithm is inherently non-convex, and we show that a
gradient based search provides a good reconstruction performance. We evaluate
our proposed algorithm using simulated data. | [
"Antoine Honoré",
"Anubhab Ghosh",
"Saikat Chatterjee"
] | 2023-10-16 12:49:33 | http://arxiv.org/abs/2310.15119v1 | http://arxiv.org/pdf/2310.15119v1 | 2310.15119v1 |
Multimodal Object Query Initialization for 3D Object Detection | 3D object detection models that exploit both LiDAR and camera sensor features
are top performers in large-scale autonomous driving benchmarks. A transformer
is a popular network architecture used for this task, in which so-called object
queries act as candidate objects. Initializing these object queries based on
current sensor inputs is a common practice. For this, existing methods strongly
rely on LiDAR data however, and do not fully exploit image features. Besides,
they introduce significant latency. To overcome these limitations we propose
EfficientQ3M, an efficient, modular, and multimodal solution for object query
initialization for transformer-based 3D object detection models. The proposed
initialization method is combined with a "modality-balanced" transformer
decoder where the queries can access all sensor modalities throughout the
decoder. In experiments, we outperform the state of the art in
transformer-based LiDAR object detection on the competitive nuScenes benchmark
and showcase the benefits of input-dependent multimodal query initialization,
while being more efficient than the available alternatives for LiDAR-camera
initialization. The proposed method can be applied with any combination of
sensor modalities as input, demonstrating its modularity. | [
"Mathijs R. van Geerenstein",
"Felicia Ruppel",
"Klaus Dietmayer",
"Dariu M. Gavrila"
] | 2023-10-16 12:42:44 | http://arxiv.org/abs/2310.10353v1 | http://arxiv.org/pdf/2310.10353v1 | 2310.10353v1 |
Attribution Patching Outperforms Automated Circuit Discovery | Automated interpretability research has recently attracted attention as a
potential research direction that could scale explanations of neural network
behavior to large models. Existing automated circuit discovery work applies
activation patching to identify subnetworks responsible for solving specific
tasks (circuits). In this work, we show that a simple method based on
attribution patching outperforms all existing methods while requiring just two
forward passes and a backward pass. We apply a linear approximation to
activation patching to estimate the importance of each edge in the
computational subgraph. Using this approximation, we prune the least important
edges of the network. We survey the performance and limitations of this method,
finding that averaged over all tasks our method has greater AUC from circuit
recovery than other methods. | [
"Aaquib Syed",
"Can Rager",
"Arthur Conmy"
] | 2023-10-16 12:34:43 | http://arxiv.org/abs/2310.10348v1 | http://arxiv.org/pdf/2310.10348v1 | 2310.10348v1 |
Hamming Encoder: Mining Discriminative k-mers for Discrete Sequence Classification | Sequence classification has numerous applications in various fields. Despite
extensive studies in the last decades, many challenges still exist,
particularly in pattern-based methods. Existing pattern-based methods measure
the discriminative power of each feature individually during the mining
process, leading to the result of missing some combinations of features with
discriminative power. Furthermore, it is difficult to ensure the overall
discriminative performance after converting sequences into feature vectors. To
address these challenges, we propose a novel approach called Hamming Encoder,
which utilizes a binarized 1D-convolutional neural network (1DCNN) architecture
to mine discriminative k-mer sets. In particular, we adopt a Hamming
distance-based similarity measure to ensure consistency in the feature mining
and classification procedure. Our method involves training an interpretable CNN
encoder for sequential data and performing a gradient-based search for
discriminative k-mer combinations. Experiments show that the Hamming Encoder
method proposed in this paper outperforms existing state-of-the-art methods in
terms of classification accuracy. | [
"Junjie Dong",
"Mudi Jiang",
"Lianyu Hu",
"Zengyou He"
] | 2023-10-16 12:03:27 | http://arxiv.org/abs/2310.10321v2 | http://arxiv.org/pdf/2310.10321v2 | 2310.10321v2 |
Subsets and Splits