categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2404.01790
| null | null |
http://arxiv.org/pdf/2404.01790v1
|
2024-04-02T09:53:20Z
|
2024-04-02T09:53:20Z
|
Super-Resolution Analysis for Landfill Waste Classification
|
Illegal landfills are a critical issue due to their environmental, economic, and public health impacts. This study leverages aerial imagery for environmental crime monitoring. While advances in artificial intelligence and computer vision hold promise, the challenge lies in training models with high-resolution literature datasets and adapting them to open-access low-resolution images. Considering the substantial quality differences and limited annotation, this research explores the adaptability of models across these domains. Motivated by the necessity for a comprehensive evaluation of waste detection algorithms, it advocates cross-domain classification and super-resolution enhancement to analyze the impact of different image resolutions on waste classification as an evaluation to combat the proliferation of illegal landfills. We observed performance improvements by enhancing image quality but noted an influence on model sensitivity, necessitating careful threshold fine-tuning.
|
[
"['Matias Molina' 'Rita P. Ribeiro' 'Bruno Veloso' 'João Gama']"
] |
null | null |
2404.01804
| null | null |
http://arxiv.org/pdf/2404.01804v1
|
2024-04-02T10:06:21Z
|
2024-04-02T10:06:21Z
|
Neuromorphic Wireless Device-Edge Co-Inference via the Directed
Information Bottleneck
|
An important use case of next-generation wireless systems is device-edge co-inference, where a semantic task is partitioned between a device and an edge server. The device carries out data collection and partial processing of the data, while the remote server completes the given task based on information received from the device. It is often required that processing and communication be run as efficiently as possible at the device, while more computing resources are available at the edge. To address such scenarios, we introduce a new system solution, termed neuromorphic wireless device-edge co-inference. According to it, the device runs sensing, processing, and communication units using neuromorphic hardware, while the server employs conventional radio and computing technologies. The proposed system is designed using a transmitter-centric information-theoretic criterion that targets a reduction of the communication overhead, while retaining the most relevant information for the end-to-end semantic task of interest. Numerical results on standard data sets validate the proposed architecture, and a preliminary testbed realization is reported.
|
[
"['Yuzhen Ke' 'Zoran Utkovski' 'Mehdi Heshmati' 'Osvaldo Simeone'\n 'Johannes Dommel' 'Slawomir Stanczak']"
] |
null | null |
2404.01805
| null | null |
http://arxiv.org/pdf/2404.01805v1
|
2024-04-02T10:06:30Z
|
2024-04-02T10:06:30Z
|
Improved Text Emotion Prediction Using Combined Valence and Arousal
Ordinal Classification
|
Emotion detection in textual data has received growing interest in recent years, as it is pivotal for developing empathetic human-computer interaction systems. This paper introduces a method for categorizing emotions from text, which acknowledges and differentiates between the diversified similarities and distinctions of various emotions. Initially, we establish a baseline by training a transformer-based model for standard emotion classification, achieving state-of-the-art performance. We argue that not all misclassifications are of the same importance, as there are perceptual similarities among emotional classes. We thus redefine the emotion labeling problem by shifting it from a traditional classification model to an ordinal classification one, where discrete emotions are arranged in a sequential order according to their valence levels. Finally, we propose a method that performs ordinal classification in the two-dimensional emotion space, considering both valence and arousal scales. The results show that our approach not only preserves high accuracy in emotion prediction but also significantly reduces the magnitude of errors in cases of misclassification.
|
[
"['Michael Mitsios' 'Georgios Vamvoukakis' 'Georgia Maniati'\n 'Nikolaos Ellinas' 'Georgios Dimitriou' 'Konstantinos Markopoulos'\n 'Panos Kakoulidis' 'Alexandra Vioni' 'Myrsini Christidou' 'Junkwang Oh'\n 'Gunu Jho' 'Inchul Hwang' 'Georgios Vardaxoglou' 'Aimilios Chalamandaris'\n 'Pirros Tsiakoulis' 'Spyros Raptis']"
] |
null | null |
2404.01814
| null | null |
http://arxiv.org/pdf/2404.01814v1
|
2024-04-02T10:16:30Z
|
2024-04-02T10:16:30Z
|
A neural network-based approach to hybrid systems identification for
control
|
We consider the problem of designing a machine learning-based model of an unknown dynamical system from a finite number of (state-input)-successor state data points, such that the model obtained is also suitable for optimal control design. We propose a specific neural network (NN) architecture that yields a hybrid system with piecewise-affine dynamics that is differentiable with respect to the network's parameters, thereby enabling the use of derivative-based training procedures. We show that a careful choice of our NN's weights produces a hybrid system model with structural properties that are highly favourable when used as part of a finite horizon optimal control problem (OCP). Specifically, we show that optimal solutions with strong local optimality guarantees can be computed via nonlinear programming, in contrast to classical OCPs for general hybrid systems which typically require mixed-integer optimization. In addition to being well-suited for optimal control design, numerical simulations illustrate that our NN-based technique enjoys very similar performance to state-of-the-art system identification methodologies for hybrid systems and it is competitive on nonlinear benchmarks.
|
[
"['Filippo Fabiani' 'Bartolomeo Stellato' 'Daniele Masti' 'Paul J. Goulart']"
] |
null | null |
2404.01822
| null | null |
http://arxiv.org/pdf/2404.01822v1
|
2024-04-02T10:32:21Z
|
2024-04-02T10:32:21Z
|
A (More) Realistic Evaluation Setup for Generalisation of Community
Models on Malicious Content Detection
|
Community models for malicious content detection, which take into account the context from a social graph alongside the content itself, have shown remarkable performance on benchmark datasets. Yet, misinformation and hate speech continue to propagate on social media networks. This mismatch can be partially attributed to the limitations of current evaluation setups that neglect the rapid evolution of online content and the underlying social graph. In this paper, we propose a novel evaluation setup for model generalisation based on our few-shot subgraph sampling approach. This setup tests for generalisation through few labelled examples in local explorations of a larger graph, emulating more realistic application settings. We show this to be a challenging inductive setup, wherein strong performance on the training graph is not indicative of performance on unseen tasks, domains, or graph structures. Lastly, we show that graph meta-learners trained with our proposed few-shot subgraph sampling outperform standard community models in the inductive setup. We make our code publicly available.
|
[
"['Ivo Verhoeven' 'Pushkar Mishra' 'Rahel Beloch' 'Helen Yannakoudakis'\n 'Ekaterina Shutova']"
] |
null | null |
2404.01828
| null | null |
http://arxiv.org/pdf/2404.01828v1
|
2024-04-02T10:41:51Z
|
2024-04-02T10:41:51Z
|
Defense without Forgetting: Continual Adversarial Defense with
Anisotropic & Isotropic Pseudo Replay
|
Deep neural networks have demonstrated susceptibility to adversarial attacks. Adversarial defense techniques often focus on one-shot setting to maintain robustness against attack. However, new attacks can emerge in sequences in real-world deployment scenarios. As a result, it is crucial for a defense model to constantly adapt to new attacks, but the adaptation process can lead to catastrophic forgetting of previously defended against attacks. In this paper, we discuss for the first time the concept of continual adversarial defense under a sequence of attacks, and propose a lifelong defense baseline called Anisotropic & Isotropic Replay (AIR), which offers three advantages: (1) Isotropic replay ensures model consistency in the neighborhood distribution of new data, indirectly aligning the output preference between old and new tasks. (2) Anisotropic replay enables the model to learn a compromise data manifold with fresh mixed semantics for further replay constraints and potential future attacks. (3) A straightforward regularizer mitigates the 'plasticity-stability' trade-off by aligning model output between new and old tasks. Experiment results demonstrate that AIR can approximate or even exceed the empirical performance upper bounds achieved by Joint Training.
|
[
"['Yuhang Zhou' 'Zhongyun Hua']"
] |
null | null |
2404.01830
| null | null |
http://arxiv.org/pdf/2404.01830v1
|
2024-04-02T10:42:44Z
|
2024-04-02T10:42:44Z
|
Doubly-Robust Off-Policy Evaluation with Estimated Logging Policy
|
We introduce a novel doubly-robust (DR) off-policy evaluation (OPE) estimator for Markov decision processes, DRUnknown, designed for situations where both the logging policy and the value function are unknown. The proposed estimator initially estimates the logging policy and then estimates the value function model by minimizing the asymptotic variance of the estimator while considering the estimating effect of the logging policy. When the logging policy model is correctly specified, DRUnknown achieves the smallest asymptotic variance within the class containing existing OPE estimators. When the value function model is also correctly specified, DRUnknown is optimal as its asymptotic variance reaches the semiparametric lower bound. We present experimental results conducted in contextual bandits and reinforcement learning to compare the performance of DRUnknown with that of existing methods.
|
[
"['Kyungbok Lee' 'Myunghee Cho Paik']"
] |
null | null |
2404.01832
| null | null |
http://arxiv.org/pdf/2404.01832v1
|
2024-04-02T10:44:55Z
|
2024-04-02T10:44:55Z
|
When does Subagging Work?
|
We study the effectiveness of subagging, or subsample aggregating, on regression trees, a popular non-parametric method in machine learning. First, we give sufficient conditions for pointwise consistency of trees. We formalize that (i) the bias depends on the diameter of cells, hence trees with few splits tend to be biased, and (ii) the variance depends on the number of observations in cells, hence trees with many splits tend to have large variance. While these statements for bias and variance are known to hold globally in the covariate space, we show that, under some constraints, they are also true locally. Second, we compare the performance of subagging to that of trees across different numbers of splits. We find that (1) for any given number of splits, subagging improves upon a single tree, and (2) this improvement is larger for many splits than it is for few splits. However, (3) a single tree grown at optimal size can outperform subagging if the size of its individual trees is not optimally chosen. This last result goes against common practice of growing large randomized trees to eliminate bias and then averaging to reduce variance.
|
[
"['Christos Revelas' 'Otilia Boldea' 'Bas J. M. Werker']"
] |
null | null |
2404.01847
| null | null |
http://arxiv.org/pdf/2404.01847v2
|
2024-05-27T20:34:44Z
|
2024-04-02T11:12:42Z
|
Accelerating Transformer Pre-training with 2:4 Sparsity
|
Training large transformers is slow, but recent innovations on GPU architecture give us an advantage. NVIDIA Ampere GPUs can execute a fine-grained 2:4 sparse matrix multiplication twice as fast as its dense equivalent. In the light of this property, we comprehensively investigate the feasibility of accelerating feed-forward networks (FFNs) of transformers in pre-training. First, we define a ``flip rate'' to monitor the stability of a 2:4 training process. Utilizing this metric, we propose three techniques to preserve accuracy: to modify the sparse-refined straight-through estimator by applying the masked decay term on gradients, to determine a feasible decay factor in warm-up stage, and to enhance the model's quality by a dense fine-tuning procedure near the end of pre-training. Besides, we devise two techniques to practically accelerate training: to calculate transposable 2:4 masks by convolution, and to accelerate gated activation functions by reducing GPU L2 cache miss. Experiments show that our 2:4 sparse training algorithm achieves similar convergence to dense training algorithms on several transformer pre-training tasks, while actual acceleration can be observed on different shapes of transformer block apparently. Our toolkit is available at https://github.com/huyz2023/2by4-pretrain.
|
[
"['Yuezhou Hu' 'Kang Zhao' 'Weiyu Huang' 'Jianfei Chen' 'Jun Zhu']"
] |
null | null |
2404.01853
| null | null |
http://arxiv.org/pdf/2404.01853v1
|
2024-04-02T11:30:22Z
|
2024-04-02T11:30:22Z
|
Pairwise Similarity Distribution Clustering for Noisy Label Learning
|
Noisy label learning aims to train deep neural networks using a large amount of samples with noisy labels, whose main challenge comes from how to deal with the inaccurate supervision caused by wrong labels. Existing works either take the label correction or sample selection paradigm to involve more samples with accurate labels into the training process. In this paper, we propose a simple yet effective sample selection algorithm, termed as Pairwise Similarity Distribution Clustering~(PSDC), to divide the training samples into one clean set and another noisy set, which can power any of the off-the-shelf semi-supervised learning regimes to further train networks for different downstream tasks. Specifically, we take the pairwise similarity between sample pairs to represent the sample structure, and the Gaussian Mixture Model~(GMM) to model the similarity distribution between sample pairs belonging to the same noisy cluster, therefore each sample can be confidently divided into the clean set or noisy set. Even under severe label noise rate, the resulting data partition mechanism has been proved to be more robust in judging the label confidence in both theory and practice. Experimental results on various benchmark datasets, such as CIFAR-10, CIFAR-100 and Clothing1M, demonstrate significant improvements over state-of-the-art methods.
|
[
"['Sihan Bai']"
] |
null | null |
2404.01857
| null | null |
http://arxiv.org/pdf/2404.01857v1
|
2024-04-02T11:35:05Z
|
2024-04-02T11:35:05Z
|
Detecting Gender Bias in Course Evaluations
|
An outtake from the findnings of a master thesis studying gender bias in course evaluations through the lense of machine learning and nlp. We use different methods to examine and explore the data and find differences in what students write about courses depending on gender of the examiner. Data from English and Swedish courses are evaluated and compared, in order to capture more nuance in the gender bias that might be found. Here we present the results from the work so far, but this is an ongoing project and there is more work to do.
|
[
"['Sarah Lindau' 'Linnea Nilsson']"
] |
null | null |
2404.01863
| null | null |
http://arxiv.org/pdf/2404.01863v1
|
2024-04-02T11:40:38Z
|
2024-04-02T11:40:38Z
|
Confidence-aware Reward Optimization for Fine-tuning Text-to-Image
Models
|
Fine-tuning text-to-image models with reward functions trained on human feedback data has proven effective for aligning model behavior with human intent. However, excessive optimization with such reward models, which serve as mere proxy objectives, can compromise the performance of fine-tuned models, a phenomenon known as reward overoptimization. To investigate this issue in depth, we introduce the Text-Image Alignment Assessment (TIA2) benchmark, which comprises a diverse collection of text prompts, images, and human annotations. Our evaluation of several state-of-the-art reward models on this benchmark reveals their frequent misalignment with human assessment. We empirically demonstrate that overoptimization occurs notably when a poorly aligned reward model is used as the fine-tuning objective. To address this, we propose TextNorm, a simple method that enhances alignment based on a measure of reward model confidence estimated across a set of semantically contrastive text prompts. We demonstrate that incorporating the confidence-calibrated rewards in fine-tuning effectively reduces overoptimization, resulting in twice as many wins in human evaluation for text-image alignment compared against the baseline reward models.
|
[
"['Kyuyoung Kim' 'Jongheon Jeong' 'Minyong An' 'Mohammad Ghavamzadeh'\n 'Krishnamurthy Dvijotham' 'Jinwoo Shin' 'Kimin Lee']"
] |
null | null |
2404.01867
| null | null |
http://arxiv.org/pdf/2404.01867v1
|
2024-04-02T11:44:37Z
|
2024-04-02T11:44:37Z
|
Active Exploration in Bayesian Model-based Reinforcement Learning for
Robot Manipulation
|
Efficiently tackling multiple tasks within complex environment, such as those found in robot manipulation, remains an ongoing challenge in robotics and an opportunity for data-driven solutions, such as reinforcement learning (RL). Model-based RL, by building a dynamic model of the robot, enables data reuse and transfer learning between tasks with the same robot and similar environment. Furthermore, data gathering in robotics is expensive and we must rely on data efficient approaches such as model-based RL, where policy learning is mostly conducted on cheaper simulations based on the learned model. Therefore, the quality of the model is fundamental for the performance of the posterior tasks. In this work, we focus on improving the quality of the model and maintaining the data efficiency by performing active learning of the dynamic model during a preliminary exploration phase based on maximize information gathering. We employ Bayesian neural network models to represent, in a probabilistic way, both the belief and information encoded in the dynamic model during exploration. With our presented strategies we manage to actively estimate the novelty of each transition, using this as the exploration reward. In this work, we compare several Bayesian inference methods for neural networks, some of which have never been used in a robotics context, and evaluate them in a realistic robot manipulation setup. Our experiments show the advantages of our Bayesian model-based RL approach, with similar quality in the results than relevant alternatives with much lower requirements regarding robot execution steps. Unlike related previous studies that focused the validation solely on toy problems, our research takes a step towards more realistic setups, tackling robotic arm end-tasks.
|
[
"['Carlos Plou' 'Ana C. Murillo' 'Ruben Martinez-Cantin']"
] |
null | null |
2404.01872
| null | null |
http://arxiv.org/pdf/2404.01872v1
|
2024-04-02T11:55:50Z
|
2024-04-02T11:55:50Z
|
Fast and Adaptive Questionnaires for Voting Advice Applications
|
The effectiveness of Voting Advice Applications (VAA) is often compromised by the length of their questionnaires. To address user fatigue and incomplete responses, some applications (such as the Swiss Smartvote) offer a condensed version of their questionnaire. However, these condensed versions can not ensure the accuracy of recommended parties or candidates, which we show to remain below 40%. To tackle these limitations, this work introduces an adaptive questionnaire approach that selects subsequent questions based on users' previous answers, aiming to enhance recommendation accuracy while reducing the number of questions posed to the voters. Our method uses an encoder and decoder module to predict missing values at any completion stage, leveraging a two-dimensional latent space reflective of political science's traditional methods for visualizing political orientations. Additionally, a selector module is proposed to determine the most informative subsequent question based on the voter's current position in the latent space and the remaining unanswered questions. We validated our approach using the Smartvote dataset from the Swiss Federal elections in 2019, testing various spatial models and selection methods to optimize the system's predictive accuracy. Our findings indicate that employing the IDEAL model both as encoder and decoder, combined with a PosteriorRMSE method for question selection, significantly improves the accuracy of recommendations, achieving 74% accuracy after asking the same number of questions as in the condensed version.
|
[
"['Fynn Bachmann' 'Cristina Sarasua' 'Abraham Bernstein']"
] |
null | null |
2404.01875
| null | null |
http://arxiv.org/pdf/2404.01875v1
|
2024-04-02T11:59:58Z
|
2024-04-02T11:59:58Z
|
Satellite Federated Edge Learning: Architecture Design and Convergence
Analysis
|
The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server for centralized processing, raising privacy and bandwidth concerns. Federated edge learning (FEEL), as a distributed machine learning approach, has the potential to address these challenges by sharing only model parameters instead of raw data. Although promising, the dynamics of LEO networks, characterized by the high mobility of satellites and short ground-to-satellite link (GSL) duration, pose unique challenges for FEEL. Notably, frequent model transmission between the satellites and ground incurs prolonged waiting time and large transmission latency. This paper introduces a novel FEEL algorithm, named FEDMEGA, tailored to LEO mega-constellation networks. By integrating inter-satellite links (ISL) for intra-orbit model aggregation, the proposed algorithm significantly reduces the usage of low data rate and intermittent GSL. Our proposed method includes a ring all-reduce based intra-orbit aggregation mechanism, coupled with a network flow-based transmission scheme for global model aggregation, which enhances transmission efficiency. Theoretical convergence analysis is provided to characterize the algorithm performance. Extensive simulations show that our FEDMEGA algorithm outperforms existing satellite FEEL algorithms, exhibiting an approximate 30% improvement in convergence rate.
|
[
"['Yuanming Shi' 'Li Zeng' 'Jingyang Zhu' 'Yong Zhou' 'Chunxiao Jiang'\n 'Khaled B. Letaief']"
] |
null | null |
2404.01877
| null | null |
http://arxiv.org/pdf/2404.01877v1
|
2024-04-02T12:05:02Z
|
2024-04-02T12:05:02Z
|
Procedural Fairness in Machine Learning
|
Fairness in machine learning (ML) has received much attention. However, existing studies have mainly focused on the distributive fairness of ML models. The other dimension of fairness, i.e., procedural fairness, has been neglected. In this paper, we first define the procedural fairness of ML models, and then give formal definitions of individual and group procedural fairness. We propose a novel metric to evaluate the group procedural fairness of ML models, called $GPF_{FAE}$, which utilizes a widely used explainable artificial intelligence technique, namely feature attribution explanation (FAE), to capture the decision process of the ML models. We validate the effectiveness of $GPF_{FAE}$ on a synthetic dataset and eight real-world datasets. Our experiments reveal the relationship between procedural and distributive fairness of the ML model. Based on our analysis, we propose a method for identifying the features that lead to the procedural unfairness of the model and propose two methods to improve procedural fairness after identifying unfair features. Our experimental results demonstrate that we can accurately identify the features that lead to procedural unfairness in the ML model, and both of our proposed methods can significantly improve procedural fairness with a slight impact on model performance, while also improving distributive fairness.
|
[
"['Ziming Wang' 'Changwu Huang' 'Xin Yao']"
] |
null | null |
2404.01883
| null | null |
http://arxiv.org/abs/2404.01883v1
|
2024-04-02T12:15:37Z
|
2024-04-02T12:15:37Z
|
Adversarial Combinatorial Bandits with Switching Costs
|
We study the problem of adversarial combinatorial bandit with a switching cost $lambda$ for a switch of each selected arm in each round, considering both the bandit feedback and semi-bandit feedback settings. In the oblivious adversarial case with $K$ base arms and time horizon $T$, we derive lower bounds for the minimax regret and design algorithms to approach them. To prove these lower bounds, we design stochastic loss sequences for both feedback settings, building on an idea from previous work in Dekel et al. (2014). The lower bound for bandit feedback is $ tilde{Omega}big( (lambda K)^{frac{1}{3}} (TI)^{frac{2}{3}}big)$ while that for semi-bandit feedback is $ tilde{Omega}big( (lambda K I)^{frac{1}{3}} T^{frac{2}{3}}big)$ where $I$ is the number of base arms in the combinatorial arm played in each round. To approach these lower bounds, we design algorithms that operate in batches by dividing the time horizon into batches to restrict the number of switches between actions. For the bandit feedback setting, where only the total loss of the combinatorial arm is observed, we introduce the Batched-Exp2 algorithm which achieves a regret upper bound of $tilde{O}big((lambda K)^{frac{1}{3}}T^{frac{2}{3}}I^{frac{4}{3}}big)$ as $T$ tends to infinity. In the semi-bandit feedback setting, where all losses for the combinatorial arm are observed, we propose the Batched-BROAD algorithm which achieves a regret upper bound of $tilde{O}big( (lambda K)^{frac{1}{3}} (TI)^{frac{2}{3}}big)$.
|
[
"['Yanyan Dong' 'Vincent Y. F. Tan']"
] |
null | null |
2404.01897
| null | null |
http://arxiv.org/pdf/2404.01897v1
|
2024-04-02T12:36:40Z
|
2024-04-02T12:36:40Z
|
Continuous Spiking Graph Neural Networks
|
Continuous graph neural networks (CGNNs) have garnered significant attention due to their ability to generalize existing discrete graph neural networks (GNNs) by introducing continuous dynamics. They typically draw inspiration from diffusion-based methods to introduce a novel propagation scheme, which is analyzed using ordinary differential equations (ODE). However, the implementation of CGNNs requires significant computational power, making them challenging to deploy on battery-powered devices. Inspired by recent spiking neural networks (SNNs), which emulate a biological inference process and provide an energy-efficient neural architecture, we incorporate the SNNs with CGNNs in a unified framework, named Continuous Spiking Graph Neural Networks (COS-GNN). We employ SNNs for graph node representation at each time step, which are further integrated into the ODE process along with time. To enhance information preservation and mitigate information loss in SNNs, we introduce the high-order structure of COS-GNN, which utilizes the second-order ODE for spiking representation and continuous propagation. Moreover, we provide the theoretical proof that COS-GNN effectively mitigates the issues of exploding and vanishing gradients, enabling us to capture long-range dependencies between nodes. Experimental results on graph-based learning tasks demonstrate the effectiveness of the proposed COS-GNN over competitive baselines.
|
[
"['Nan Yin' 'Mengzhu Wan' 'Li Shen' 'Hitesh Laxmichand Patel' 'Baopu Li'\n 'Bin Gu' 'Huan Xiong']"
] |
null | null |
2404.01903
| null | null |
http://arxiv.org/pdf/2404.01903v1
|
2024-04-02T12:44:44Z
|
2024-04-02T12:44:44Z
|
Activation Steering for Robust Type Prediction in CodeLLMs
|
Contemporary LLMs pretrained on code are capable of succeeding at a wide variety of programming tasks. However, their performance is very sensitive to syntactic features, such as the names of variables and types, the structure of code, and presence of type hints. We contribute an inference-time technique to make CodeLLMs more robust to syntactic distractors that are semantically irrelevant. Our methodology relies on activation steering, which involves editing internal model activations to steer the model towards the correct prediction. We contribute a novel way to construct steering vectors by taking inspiration from mutation testing, which constructs minimal semantics-breaking code edits. In contrast, we construct steering vectors from semantics-preserving code edits. We apply our approach to the task of type prediction for the gradually typed languages Python and TypeScript. This approach corrects up to 90% of type mispredictions. Finally, we show that steering vectors calculated from Python activations reliably correct type mispredictions in TypeScript, and vice versa. This result suggests that LLMs may be learning to transfer knowledge of types across programming languages.
|
[
"['Francesca Lucchetti' 'Arjun Guha']"
] |
null | null |
2404.01907
| null | null |
http://arxiv.org/pdf/2404.01907v1
|
2024-04-02T12:49:22Z
|
2024-04-02T12:49:22Z
|
Humanizing Machine-Generated Content: Evading AI-Text Detection through
Adversarial Attack
|
With the development of large language models (LLMs), detecting whether text is generated by a machine becomes increasingly challenging in the face of malicious use cases like the spread of false information, protection of intellectual property, and prevention of academic plagiarism. While well-trained text detectors have demonstrated promising performance on unseen test data, recent research suggests that these detectors have vulnerabilities when dealing with adversarial attacks such as paraphrasing. In this paper, we propose a framework for a broader class of adversarial attacks, designed to perform minor perturbations in machine-generated content to evade detection. We consider two attack settings: white-box and black-box, and employ adversarial learning in dynamic scenarios to assess the potential enhancement of the current detection model's robustness against such attacks. The empirical results reveal that the current detection models can be compromised in as little as 10 seconds, leading to the misclassification of machine-generated text as human-written content. Furthermore, we explore the prospect of improving the model's robustness over iterative adversarial learning. Although some improvements in model robustness are observed, practical applications still face significant challenges. These findings shed light on the future development of AI-text detectors, emphasizing the need for more accurate and robust detection methods.
|
[
"['Ying Zhou' 'Ben He' 'Le Sun']"
] |
null | null |
2404.01930
| null | null |
http://arxiv.org/pdf/2404.01930v1
|
2024-04-02T13:23:54Z
|
2024-04-02T13:23:54Z
|
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies
|
We study adaptive combinatorial maximization, which is a core challenge in machine learning, with applications in active learning as well as many other domains. We study the Bayesian setting, and consider the objectives of maximization under a cardinality constraint and minimum cost coverage. We provide new comprehensive approximation guarantees that subsume previous results, as well as considerably strengthen them. Our approximation guarantees simultaneously support the maximal gain ratio as well as near-submodular utility functions, and include both maximization under a cardinality constraint and a minimum cost coverage guarantee. In addition, we provided an approximation guarantee for a modified prior, which is crucial for obtaining active learning guarantees that do not depend on the smallest probability in the prior. Moreover, we discover a new parameter of adaptive selection policies, which we term the "maximal gain ratio". We show that this parameter is strictly less restrictive than the greedy approximation parameter that has been used in previous approximation guarantees, and show that it can be used to provide stronger approximation guarantees than previous results. In particular, we show that the maximal gain ratio is never larger than the greedy approximation factor of a policy, and that it can be considerably smaller. This provides a new insight into the properties that make a policy useful for adaptive combinatorial maximization.
|
[
"['Shlomi Weitzman' 'Sivan Sabato']"
] |
null | null |
2404.01932
| null | null |
http://arxiv.org/pdf/2404.01932v1
|
2024-04-02T13:25:16Z
|
2024-04-02T13:25:16Z
|
Bridging Language, Vision and Action: Multimodal VAEs in Robotic
Manipulation Tasks
|
In this work, we focus on unsupervised vision-language-action mapping in the area of robotic manipulation. Recently, multiple approaches employing pre-trained large language and vision models have been proposed for this task. However, they are computationally demanding and require careful fine-tuning of the produced outputs. A more lightweight alternative would be the implementation of multimodal Variational Autoencoders (VAEs) which can extract the latent features of the data and integrate them into a joint representation, as has been demonstrated mostly on image-image or image-text data for the state-of-the-art models. Here we explore whether and how can multimodal VAEs be employed in unsupervised robotic manipulation tasks in a simulated environment. Based on the obtained results, we propose a model-invariant training alternative that improves the models' performance in a simulator by up to 55%. Moreover, we systematically evaluate the challenges raised by the individual tasks such as object or robot position variability, number of distractors or the task length. Our work thus also sheds light on the potential benefits and limitations of using the current multimodal VAEs for unsupervised learning of robotic motion trajectories based on vision and language.
|
[
"['Gabriela Sejnova' 'Michal Vavrecka' 'Karla Stepanova']"
] |
null | null |
2404.01936
| null | null |
http://arxiv.org/pdf/2404.01936v1
|
2024-04-02T13:31:19Z
|
2024-04-02T13:31:19Z
|
Settling Time vs. Accuracy Tradeoffs for Clustering Big Data
|
We study the theoretical and practical runtime limits of k-means and k-median clustering on large datasets. Since effectively all clustering methods are slower than the time it takes to read the dataset, the fastest approach is to quickly compress the data and perform the clustering on the compressed representation. Unfortunately, there is no universal best choice for compressing the number of points - while random sampling runs in sublinear time and coresets provide theoretical guarantees, the former does not enforce accuracy while the latter is too slow as the numbers of points and clusters grow. Indeed, it has been conjectured that any sensitivity-based coreset construction requires super-linear time in the dataset size. We examine this relationship by first showing that there does exist an algorithm that obtains coresets via sensitivity sampling in effectively linear time - within log-factors of the time it takes to read the data. Any approach that significantly improves on this must then resort to practical heuristics, leading us to consider the spectrum of sampling strategies across both real and artificial datasets in the static and streaming settings. Through this, we show the conditions in which coresets are necessary for preserving cluster validity as well as the settings in which faster, cruder sampling strategies are sufficient. As a result, we provide a comprehensive theoretical and practical blueprint for effective clustering regardless of data size. Our code is publicly available and has scripts to recreate the experiments.
|
[
"['Andrew Draganov' 'David Saulpic' 'Chris Schwiegelshohn']"
] |
null | null |
2404.01946
| null | null |
http://arxiv.org/pdf/2404.01946v1
|
2024-04-02T13:42:29Z
|
2024-04-02T13:42:29Z
|
Synthetic Data for Robust Stroke Segmentation
|
Deep learning-based semantic segmentation in neuroimaging currently requires high-resolution scans and extensive annotated datasets, posing significant barriers to clinical applicability. We present a novel synthetic framework for the task of lesion segmentation, extending the capabilities of the established SynthSeg approach to accommodate large heterogeneous pathologies with lesion-specific augmentation strategies. Our method trains deep learning models, demonstrated here with the UNet architecture, using label maps derived from healthy and stroke datasets, facilitating the segmentation of both healthy tissue and pathological lesions without sequence-specific training data. Evaluated against in-domain and out-of-domain (OOD) datasets, our framework demonstrates robust performance, rivaling current methods within the training domain and significantly outperforming them on OOD data. This contribution holds promise for advancing medical imaging analysis in clinical settings, especially for stroke pathology, by enabling reliable segmentation across varied imaging sequences with reduced dependency on large annotated corpora. Code and weights available at https://github.com/liamchalcroft/SynthStroke.
|
[
"['Liam Chalcroft' 'Ioannis Pappas' 'Cathy J. Price' 'John Ashburner']"
] |
null | null |
2404.01958
| null | null |
http://arxiv.org/pdf/2404.01958v1
|
2024-04-02T13:54:05Z
|
2024-04-02T13:54:05Z
|
MESEN: Exploit Multimodal Data to Design Unimodal Human Activity
Recognition with Few Labels
|
Human activity recognition (HAR) will be an essential function of various emerging applications. However, HAR typically encounters challenges related to modality limitations and label scarcity, leading to an application gap between current solutions and real-world requirements. In this work, we propose MESEN, a multimodal-empowered unimodal sensing framework, to utilize unlabeled multimodal data available during the HAR model design phase for unimodal HAR enhancement during the deployment phase. From a study on the impact of supervised multimodal fusion on unimodal feature extraction, MESEN is designed to feature a multi-task mechanism during the multimodal-aided pre-training stage. With the proposed mechanism integrating cross-modal feature contrastive learning and multimodal pseudo-classification aligning, MESEN exploits unlabeled multimodal data to extract effective unimodal features for each modality. Subsequently, MESEN can adapt to downstream unimodal HAR with only a few labeled samples. Extensive experiments on eight public multimodal datasets demonstrate that MESEN achieves significant performance improvements over state-of-the-art baselines in enhancing unimodal HAR by exploiting multimodal data.
|
[
"['Lilin Xu' 'Chaojie Gu' 'Rui Tan' 'Shibo He' 'Jiming Chen']"
] |
null | null |
2404.01959
| null | null |
http://arxiv.org/pdf/2404.01959v2
|
2024-04-07T05:26:08Z
|
2024-04-02T13:54:22Z
|
Bi-LORA: A Vision-Language Approach for Synthetic Image Detection
|
Advancements in deep image synthesis techniques, such as generative adversarial networks (GANs) and diffusion models (DMs), have ushered in an era of generating highly realistic images. While this technological progress has captured significant interest, it has also raised concerns about the potential difficulty in distinguishing real images from their synthetic counterparts. This paper takes inspiration from the potent convergence capabilities between vision and language, coupled with the zero-shot nature of vision-language models (VLMs). We introduce an innovative method called Bi-LORA that leverages VLMs, combined with low-rank adaptation (LORA) tuning techniques, to enhance the precision of synthetic image detection for unseen model-generated images. The pivotal conceptual shift in our methodology revolves around reframing binary classification as an image captioning task, leveraging the distinctive capabilities of cutting-edge VLM, notably bootstrapping language image pre-training (BLIP2). Rigorous and comprehensive experiments are conducted to validate the effectiveness of our proposed approach, particularly in detecting unseen diffusion-generated images from unknown diffusion-based generative models during training, showcasing robustness to noise, and demonstrating generalization capabilities to GANs. The obtained results showcase an impressive average accuracy of 93.41% in synthetic image detection on unseen generation models. The code and models associated with this research can be publicly accessed at https://github.com/Mamadou-Keita/VLM-DETECT.
|
[
"['Mamadou Keita' 'Wassim Hamidouche' 'Hessen Bougueffa Eutamene'\n 'Abdenour Hadid' 'Abdelmalik Taleb-Ahmed']"
] |
null | null |
2404.01964
| null | null |
http://arxiv.org/pdf/2404.01964v2
|
2024-07-08T11:00:51Z
|
2024-04-02T13:57:30Z
|
CAM-Based Methods Can See through Walls
|
CAM-based methods are widely-used post-hoc interpretability method that produce a saliency map to explain the decision of an image classification model. The saliency map highlights the important areas of the image relevant to the prediction. In this paper, we show that most of these methods can incorrectly attribute an important score to parts of the image that the model cannot see. We show that this phenomenon occurs both theoretically and experimentally. On the theory side, we analyze the behavior of GradCAM on a simple masked CNN model at initialization. Experimentally, we train a VGG-like model constrained to not use the lower part of the image and nevertheless observe positive scores in the unseen part of the image. This behavior is evaluated quantitatively on two new datasets. We believe that this is problematic, potentially leading to mis-interpretation of the model's behavior.
|
[
"['Magamed Taimeskhanov' 'Ronan Sicre' 'Damien Garreau']"
] |
null | null |
2404.01965
| null | null |
http://arxiv.org/pdf/2404.01965v2
|
2024-04-04T10:54:04Z
|
2024-04-02T14:03:37Z
|
Towards Leveraging AutoML for Sustainable Deep Learning: A
Multi-Objective HPO Approach on Deep Shift Neural Networks
|
Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets. However, the computational demands of DL models pose environmental and resource challenges. Deep shift neural networks (DSNNs) offer a solution by leveraging shift operations to reduce computational complexity at inference. Following the insights from standard DNNs, we are interested in leveraging the full potential of DSNNs by means of AutoML techniques. We study the impact of hyperparameter optimization (HPO) to maximize DSNN performance while minimizing resource consumption. Since this combines multi-objective (MO) optimization with accuracy and energy consumption as potentially complementary objectives, we propose to combine state-of-the-art multi-fidelity (MF) HPO with multi-objective optimization. Experimental results demonstrate the effectiveness of our approach, resulting in models with over 80% in accuracy and low computational cost. Overall, our method accelerates efficient model development while enabling sustainable AI applications.
|
[
"['Leona Hennig' 'Tanja Tornede' 'Marius Lindauer']"
] |
null | null |
2404.01975
| null | null |
http://arxiv.org/pdf/2404.01975v1
|
2024-04-02T14:16:57Z
|
2024-04-02T14:16:57Z
|
DSGNN: A Dual-View Supergrid-Aware Graph Neural Network for Regional Air
Quality Estimation
|
Air quality estimation can provide air quality for target regions without air quality stations, which is useful for the public. Existing air quality estimation methods divide the study area into disjointed grid regions, and apply 2D convolution to model the spatial dependencies of adjacent grid regions based on the first law of geography, failing to model the spatial dependencies of distant grid regions. To this end, we propose a Dual-view Supergrid-aware Graph Neural Network (DSGNN) for regional air quality estimation, which can model the spatial dependencies of distant grid regions from dual views (i.e., satellite-derived aerosol optical depth (AOD) and meteorology). Specifically, images are utilized to represent the regional data (i.e., AOD data and meteorology data). The dual-view supergrid learning module is introduced to generate supergrids in a parameterized way. Based on the dual-view supergrids, the dual-view implicit correlation encoding module is introduced to learn the correlations between pairwise supergrids. In addition, the dual-view message passing network is introduced to implement the information interaction on the supergrid graphs and images. Extensive experiments on two real-world datasets demonstrate that DSGNN achieves the state-of-the-art performances on the air quality estimation task, outperforming the best baseline by an average of 19.64% in MAE.
|
[
"['Xin Zhang' 'Ling Chen' 'Xing Tang' 'Hongyu Shi']"
] |
null | null |
2404.01976
| null | null |
http://arxiv.org/pdf/2404.01976v1
|
2024-04-02T14:16:59Z
|
2024-04-02T14:16:59Z
|
Joint-Task Regularization for Partially Labeled Multi-Task Learning
|
Multi-task learning has become increasingly popular in the machine learning field, but its practicality is hindered by the need for large, labeled datasets. Most multi-task learning methods depend on fully labeled datasets wherein each input example is accompanied by ground-truth labels for all target tasks. Unfortunately, curating such datasets can be prohibitively expensive and impractical, especially for dense prediction tasks which require per-pixel labels for each image. With this in mind, we propose Joint-Task Regularization (JTR), an intuitive technique which leverages cross-task relations to simultaneously regularize all tasks in a single joint-task latent space to improve learning when data is not fully labeled for all tasks. JTR stands out from existing approaches in that it regularizes all tasks jointly rather than separately in pairs -- therefore, it achieves linear complexity relative to the number of tasks while previous methods scale quadratically. To demonstrate the validity of our approach, we extensively benchmark our method across a wide variety of partially labeled scenarios based on NYU-v2, Cityscapes, and Taskonomy.
|
[
"['Kento Nishi' 'Junsik Kim' 'Wanhua Li' 'Hanspeter Pfister']"
] |
null | null |
2404.01981
| null | null |
http://arxiv.org/pdf/2404.01981v2
|
2024-04-05T20:53:20Z
|
2024-04-02T14:19:30Z
|
Zero-Shot Multi-Lingual Speaker Verification in Clinical Trials
|
Due to the substantial number of clinicians, patients, and data collection environments involved in clinical trials, gathering data of superior quality poses a significant challenge. In clinical trials, patients are assessed based on their speech data to detect and monitor cognitive and mental health disorders. We propose using these speech recordings to verify the identities of enrolled patients and identify and exclude the individuals who try to enroll multiple times in the same trial. Since clinical studies are often conducted across different countries, creating a system that can perform speaker verification in diverse languages without additional development effort is imperative. We evaluate pre-trained TitaNet, ECAPA-TDNN, and SpeakerNet models by enrolling and testing with speech-impaired patients speaking English, German, Danish, Spanish, and Arabic languages. Our results demonstrate that tested models can effectively generalize to clinical speakers, with less than 2.7% EER for European Languages and 8.26% EER for Arabic. This represents a significant step in developing more versatile and efficient speaker verification systems for cognitive and mental health clinical trials that can be used across a wide range of languages and dialects, substantially reducing the effort required to develop speaker verification systems for multiple languages. We also evaluate how speech tasks and number of speakers involved in the trial influence the performance and show that the type of speech tasks impacts the model performance.
|
[
"['Ali Akram' 'Marija Stanojevic' 'Malikeh Ehghaghi' 'Jekaterina Novikova']"
] |
null | null |
2404.01986
| null | null |
http://arxiv.org/pdf/2404.01986v1
|
2024-04-02T14:22:54Z
|
2024-04-02T14:22:54Z
|
Predicting the Intention to Interact with a Service Robot:the Role of
Gaze Cues
|
For a service robot, it is crucial to perceive as early as possible that an approaching person intends to interact: in this case, it can proactively enact friendly behaviors that lead to an improved user experience. We solve this perception task with a sequence-to-sequence classifier of a potential user intention to interact, which can be trained in a self-supervised way. Our main contribution is a study of the benefit of features representing the person's gaze in this context. Extensive experiments on a novel dataset show that the inclusion of gaze cues significantly improves the classifier performance (AUROC increases from 84.5% to 91.2%); the distance at which an accurate classification can be achieved improves from 2.4 m to 3.2 m. We also quantify the system's ability to adapt to new environments without external supervision. Qualitative experiments show practical applications with a waiter robot.
|
[
"['Simone Arreghini' 'Gabriele Abbate' 'Alessandro Giusti'\n 'Antonio Paolillo']"
] |
null | null |
2404.01994
| null | null |
http://arxiv.org/pdf/2404.01994v1
|
2024-04-02T14:40:04Z
|
2024-04-02T14:40:04Z
|
DELAN: Dual-Level Alignment for Vision-and-Language Navigation by
Cross-Modal Contrastive Learning
|
Vision-and-Language navigation (VLN) requires an agent to navigate in unseen environment by following natural language instruction. For task completion, the agent needs to align and integrate various navigation modalities, including instruction, observation and navigation history. Existing works primarily concentrate on cross-modal attention at the fusion stage to achieve this objective. Nevertheless, modality features generated by disparate uni-encoders reside in their own spaces, leading to a decline in the quality of cross-modal fusion and decision. To address this problem, we propose a Dual-levEL AligNment (DELAN) framework by cross-modal contrastive learning. This framework is designed to align various navigation-related modalities before fusion, thereby enhancing cross-modal interaction and action decision-making. Specifically, we divide the pre-fusion alignment into dual levels: instruction-history level and landmark-observation level according to their semantic correlations. We also reconstruct a dual-level instruction for adaptation to the dual-level alignment. As the training signals for pre-fusion alignment are extremely limited, self-supervised contrastive learning strategies are employed to enforce the matching between different modalities. Our approach seamlessly integrates with the majority of existing models, resulting in improved navigation performance on various VLN benchmarks, including R2R, R4R, RxR and CVDN.
|
[
"['Mengfei Du' 'Binhao Wu' 'Jiwen Zhang' 'Zhihao Fan' 'Zejun Li'\n 'Ruipu Luo' 'Xuanjing Huang' 'Zhongyu Wei']"
] |
null | null |
2404.01998
| null | null |
http://arxiv.org/pdf/2404.01998v1
|
2024-04-02T14:41:42Z
|
2024-04-02T14:41:42Z
|
Specularity Factorization for Low-Light Enhancement
|
We present a new additive image factorization technique that treats images to be composed of multiple latent specular components which can be simply estimated recursively by modulating the sparsity during decomposition. Our model-driven {em RSFNet} estimates these factors by unrolling the optimization into network layers requiring only a few scalars to be learned. The resultant factors are interpretable by design and can be fused for different image enhancement tasks via a network or combined directly by the user in a controllable fashion. Based on RSFNet, we detail a zero-reference Low Light Enhancement (LLE) application trained without paired or unpaired supervision. Our system improves the state-of-the-art performance on standard benchmarks and achieves better generalization on multiple other datasets. We also integrate our factors with other task specific fusion networks for applications like deraining, deblurring and dehazing with negligible overhead thereby highlighting the multi-domain and multi-task generalizability of our proposed RSFNet. The code and data is released for reproducibility on the project homepage.
|
[
"['Saurabh Saini' 'P J Narayanan']"
] |
null | null |
2404.01999
| null | null |
http://arxiv.org/pdf/2404.01999v1
|
2024-04-02T14:42:52Z
|
2024-04-02T14:42:52Z
|
Emergence of Chemotactic Strategies with Multi-Agent Reinforcement
Learning
|
Reinforcement learning (RL) is a flexible and efficient method for programming micro-robots in complex environments. Here we investigate whether reinforcement learning can provide insights into biological systems when trained to perform chemotaxis. Namely, whether we can learn about how intelligent agents process given information in order to swim towards a target. We run simulations covering a range of agent shapes, sizes, and swim speeds to determine if the physical constraints on biological swimmers, namely Brownian motion, lead to regions where reinforcement learners' training fails. We find that the RL agents can perform chemotaxis as soon as it is physically possible and, in some cases, even before the active swimming overpowers the stochastic environment. We study the efficiency of the emergent policy and identify convergence in agent size and swim speeds. Finally, we study the strategy adopted by the reinforcement learning algorithm to explain how the agents perform their tasks. To this end, we identify three emerging dominant strategies and several rare approaches taken. These strategies, whilst producing almost identical trajectories in simulation, are distinct and give insight into the possible mechanisms behind which biological agents explore their environment and respond to changing conditions.
|
[
"['Samuel Tovey' 'Christoph Lohrmann' 'Christian Holm']"
] |
null | null |
2404.02000
| null | null |
http://arxiv.org/pdf/2404.02000v3
|
2024-04-22T09:18:44Z
|
2024-04-02T14:43:36Z
|
Africa-Centric Self-Supervised Pre-Training for Multilingual Speech
Representation in a Sub-Saharan Context
|
We present the first self-supervised multilingual speech model trained exclusively on African speech. The model learned from nearly 60 000 hours of unlabeled speech segments in 21 languages and dialects spoken in sub-Saharan Africa. On the SSA subset of the FLEURS-102 dataset, our approach based on a HuBERT$_{base}$ (0.09B) architecture shows competitive results, for ASR downstream task, compared to the w2v-bert-51 (0.6B) pre-trained model proposed in the FLEURS benchmark, while being more efficient by using 7x less data and 6x less parameters. Furthermore, in the context of a LID downstream task, our approach outperforms FLEURS baselines accuracy by over 22%.
|
[
"['Antoine Caubrière' 'Elodie Gauthier']"
] |
null | null |
2404.02003
| null | null |
http://arxiv.org/pdf/2404.02003v2
|
2024-04-03T12:05:27Z
|
2024-04-02T14:44:02Z
|
AUTODIFF: Autoregressive Diffusion Modeling for Structure-based Drug
Design
|
Structure-based drug design (SBDD), which aims to generate molecules that can bind tightly to the target protein, is an essential problem in drug discovery, and previous approaches have achieved initial success. However, most existing methods still suffer from invalid local structure or unrealistic conformation issues, which are mainly due to the poor leaning of bond angles or torsional angles. To alleviate these problems, we propose AUTODIFF, a diffusion-based fragment-wise autoregressive generation model. Specifically, we design a novel molecule assembly strategy named conformal motif that preserves the conformation of local structures of molecules first, then we encode the interaction of the protein-ligand complex with an SE(3)-equivariant convolutional network and generate molecules motif-by-motif with diffusion modeling. In addition, we also improve the evaluation framework of SBDD by constraining the molecular weights of the generated molecules in the same range, together with some new metrics, which make the evaluation more fair and practical. Extensive experiments on CrossDocked2020 demonstrate that our approach outperforms the existing models in generating realistic molecules with valid structures and conformations while maintaining high binding affinity.
|
[
"['Xinze Li' 'Penglei Wang' 'Tianfan Fu' 'Wenhao Gao' 'Chengtao Li'\n 'Leilei Shi' 'Junhong Liu']"
] |
null | null |
2404.02040
| null | null |
http://arxiv.org/pdf/2404.02040v1
|
2024-04-02T15:34:47Z
|
2024-04-02T15:34:47Z
|
Transformers as Transducers
|
We study the sequence-to-sequence mapping capacity of transformers by relating them to finite transducers, and find that they can express surprisingly large classes of transductions. We do so using variants of RASP, a programming language designed to help people "think like transformers," as an intermediate representation. We extend the existing Boolean variant B-RASP to sequence-to-sequence functions and show that it computes exactly the first-order rational functions (such as string rotation). Then, we introduce two new extensions. B-RASP[pos] enables calculations on positions (such as copying the first half of a string) and contains all first-order regular functions. S-RASP adds prefix sum, which enables additional arithmetic operations (such as squaring a string) and contains all first-order polyregular functions. Finally, we show that masked average-hard attention transformers can simulate S-RASP. A corollary of our results is a new proof that transformer decoders are Turing-complete.
|
[
"['Lena Strobl' 'Dana Angluin' 'David Chiang' 'Jonathan Rawski'\n 'Ashish Sabharwal']"
] |
null | null |
2404.02047
| null | null |
http://arxiv.org/pdf/2404.02047v1
|
2024-04-02T15:39:14Z
|
2024-04-02T15:39:14Z
|
Universal representations for financial transactional data: embracing
local, global, and external contexts
|
Effective processing of financial transactions is essential for banking data analysis. However, in this domain, most methods focus on specialized solutions to stand-alone problems instead of constructing universal representations suitable for many problems. We present a representation learning framework that addresses diverse business challenges. We also suggest novel generative models that account for data specifics, and a way to integrate external information into a client's representation, leveraging insights from other customers' actions. Finally, we offer a benchmark, describing representation quality globally, concerning the entire transaction history; locally, reflecting the client's current state; and dynamically, capturing representation evolution over time. Our generative approach demonstrates superior performance in local tasks, with an increase in ROC-AUC of up to 14% for the next MCC prediction task and up to 46% for downstream tasks from existing contrastive baselines. Incorporating external information improves the scores by an additional 20%.
|
[
"['Alexandra Bazarova' 'Maria Kovaleva' 'Ilya Kuleshov'\n 'Evgenia Romanenkova' 'Alexander Stepikin' 'Alexandr Yugay'\n 'Dzhambulat Mollaev' 'Ivan Kireev' 'Andrey Savchenko' 'Alexey Zaytsev']"
] |
null | null |
2404.02052
| null | null |
http://arxiv.org/pdf/2404.02052v1
|
2024-04-02T15:49:03Z
|
2024-04-02T15:49:03Z
|
Noise Masking Attacks and Defenses for Pretrained Speech Models
|
Speech models are often trained on sensitive data in order to improve model performance, leading to potential privacy leakage. Our work considers noise masking attacks, introduced by Amid et al. 2022, which attack automatic speech recognition (ASR) models by requesting a transcript of an utterance which is partially replaced with noise. They show that when a record has been seen at training time, the model will transcribe the noisy record with its memorized sensitive transcript. In our work, we extend these attacks beyond ASR models, to attack pretrained speech encoders. Our method fine-tunes the encoder to produce an ASR model, and then performs noise masking on this model, which we find recovers private information from the pretraining data, despite the model never having seen transcripts at pretraining time! We show how to improve the precision of these attacks and investigate a number of countermeasures to our attacks.
|
[
"['Matthew Jagielski' 'Om Thakkar' 'Lun Wang']"
] |
null | null |
2404.02058
| null | null |
http://arxiv.org/pdf/2404.02058v1
|
2024-04-02T15:57:32Z
|
2024-04-02T15:57:32Z
|
Generalizable, Fast, and Accurate DeepQSPR with fastprop Part 1:
Framework and Benchmarks
|
Quantitative Structure Property Relationship studies aim to define a mapping between molecular structure and arbitrary quantities of interest. This was historically accomplished via the development of descriptors which requires significant domain expertise and struggles to generalize. Thus the field has morphed into Molecular Property Prediction and been given over to learned representations which are highly generalizable. The paper introduces fastprop, a DeepQSPR framework which uses a cogent set of molecular level descriptors to meet and exceed the performance of learned representations on diverse datasets in dramatically less time. fastprop is freely available on github at github.com/JacksonBurns/fastprop.
|
[
"['Jackson Burns' 'William Green']"
] |
null | null |
2404.02062
| null | null |
http://arxiv.org/pdf/2404.02062v1
|
2024-04-02T16:01:18Z
|
2024-04-02T16:01:18Z
|
Digital Forgetting in Large Language Models: A Survey of Unlearning
Methods
|
The objective of digital forgetting is, given a model with undesirable knowledge or behavior, obtain a new model where the detected issues are no longer present. The motivations for forgetting include privacy protection, copyright protection, elimination of biases and discrimination, and prevention of harmful content generation. Effective digital forgetting has to be effective (meaning how well the new model has forgotten the undesired knowledge/behavior), retain the performance of the original model on the desirable tasks, and be scalable (in particular forgetting has to be more efficient than retraining from scratch on just the tasks/data to be retained). This survey focuses on forgetting in large language models (LLMs). We first provide background on LLMs, including their components, the types of LLMs, and their usual training pipeline. Second, we describe the motivations, types, and desired properties of digital forgetting. Third, we introduce the approaches to digital forgetting in LLMs, among which unlearning methodologies stand out as the state of the art. Fourth, we provide a detailed taxonomy of machine unlearning methods for LLMs, and we survey and compare current approaches. Fifth, we detail datasets, models and metrics used for the evaluation of forgetting, retaining and runtime. Sixth, we discuss challenges in the area. Finally, we provide some concluding remarks.
|
[
"['Alberto Blanco-Justicia' 'Najeeb Jebreel' 'Benet Manzanares'\n 'David Sánchez' 'Josep Domingo-Ferrer' 'Guillem Collell' 'Kuan Eeik Tan']"
] |
null | null |
2404.02067
| null | null |
http://arxiv.org/pdf/2404.02067v1
|
2024-04-02T16:07:50Z
|
2024-04-02T16:07:50Z
|
Red-Teaming Segment Anything Model
|
Foundation models have emerged as pivotal tools, tackling many complex tasks through pre-training on vast datasets and subsequent fine-tuning for specific applications. The Segment Anything Model is one of the first and most well-known foundation models for computer vision segmentation tasks. This work presents a multi-faceted red-teaming analysis that tests the Segment Anything Model against challenging tasks: (1) We analyze the impact of style transfer on segmentation masks, demonstrating that applying adverse weather conditions and raindrops to dashboard images of city roads significantly distorts generated masks. (2) We focus on assessing whether the model can be used for attacks on privacy, such as recognizing celebrities' faces, and show that the model possesses some undesired knowledge in this task. (3) Finally, we check how robust the model is to adversarial attacks on segmentation masks under text prompts. We not only show the effectiveness of popular white-box attacks and resistance to black-box attacks but also introduce a novel approach - Focused Iterative Gradient Attack (FIGA) that combines white-box approaches to construct an efficient attack resulting in a smaller number of modified pixels. All of our testing methods and analyses indicate a need for enhanced safety measures in foundation models for image segmentation.
|
[
"['Krzysztof Jankowski' 'Bartlomiej Sobieski' 'Mateusz Kwiatkowski'\n 'Jakub Szulc' 'Michal Janik' 'Hubert Baniecki' 'Przemyslaw Biecek']"
] |
null | null |
2404.02072
| null | null |
http://arxiv.org/pdf/2404.02072v5
|
2024-06-24T15:52:57Z
|
2024-04-02T16:20:02Z
|
EGTR: Extracting Graph from Transformer for Scene Graph Generation
|
Scene Graph Generation (SGG) is a challenging task of detecting objects and predicting relationships between objects. After DETR was developed, one-stage SGG models based on a one-stage object detector have been actively studied. However, complex modeling is used to predict the relationship between objects, and the inherent relationship between object queries learned in the multi-head self-attention of the object detector has been neglected. We propose a lightweight one-stage SGG model that extracts the relation graph from the various relationships learned in the multi-head self-attention layers of the DETR decoder. By fully utilizing the self-attention by-products, the relation graph can be extracted effectively with a shallow relation extraction head. Considering the dependency of the relation extraction task on the object detection task, we propose a novel relation smoothing technique that adjusts the relation label adaptively according to the quality of the detected objects. By the relation smoothing, the model is trained according to the continuous curriculum that focuses on object detection task at the beginning of training and performs multi-task learning as the object detection performance gradually improves. Furthermore, we propose a connectivity prediction task that predicts whether a relation exists between object pairs as an auxiliary task of the relation extraction. We demonstrate the effectiveness and efficiency of our method for the Visual Genome and Open Image V6 datasets. Our code is publicly available at https://github.com/naver-ai/egtr.
|
[
"['Jinbae Im' 'JeongYeon Nam' 'Nokyung Park' 'Hyungmin Lee'\n 'Seunghyun Park']"
] |
null | null |
2404.02078
| null | null |
http://arxiv.org/pdf/2404.02078v1
|
2024-04-02T16:25:30Z
|
2024-04-02T16:25:30Z
|
Advancing LLM Reasoning Generalists with Preference Trees
|
We introduce Eurus, a suite of large language models (LLMs) optimized for reasoning. Finetuned from Mistral-7B and CodeLlama-70B, Eurus models achieve state-of-the-art results among open-source models on a diverse set of benchmarks covering mathematics, code generation, and logical reasoning problems. Notably, Eurus-70B beats GPT-3.5 Turbo in reasoning through a comprehensive benchmarking across 12 tests covering five tasks, and achieves a 33.3% pass@1 accuracy on LeetCode and 32.6% on TheoremQA, two challenging benchmarks, substantially outperforming existing open-source models by margins more than 13.3%. The strong performance of Eurus can be primarily attributed to UltraInteract, our newly-curated large-scale, high-quality alignment dataset specifically designed for complex reasoning tasks. UltraInteract can be used in both supervised fine-tuning and preference learning. For each instruction, it includes a preference tree consisting of (1) reasoning chains with diverse planning strategies in a unified format, (2) multi-turn interaction trajectories with the environment and the critique, and (3) pairwise data to facilitate preference learning. UltraInteract allows us to conduct an in-depth exploration of preference learning for reasoning tasks. Our investigation reveals that some well-established preference learning algorithms may be less suitable for reasoning tasks compared to their effectiveness in general conversations. Inspired by this, we derive a novel reward modeling objective which, together with UltraInteract, leads to a strong reward model.
|
[
"['Lifan Yuan' 'Ganqu Cui' 'Hanbin Wang' 'Ning Ding' 'Xingyao Wang'\n 'Jia Deng' 'Boji Shan' 'Huimin Chen' 'Ruobing Xie' 'Yankai Lin'\n 'Zhenghao Liu' 'Bowen Zhou' 'Hao Peng' 'Zhiyuan Liu' 'Maosong Sun']"
] |
null | null |
2404.02108
| null | null |
http://arxiv.org/pdf/2404.02108v1
|
2024-04-02T17:08:23Z
|
2024-04-02T17:08:23Z
|
Variance-Reduced Policy Gradient Approaches for Infinite Horizon Average
Reward Markov Decision Processes
|
We present two Policy Gradient-based methods with general parameterization in the context of infinite horizon average reward Markov Decision Processes. The first approach employs Implicit Gradient Transport for variance reduction, ensuring an expected regret of the order $tilde{mathcal{O}}(T^{3/5})$. The second approach, rooted in Hessian-based techniques, ensures an expected regret of the order $tilde{mathcal{O}}(sqrt{T})$. These results significantly improve the state of the art of the problem, which achieves a regret of $tilde{mathcal{O}}(T^{3/4})$.
|
[
"['Swetha Ganesh' 'Washim Uddin Mondal' 'Vaneet Aggarwal']"
] |
null | null |
2404.02112
| null | null |
http://arxiv.org/pdf/2404.02112v1
|
2024-04-02T17:13:04Z
|
2024-04-02T17:13:04Z
|
ImageNot: A contrast with ImageNet preserves model rankings
|
We introduce ImageNot, a dataset designed to match the scale of ImageNet while differing drastically in other aspects. We show that key model architectures developed for ImageNet over the years rank identically when trained and evaluated on ImageNot to how they rank on ImageNet. This is true when training models from scratch or fine-tuning them. Moreover, the relative improvements of each model over earlier models strongly correlate in both datasets. We further give evidence that ImageNot has a similar utility as ImageNet for transfer learning purposes. Our work demonstrates a surprising degree of external validity in the relative performance of image classification models. This stands in contrast with absolute accuracy numbers that typically drop sharply even under small changes to a dataset.
|
[
"['Olawale Salaudeen' 'Moritz Hardt']"
] |
null | null |
2404.02113
| null | null |
http://arxiv.org/pdf/2404.02113v3
|
2024-05-25T21:22:21Z
|
2024-04-02T17:13:22Z
|
K-percent Evaluation for Lifelong RL
|
In continual or lifelong reinforcement learning, access to the environment should be limited. If we aspire to design algorithms that can run for long periods, continually adapting to new, unexpected situations, then we must be willing to deploy our agents without tuning their hyperparameters over the agent's entire lifetime. The standard practice in deep RL, and even continual RL, is to assume unfettered access to the deployment environment for the full lifetime of the agent. In this paper, we propose a new approach for evaluating lifelong RL agents where only k percent of the experiment data can be used for hyperparameter tuning. We then conduct an empirical study of DQN and SAC across a variety of continuing and non-stationary domains. We find agents generally perform poorly when restricted to k-percent tuning, whereas several algorithmic mitigations designed to maintain network plasticity perform surprisingly well.
|
[
"['Golnaz Mesbahi' 'Parham Mohammad Panahi' 'Olya Mastikhina'\n 'Martha White' 'Adam White']"
] |
null | null |
2404.02115
| null | null |
http://arxiv.org/pdf/2404.02115v1
|
2024-04-02T17:18:48Z
|
2024-04-02T17:18:48Z
|
GINopic: Topic Modeling with Graph Isomorphism Network
|
Topic modeling is a widely used approach for analyzing and exploring large document collections. Recent research efforts have incorporated pre-trained contextualized language models, such as BERT embeddings, into topic modeling. However, they often neglect the intrinsic informational value conveyed by mutual dependencies between words. In this study, we introduce GINopic, a topic modeling framework based on graph isomorphism networks to capture the correlation between words. By conducting intrinsic (quantitative as well as qualitative) and extrinsic evaluations on diverse benchmark datasets, we demonstrate the effectiveness of GINopic compared to existing topic models and highlight its potential for advancing topic modeling.
|
[
"['Suman Adhya' 'Debarshi Kumar Sanyal']"
] |
null | null |
2404.02127
| null | null |
http://arxiv.org/pdf/2404.02127v1
|
2024-04-02T17:33:34Z
|
2024-04-02T17:33:34Z
|
FLawN-T5: An Empirical Examination of Effective Instruction-Tuning Data
Mixtures for Legal Reasoning
|
Instruction tuning is an important step in making language models useful for direct user interaction. However, many legal tasks remain out of reach for most open LLMs and there do not yet exist any large scale instruction datasets for the domain. This critically limits research in this application area. In this work, we curate LawInstruct, a large legal instruction dataset, covering 17 jurisdictions, 24 languages and a total of 12M examples. We present evidence that domain-specific pretraining and instruction tuning improve performance on LegalBench, including improving Flan-T5 XL by 8 points or 16% over the baseline. However, the effect does not generalize across all tasks, training regimes, model sizes, and other factors. LawInstruct is a resource for accelerating the development of models with stronger information processing and decision making capabilities in the legal domain.
|
[
"['Joel Niklaus' 'Lucia Zheng' 'Arya D. McCarthy' 'Christopher Hahn'\n 'Brian M. Rosen' 'Peter Henderson' 'Daniel E. Ho' 'Garrett Honke'\n 'Percy Liang' 'Christopher Manning']"
] |
null | null |
2404.02138
| null | null |
http://arxiv.org/pdf/2404.02138v2
|
2024-04-16T07:28:05Z
|
2024-04-02T17:49:40Z
|
Topic-based Watermarks for LLM-Generated Text
|
Recent advancements of large language models (LLMs) have resulted in indistinguishable text outputs comparable to human-generated text. Watermarking algorithms are potential tools that offer a way to differentiate between LLM- and human-generated text by embedding detectable signatures within LLM-generated output. However, current watermarking schemes lack robustness against known attacks against watermarking algorithms. In addition, they are impractical considering an LLM generates tens of thousands of text outputs per day and the watermarking algorithm needs to memorize each output it generates for the detection to work. In this work, focusing on the limitations of current watermarking schemes, we propose the concept of a "topic-based watermarking algorithm" for LLMs. The proposed algorithm determines how to generate tokens for the watermarked LLM output based on extracted topics of an input prompt or the output of a non-watermarked LLM. Inspired from previous work, we propose using a pair of lists (that are generated based on the specified extracted topic(s)) that specify certain tokens to be included or excluded while generating the watermarked output of the LLM. Using the proposed watermarking algorithm, we show the practicality of a watermark detection algorithm. Furthermore, we discuss a wide range of attacks that can emerge against watermarking algorithms for LLMs and the benefit of the proposed watermarking scheme for the feasibility of modeling a potential attacker considering its benefit vs. loss.
|
[
"['Alexander Nemecek' 'Yuzhou Jiang' 'Erman Ayday']"
] |
null | null |
2404.02141
| null | null |
http://arxiv.org/pdf/2404.02141v2
|
2024-06-25T18:17:43Z
|
2024-04-02T17:53:28Z
|
Robustly estimating heterogeneity in factorial data using Rashomon
Partitions
|
Many statistical analyses, in both observational data and randomized control trials, ask: how does the outcome of interest vary with combinations of observable covariates? How do various drug combinations affect health outcomes, or how does technology adoption depend on incentives and demographics? Our goal is to partition this factorial space into "pools" of covariate combinations where the outcome differs across the pools (but not within a pool). Existing approaches (i) search for a single "optimal" partition under assumptions about the association between covariates or (ii) sample from the entire set of possible partitions. Both these approaches ignore the reality that, especially with correlation structure in covariates, many ways to partition the covariate space may be statistically indistinguishable, despite very different implications for policy or science. We develop an alternative perspective, called Rashomon Partition Sets (RPSs). Each item in the RPS partitions the space of covariates using a tree-like geometry. RPSs incorporate all partitions that have posterior values near the maximum a posteriori partition, even if they offer substantively different explanations, and do so using a prior that makes no assumptions about associations between covariates. This prior is the $ell_0$ prior, which we show is minimax optimal. Given the RPS we calculate the posterior of any measurable function of the feature effects vector on outcomes, conditional on being in the RPS. We also characterize approximation error relative to the entire posterior and provide bounds on the size of the RPS. Simulations demonstrate this framework allows for robust conclusions relative to conventional regularization techniques. We apply our method to three empirical settings: price effects on charitable giving, chromosomal structure (telomere length), and the introduction of microfinance.
|
[
"['Aparajithan Venkateswaran' 'Anirudh Sankar' 'Arun G. Chandrasekhar'\n 'Tyler H. McCormick']"
] |
null | null |
2404.02151
| null | null |
http://arxiv.org/pdf/2404.02151v2
|
2024-06-18T17:29:04Z
|
2024-04-02T17:58:27Z
|
Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks
|
We show that even the most recent safety-aligned LLMs are not robust to simple adaptive jailbreaking attacks. First, we demonstrate how to successfully leverage access to logprobs for jailbreaking: we initially design an adversarial prompt template (sometimes adapted to the target LLM), and then we apply random search on a suffix to maximize a target logprob (e.g., of the token ``Sure''), potentially with multiple restarts. In this way, we achieve nearly 100% attack success rate -- according to GPT-4 as a judge -- on Vicuna-13B, Mistral-7B, Phi-3-Mini, Nemotron-4-340B, Llama-2-Chat-7B/13B/70B, Llama-3-Instruct-8B, Gemma-7B, GPT-3.5, GPT-4, and R2D2 from HarmBench that was adversarially trained against the GCG attack. We also show how to jailbreak all Claude models -- that do not expose logprobs -- via either a transfer or prefilling attack with a 100% success rate. In addition, we show how to use random search on a restricted set of tokens for finding trojan strings in poisoned models -- a task that shares many similarities with jailbreaking -- which is the algorithm that brought us the first place in the SaTML'24 Trojan Detection Competition. The common theme behind these attacks is that adaptivity is crucial: different models are vulnerable to different prompting templates (e.g., R2D2 is very sensitive to in-context learning prompts), some models have unique vulnerabilities based on their APIs (e.g., prefilling for Claude), and in some settings, it is crucial to restrict the token search space based on prior knowledge (e.g., for trojan detection). For reproducibility purposes, we provide the code, logs, and jailbreak artifacts in the JailbreakBench format at https://github.com/tml-epfl/llm-adaptive-attacks.
|
[
"['Maksym Andriushchenko' 'Francesco Croce' 'Nicolas Flammarion']"
] |
null | null |
2404.02171
| null | null |
http://arxiv.org/pdf/2404.02171v1
|
2024-03-29T17:01:09Z
|
2024-03-29T17:01:09Z
|
Path planning of magnetic microswimmers in high-fidelity simulations of
capillaries with deep reinforcement learning
|
Biomedical applications such as targeted drug delivery, microsurgery or sensing rely on reaching precise areas within the body in a minimally invasive way. Artificial bacterial flagella (ABFs) have emerged as potential tools for this task by navigating through the circulatory system. While the control and swimming characteristics of ABFs is understood in simple scenarios, their behavior within the bloodstream remains unclear. We conduct simulations of ABFs evolving in the complex capillary networks found in the human retina. The ABF is robustly guided to a prescribed target by a reinforcement learning agent previously trained on a reduced order model.
|
[
"['Lucas Amoudruz' 'Sergey Litvinov' 'Petros Koumoutsakos']"
] |
null | null |
2404.02175
| null | null |
http://arxiv.org/pdf/2404.02175v1
|
2024-04-01T11:23:31Z
|
2024-04-01T11:23:31Z
|
Social Dynamics of Consumer Response: A Unified Framework Integrating
Statistical Physics and Marketing Dynamics
|
Comprehending how consumers react to advertising inputs is essential for marketers aiming to optimize advertising strategies and improve campaign effectiveness. This study examines the complex nature of consumer behaviour by applying theoretical frameworks derived from physics and social psychology. We present an innovative equation that captures the relation between spending on advertising and consumer response, using concepts such as symmetries, scaling laws, and phase transitions. By validating our equation against well-known models such as the Michaelis-Menten and Hill equations, we prove its effectiveness in accurately representing the complexity of consumer response dynamics. The analysis emphasizes the importance of key model parameters, such as marketing effectiveness, response sensitivity, and behavioural sensitivity, in influencing consumer behaviour. The work explores the practical implications for advertisers and marketers, as well as discussing the limitations and future research directions. In summary, this study provides a thorough framework for comprehending and forecasting consumer reactions to advertising, which has implications for optimizing advertising strategies and allocating resources.
|
[
"['Javier Marin']"
] |
null | null |
2404.02177
| null | null |
http://arxiv.org/pdf/2404.02177v1
|
2024-04-01T20:55:03Z
|
2024-04-01T20:55:03Z
|
Exploring Quantum-Enhanced Machine Learning for Computer Vision:
Applications and Insights on Noisy Intermediate-Scale Quantum Devices
|
As medium-scale quantum computers progress, the application of quantum algorithms across diverse fields like simulating physical systems, chemistry, optimization, and cryptography becomes more prevalent. However, these quantum computers, known as Noisy Intermediate Scale Quantum (NISQ), are susceptible to noise, prompting the search for applications that can capitalize on quantum advantage without extensive error correction procedures. Since, Machine Learning (ML), particularly Deep Learning (DL), faces challenges due to resource-intensive training and algorithmic opacity. Therefore, this study explores the intersection of quantum computing and ML, focusing on computer vision tasks. Specifically, it evaluates the effectiveness of hybrid quantum-classical algorithms, such as the data re-uploading scheme and the patch Generative Adversarial Networks (GAN) model, on small-scale quantum devices. Through practical implementation and testing, the study reveals comparable or superior performance of these algorithms compared to classical counterparts, highlighting the potential of leveraging quantum algorithms in ML tasks.
|
[
"['Purnachandra Mandadapu']"
] |
null | null |
2404.02180
| null | null |
http://arxiv.org/pdf/2404.02180v3
|
2024-07-02T05:52:15Z
|
2024-04-02T09:15:32Z
|
Remote sensing framework for geological mapping via stacked autoencoders
and clustering
|
Supervised machine learning methods for geological mapping via remote sensing face limitations due to the scarcity of accurately labelled training data that can be addressed by unsupervised learning, such as dimensionality reduction and clustering. Dimensionality reduction methods have the potential to play a crucial role in improving the accuracy of geological maps. Although conventional dimensionality reduction methods may struggle with nonlinear data, unsupervised deep learning models such as autoencoders can model non-linear relationships. Stacked autoencoders feature multiple interconnected layers to capture hierarchical data representations useful for remote sensing data. This study presents an unsupervised machine learning-based framework for processing remote sensing data using stacked autoencoders for dimensionality reduction and k-means clustering for mapping geological units. We use Landsat 8, ASTER, and Sentinel-2 datasets to evaluate the framework for geological mapping of the Mutawintji region in Western New South Wales, Australia. We also compare stacked autoencoders with principal component analysis and canonical autoencoders. Our results reveal that the framework produces accurate and interpretable geological maps, efficiently discriminating rock units. We find that the accuracy of stacked autoencoders ranges from 86.6 % to 90 %, depending on the remote sensing data type, which is superior to their counterparts. We also find that the generated maps align with prior geological knowledge of the study area while providing novel insights into geological structures.
|
[
"['Sandeep Nagar' 'Ehsan Farahbakhsh' 'Joseph Awange' 'Rohitash Chandra']"
] |
null | null |
2404.02181
| null | null |
http://arxiv.org/pdf/2404.02181v1
|
2024-04-02T12:44:51Z
|
2024-04-02T12:44:51Z
|
Leveraging Machine Learning for Early Autism Detection via INDT-ASD
Indian Database
|
Machine learning (ML) has advanced quickly, particularly throughout the area of health care. The diagnosis of neurodevelopment problems using ML is a very important area of healthcare. Autism spectrum disorder (ASD) is one of the developmental disorders that is growing the fastest globally. The clinical screening tests used to identify autistic symptoms are expensive and time-consuming. But now that ML has been advanced, it's feasible to identify autism early on. Previously, many different techniques have been used in investigations. Still, none of them have produced the anticipated outcomes when it comes to the capacity to predict autistic features utilizing a clinically validated Indian ASD database. Therefore, this study aimed to develop a simple, quick, and inexpensive technique for identifying ASD by using ML. Various machine learning classifiers, including Adaboost (AB), Gradient Boost (GB), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Gaussian Naive Bayes (GNB), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), were used to develop the autism prediction model. The proposed method was tested with records from the AIIMS Modified INDT-ASD (AMI) database, which were collected through an application developed by AIIMS in Delhi, India. Feature engineering has been applied to make the proposed solution easier than already available solutions. Using the proposed model, we succeeded in predicting ASD using a minimized set of 20 questions rather than the 28 questions presented in AMI with promising accuracy. In a comparative evaluation, SVM emerged as the superior model among others, with 100 $pm$ 0.05% accuracy, higher recall by 5.34%, and improved accuracy by 2.22%-6.67% over RF. We have also introduced a web-based solution supporting both Hindi and English.
|
[
"['Trapti Shrivastava' 'Harshal Chaudhari' 'Vrijendra Singh']"
] |
null | null |
2404.02183
| null | null |
http://arxiv.org/pdf/2404.02183v1
|
2024-04-02T13:37:28Z
|
2024-04-02T13:37:28Z
|
Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra
Large-Scale Code Generation and Optimization
|
Recent advancements in automatic code generation using large language model (LLM) agent have brought us closer to the future of automated software development. However, existing single-agent approaches face limitations in generating and improving large-scale, complex codebases due to constraints in context length. To tackle this challenge, we propose Self-Organized multi-Agent framework (SoA), a novel multi-agent framework that enables the scalable and efficient generation and optimization of large-scale code. In SoA, self-organized agents operate independently to generate and modify code components while seamlessly collaborating to construct the overall codebase. A key feature of our framework is the automatic multiplication of agents based on problem complexity, allowing for dynamic scalability. This enables the overall code volume to be increased indefinitely according to the number of agents, while the amount of code managed by each agent remains constant. We evaluate SoA on the HumanEval benchmark and demonstrate that, compared to a single-agent system, each agent in SoA handles significantly less code, yet the overall generated code is substantially greater. Moreover, SoA surpasses the powerful single-agent baseline by 5% in terms of Pass@1 accuracy.
|
[
"['Yoichi Ishibashi' 'Yoshimasa Nishimura']"
] |
null | null |
2404.02184
| null | null |
http://arxiv.org/abs/2404.02184v1
|
2024-04-02T15:28:59Z
|
2024-04-02T15:28:59Z
|
What is to be gained by ensemble models in analysis of spectroscopic
data?
|
An empirical study was carried out to compare different implementations of ensemble models aimed at improving prediction in spectroscopic data. A wide range of candidate models were fitted to benchmark datasets from regression and classification settings. A statistical analysis using linear mixed model was carried out on prediction performance criteria resulting from model fits over random splits of the data. The results showed that the ensemble classifiers were able to consistently outperform candidate models in our application
|
[
"['Katarina Domijan']"
] |
null | null |
2404.02187
| null | null |
http://arxiv.org/pdf/2404.02187v1
|
2024-04-02T16:07:27Z
|
2024-04-02T16:07:27Z
|
A Generative Deep Learning Approach for Crash Severity Modeling with
Imbalanced Data
|
Crash data is often greatly imbalanced, with the majority of crashes being non-fatal crashes, and only a small number being fatal crashes due to their rarity. Such data imbalance issue poses a challenge for crash severity modeling since it struggles to fit and interpret fatal crash outcomes with very limited samples. Usually, such data imbalance issues are addressed by data resampling methods, such as under-sampling and over-sampling techniques. However, most traditional and deep learning-based data resampling methods, such as synthetic minority oversampling technique (SMOTE) and generative Adversarial Networks (GAN) are designed dedicated to processing continuous variables. Though some resampling methods have improved to handle both continuous and discrete variables, they may have difficulties in dealing with the collapse issue associated with sparse discrete risk factors. Moreover, there is a lack of comprehensive studies that compare the performance of various resampling methods in crash severity modeling. To address the aforementioned issues, the current study proposes a crash data generation method based on the Conditional Tabular GAN. After data balancing, a crash severity model is employed to estimate the performance of classification and interpretation. A comparative study is conducted to assess classification accuracy and distribution consistency of the proposed generation method using a 4-year imbalanced crash dataset collected in Washington State, U.S. Additionally, Monte Carlo simulation is employed to estimate the performance of parameter and probability estimation in both two- and three-class imbalance scenarios. The results indicate that using synthetic data generated by CTGAN-RU for crash severity modeling outperforms using original data or synthetic data generated by other resampling methods.
|
[
"['Junlan Chen' 'Ziyuan Pu' 'Nan Zheng' 'Xiao Wen' 'Hongliang Ding'\n 'Xiucheng Guo']"
] |
null | null |
2404.02189
| null | null |
http://arxiv.org/pdf/2404.02189v1
|
2024-04-02T16:48:34Z
|
2024-04-02T16:48:34Z
|
Insights from the Use of Previously Unseen Neural Architecture Search
Datasets
|
The boundless possibility of neural networks which can be used to solve a problem -- each with different performance -- leads to a situation where a Deep Learning expert is required to identify the best neural network. This goes against the hope of removing the need for experts. Neural Architecture Search (NAS) offers a solution to this by automatically identifying the best architecture. However, to date, NAS work has focused on a small set of datasets which we argue are not representative of real-world problems. We introduce eight new datasets created for a series of NAS Challenges: AddNIST, Language, MultNIST, CIFARTile, Gutenberg, Isabella, GeoClassing, and Chesseract. These datasets and challenges are developed to direct attention to issues in NAS development and to encourage authors to consider how their models will perform on datasets unknown to them at development time. We present experimentation using standard Deep Learning methods as well as the best results from challenge participants.
|
[
"['Rob Geada' 'David Towers' 'Matthew Forshaw' 'Amir Atapour-Abarghouei'\n 'A. Stephen McGough']"
] |
null | null |
2404.02204
| null | null |
http://arxiv.org/pdf/2404.02204v1
|
2024-04-02T18:00:28Z
|
2024-04-02T18:00:28Z
|
Emergent Abilities in Reduced-Scale Generative Language Models
|
Large language models can solve new tasks without task-specific fine-tuning. This ability, also known as in-context learning (ICL), is considered an emergent ability and is primarily seen in large language models with billions of parameters. This study investigates if such emergent properties are strictly tied to model size or can be demonstrated by smaller models trained on reduced-scale data. To explore this, we simplify pre-training data and pre-train 36 causal language models with parameters varying from 1 million to 165 million parameters. We show that models trained on this simplified pre-training data demonstrate enhanced zero-shot capabilities across various tasks in simplified language, achieving performance comparable to that of pre-trained models six times larger on unrestricted language. This suggests that downscaling the language allows zero-shot learning capabilities to emerge in models with limited size. Additionally, we find that these smaller models pre-trained on simplified data demonstrate a power law relationship between the evaluation loss and the three scaling factors: compute, dataset size, and model size.
|
[
"['Sherin Muckatira' 'Vijeta Deshpande' 'Vladislav Lialin' 'Anna Rumshisky']"
] |
null | null |
2404.02227
| null | null |
http://arxiv.org/pdf/2404.02227v1
|
2024-04-02T18:30:29Z
|
2024-04-02T18:30:29Z
|
OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning
Denoising
|
Trajectory prediction is fundamental in computer vision and autonomous driving, particularly for understanding pedestrian behavior and enabling proactive decision-making. Existing approaches in this field often assume precise and complete observational data, neglecting the challenges associated with out-of-view objects and the noise inherent in sensor data due to limited camera range, physical obstructions, and the absence of ground truth for denoised sensor data. Such oversights are critical safety concerns, as they can result in missing essential, non-visible objects. To bridge this gap, we present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique. Our approach denoises noisy sensor observations in an unsupervised manner and precisely maps sensor-based trajectories of out-of-sight objects into visual trajectories. This method has demonstrated state-of-the-art performance in out-of-sight noisy sensor trajectory denoising and prediction on the Vi-Fi and JRDB datasets. By enhancing trajectory prediction accuracy and addressing the challenges of out-of-sight objects, our work significantly contributes to improving the safety and reliability of autonomous driving in complex environments. Our work represents the first initiative towards Out-Of-Sight Trajectory prediction (OOSTraj), setting a new benchmark for future research. The code is available at url{https://github.com/Hai-chao-Zhang/OOSTraj}.
|
[
"['Haichao Zhang' 'Yi Xu' 'Hongsheng Lu' 'Takayuki Shimizu' 'Yun Fu']"
] |
null | null |
2404.02234
| null | null |
http://arxiv.org/pdf/2404.02234v1
|
2024-04-02T18:44:53Z
|
2024-04-02T18:44:53Z
|
Deep Neural Networks with 3D Point Clouds for Empirical Friction
Measurements in Hydrodynamic Flood Models
|
Friction is one of the cruxes of hydrodynamic modeling; flood conditions are highly sensitive to the Friction Factors (FFs) used to calculate momentum losses. However, empirical FFs are challenging to measure because they require laboratory experiments. Flood models often rely on surrogate observations (such as land use) to estimate FFs, introducing uncertainty. This research presents a laboratory-trained Deep Neural Network (DNN), trained using flume experiments with data augmentation techniques, to measure Manning's n based on Point Cloud data. The DNN was deployed on real-world lidar Point Clouds to directly measure Manning's n under regulatory and extreme storm events, showing improved prediction capabilities in both 1D and 2D hydrodynamic models. For 1D models, the lidar values decreased differences with regulatory models for in-channel water depth when compared to land cover values. For 1D/2D coupled models, the lidar values produced better agreement with flood extents measured from airborne imagery, while better matching flood insurance claim data for Hurricane Harvey. In both 1D and 1D/2D coupled models, lidar resulted in better agreement with validation gauges. For these reasons, the lidar measurements of Manning's n were found to improve both regulatory models and forecasts for extreme storm events, while simultaneously providing a pathway to standardize the measurement of FFs. Changing FFs significantly affected fluvial and pluvial flood models, while surge flooding was generally unaffected. Downstream flow conditions were found to change the importance of FFs to fluvial models, advancing the literature of friction in flood models. This research introduces a reliable, repeatable, and readily-accessible avenue to measure high-resolution FFs based on 3D point clouds, improving flood prediction, and removing uncertainty from hydrodynamic modeling.
|
[
"['Francisco Haces-Garcia' 'Vasileios Kotzamanis' 'Craig Glennie'\n 'Hanadi Rifai']"
] |
null | null |
2404.02235
| null | null |
http://arxiv.org/pdf/2404.02235v1
|
2024-04-02T18:45:01Z
|
2024-04-02T18:45:01Z
|
Is Exploration All You Need? Effective Exploration Characteristics for
Transfer in Reinforcement Learning
|
In deep reinforcement learning (RL) research, there has been a concerted effort to design more efficient and productive exploration methods while solving sparse-reward problems. These exploration methods often share common principles (e.g., improving diversity) and implementation details (e.g., intrinsic reward). Prior work found that non-stationary Markov decision processes (MDPs) require exploration to efficiently adapt to changes in the environment with online transfer learning. However, the relationship between specific exploration characteristics and effective transfer learning in deep RL has not been characterized. In this work, we seek to understand the relationships between salient exploration characteristics and improved performance and efficiency in transfer learning. We test eleven popular exploration algorithms on a variety of transfer types -- or ``novelties'' -- to identify the characteristics that positively affect online transfer learning. Our analysis shows that some characteristics correlate with improved performance and efficiency across a wide range of transfer tasks, while others only improve transfer performance with respect to specific environment changes. From our analysis, make recommendations about which exploration algorithm characteristics are best suited to specific transfer situations.
|
[
"['Jonathan C. Balloch' 'Rishav Bhagat' 'Geigh Zollicoffer' 'Ruoran Jia'\n 'Julia Kim' 'Mark O. Riedl']"
] |
null | null |
2404.02239
| null | null |
http://arxiv.org/pdf/2404.02239v1
|
2024-04-02T18:52:28Z
|
2024-04-02T18:52:28Z
|
Proximal Oracles for Optimization and Sampling
|
We consider convex optimization with non-smooth objective function and log-concave sampling with non-smooth potential (negative log density). In particular, we study two specific settings where the convex objective/potential function is either semi-smooth or in composite form as the finite sum of semi-smooth components. To overcome the challenges caused by non-smoothness, our algorithms employ two powerful proximal frameworks in optimization and sampling: the proximal point framework for optimization and the alternating sampling framework (ASF) that uses Gibbs sampling on an augmented distribution. A key component of both optimization and sampling algorithms is the efficient implementation of the proximal map by the regularized cutting-plane method. We establish the iteration-complexity of the proximal map in both semi-smooth and composite settings. We further propose an adaptive proximal bundle method for non-smooth optimization. The proposed method is universal since it does not need any problem parameters as input. Additionally, we develop a proximal sampling oracle that resembles the proximal map in optimization and establish its complexity using a novel technique (a modified Gaussian integral). Finally, we combine this proximal sampling oracle and ASF to obtain a Markov chain Monte Carlo method with non-asymptotic complexity bounds for sampling in semi-smooth and composite settings.
|
[
"['Jiaming Liang' 'Yongxin Chen']"
] |
null | null |
2404.02249
| null | null |
http://arxiv.org/abs/2404.02249v2
|
2024-04-05T02:13:30Z
|
2024-04-02T19:14:23Z
|
RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction
|
Predicting click-through rates (CTR) is a fundamental task for Web applications, where a key issue is to devise effective models for feature interactions. Current methodologies predominantly concentrate on modeling feature interactions within an individual sample, while overlooking the potential cross-sample relationships that can serve as a reference context to enhance the prediction. To make up for such deficiency, this paper develops a Retrieval-Augmented Transformer (RAT), aiming to acquire fine-grained feature interactions within and across samples. By retrieving similar samples, we construct augmented input for each target sample. We then build Transformer layers with cascaded attention to capture both intra- and cross-sample feature interactions, facilitating comprehensive reasoning for improved CTR prediction while retaining efficiency. Extensive experiments on real-world datasets substantiate the effectiveness of RAT and suggest its advantage in long-tail scenarios. The code has been open-sourced at url{https://github.com/YushenLi807/WWW24-RAT}.
|
[
"['Yushen Li' 'Jinpeng Wang' 'Tao Dai' 'Jieming Zhu' 'Jun Yuan' 'Rui Zhang'\n 'Shu-Tao Xia']"
] |
null | null |
2404.02254
| null | null |
http://arxiv.org/pdf/2404.02254v1
|
2024-04-02T19:21:28Z
|
2024-04-02T19:21:28Z
|
On Stronger Computational Separations Between Multimodal and Unimodal
Machine Learning
|
In multimodal machine learning, multiple modalities of data (e.g., text and images) are combined to facilitate the learning of a better machine learning model, which remains applicable to a corresponding unimodal task (e.g., text generation). Recently, multimodal machine learning has enjoyed huge empirical success (e.g. GPT-4). Motivated to develop theoretical justification for this empirical success, Lu (NeurIPS '23, ALT '24) introduces a theory of multimodal learning, and considers possible separations between theoretical models of multimodal and unimodal learning. In particular, Lu (ALT '24) shows a computational separation, which is relevant to worst-case instances of the learning task. In this paper, we give a stronger average-case computational separation, where for "typical" instances of the learning task, unimodal learning is computationally hard, but multimodal learning is easy. We then question how "organic" the average-case separation is. Would it be encountered in practice? To this end, we prove that under natural conditions, any given computational separation between average-case unimodal and multimodal learning tasks implies a corresponding cryptographic key agreement protocol. We suggest to interpret this as evidence that very strong computational advantages of multimodal learning may arise infrequently in practice, since they exist only for the "pathological" case of inherently cryptographic distributions. However, this does not apply to possible (super-polynomial) statistical advantages.
|
[
"['Ari Karchmer']"
] |
null | null |
2404.02258
| null | null |
http://arxiv.org/pdf/2404.02258v1
|
2024-04-02T19:28:11Z
|
2024-04-02T19:28:11Z
|
Mixture-of-Depths: Dynamically allocating compute in transformer-based
language models
|
Transformer-based language models spread FLOPs uniformly across input sequences. In this work we demonstrate that transformers can instead learn to dynamically allocate FLOPs (or compute) to specific positions in a sequence, optimising the allocation along the sequence for different layers across the model depth. Our method enforces a total compute budget by capping the number of tokens ($k$) that can participate in the self-attention and MLP computations at a given layer. The tokens to be processed are determined by the network using a top-$k$ routing mechanism. Since $k$ is defined a priori, this simple procedure uses a static computation graph with known tensor sizes, unlike other conditional computation techniques. Nevertheless, since the identities of the $k$ tokens are fluid, this method can expend FLOPs non-uniformly across the time and model depth dimensions. Thus, compute expenditure is entirely predictable in sum total, but dynamic and context-sensitive at the token-level. Not only do models trained in this way learn to dynamically allocate compute, they do so efficiently. These models match baseline performance for equivalent FLOPS and wall-clock times to train, but require a fraction of the FLOPs per forward pass, and can be upwards of 50% faster to step during post-training sampling.
|
[
"['David Raposo' 'Sam Ritter' 'Blake Richards' 'Timothy Lillicrap'\n 'Peter Conway Humphreys' 'Adam Santoro']"
] |
null | null |
2404.02261
| null | null |
http://arxiv.org/pdf/2404.02261v2
|
2024-06-23T18:21:01Z
|
2024-04-02T19:34:22Z
|
LLMs in the Loop: Leveraging Large Language Model Annotations for Active
Learning in Low-Resource Languages
|
Low-resource languages face significant barriers in AI development due to limited linguistic resources and expertise for data labeling, rendering them rare and costly. The scarcity of data and the absence of preexisting tools exacerbate these challenges, especially since these languages may not be adequately represented in various NLP datasets. To address this gap, we propose leveraging the potential of LLMs in the active learning loop for data annotation. Initially, we conduct evaluations to assess inter-annotator agreement and consistency, facilitating the selection of a suitable LLM annotator. The chosen annotator is then integrated into a training loop for a classifier using an active learning paradigm, minimizing the amount of queried data required. Empirical evaluations, notably employing GPT-4-Turbo, demonstrate near-state-of-the-art performance with significantly reduced data requirements, as indicated by estimated potential cost savings of at least 42.45 times compared to human annotation. Our proposed solution shows promising potential to substantially reduce both the monetary and computational costs associated with automation in low-resource settings. By bridging the gap between low-resource languages and AI, this approach fosters broader inclusion and shows the potential to enable automation across diverse linguistic landscapes.
|
[
"['Nataliia Kholodna' 'Sahib Julka' 'Mohammad Khodadadi'\n 'Muhammed Nurullah Gumus' 'Michael Granitzer']"
] |
null | null |
2404.02289
| null | null |
http://arxiv.org/pdf/2404.02289v1
|
2024-04-02T20:32:32Z
|
2024-04-02T20:32:32Z
|
Federated Multi-Agent Mapping for Planetary Exploration
|
In multi-agent robotic exploration, managing and effectively utilizing the vast, heterogeneous data generated from dynamic environments poses a significant challenge. Federated learning (FL) is a promising approach for distributed mapping, addressing the challenges of decentralized data in collaborative learning. FL enables joint model training across multiple agents without requiring the centralization or sharing of raw data, overcoming bandwidth and storage constraints. Our approach leverages implicit neural mapping, representing maps as continuous functions learned by neural networks, for compact and adaptable representations. We further enhance this approach with meta-initialization on Earth datasets, pre-training the network to quickly learn new map structures. This combination demonstrates strong generalization to diverse domains like Martian terrain and glaciers. We rigorously evaluate this approach, demonstrating its effectiveness for real-world deployment in multi-agent exploration scenarios.
|
[
"['Tiberiu-Ioan Szatmari' 'Abhishek Cauligi']"
] |
null | null |
2404.02294
| null | null |
http://arxiv.org/pdf/2404.02294v1
|
2024-04-02T20:46:13Z
|
2024-04-02T20:46:13Z
|
Constrained Robotic Navigation on Preferred Terrains Using LLMs and
Speech Instruction: Exploiting the Power of Adverbs
|
This paper explores leveraging large language models for map-free off-road navigation using generative AI, reducing the need for traditional data collection and annotation. We propose a method where a robot receives verbal instructions, converted to text through Whisper, and a large language model (LLM) model extracts landmarks, preferred terrains, and crucial adverbs translated into speed settings for constrained navigation. A language-driven semantic segmentation model generates text-based masks for identifying landmarks and terrain types in images. By translating 2D image points to the vehicle's motion plane using camera parameters, an MPC controller can guides the vehicle towards the desired terrain. This approach enhances adaptation to diverse environments and facilitates the use of high-level instructions for navigating complex and challenging terrains.
|
[
"['Faraz Lotfi' 'Farnoosh Faraji' 'Nikhil Kakodkar' 'Travis Manderson'\n 'David Meger' 'Gregory Dudek']"
] |
null | null |
2404.02300
| null | null |
http://arxiv.org/pdf/2404.02300v1
|
2024-04-02T20:55:39Z
|
2024-04-02T20:55:39Z
|
CATGNN: Cost-Efficient and Scalable Distributed Training for Graph
Neural Networks
|
Graph neural networks have been shown successful in recent years. While different GNN architectures and training systems have been developed, GNN training on large-scale real-world graphs still remains challenging. Existing distributed systems load the entire graph in memory for graph partitioning, requiring a huge memory space to process large graphs and thus hindering GNN training on such large graphs using commodity workstations. In this paper, we propose CATGNN, a cost-efficient and scalable distributed GNN training system which focuses on scaling GNN training to billion-scale or larger graphs under limited computational resources. Among other features, it takes a stream of edges as input, instead of loading the entire graph in memory, for partitioning. We also propose a novel streaming partitioning algorithm named SPRING for distributed GNN training. We verify the correctness and effectiveness of CATGNN with SPRING on 16 open datasets. In particular, we demonstrate that CATGNN can handle the largest publicly available dataset with limited memory, which would have been infeasible without increasing the memory space. SPRING also outperforms state-of-the-art partitioning algorithms significantly, with a 50% reduction in replication factor on average.
|
[
"['Xin Huang' 'Weipeng Zhuo' 'Minh Phu Vuong' 'Shiju Li' 'Jongryool Kim'\n 'Bradley Rees' 'Chul-Ho Lee']"
] |
null | null |
2404.02304
| null | null |
http://arxiv.org/pdf/2404.02304v1
|
2024-04-02T21:03:17Z
|
2024-04-02T21:03:17Z
|
Virtual Sensor for Real-Time Bearing Load Prediction Using Heterogeneous
Temporal Graph Neural Networks
|
Accurate bearing load monitoring is essential for their Prognostics and Health Management (PHM), enabling damage assessment, wear prediction, and proactive maintenance. While bearing sensors are typically placed on the bearing housing, direct load monitoring requires sensors inside the bearing itself. Recently introduced sensor rollers enable direct bearing load monitoring but are constrained by their battery life. Data-driven virtual sensors can learn from sensor roller data collected during a batterys lifetime to map operating conditions to bearing loads. Although spatially distributed bearing sensors offer insights into load distribution (e.g., correlating temperature with load), traditional machine learning algorithms struggle to fully exploit these spatial-temporal dependencies. To address this gap, we introduce a graph-based virtual sensor that leverages Graph Neural Networks (GNNs) to analyze spatial-temporal dependencies among sensor signals, mapping existing measurements (temperature, vibration) to bearing loads. Since temperature and vibration signals exhibit vastly different dynamics, we propose Heterogeneous Temporal Graph Neural Networks (HTGNN), which explicitly models these signal types and their interactions for effective load prediction. Our results demonstrate that HTGNN outperforms Convolutional Neural Networks (CNNs), which struggle to capture both spatial and heterogeneous signal characteristics. These findings highlight the importance of capturing the complex spatial interactions between temperature, vibration, and load.
|
[
"['Mengjie Zhao' 'Cees Taal' 'Stephan Baggerohr' 'Olga Fink']"
] |
null | null |
2404.02314
| null | null |
http://arxiv.org/pdf/2404.02314v1
|
2024-04-02T21:20:51Z
|
2024-04-02T21:20:51Z
|
Is Meta-training Really Necessary for Molecular Few-Shot Learning ?
|
Few-shot learning has recently attracted significant interest in drug discovery, with a recent, fast-growing literature mostly involving convoluted meta-learning strategies. We revisit the more straightforward fine-tuning approach for molecular data, and propose a regularized quadratic-probe loss based on the the Mahalanobis distance. We design a dedicated block-coordinate descent optimizer, which avoid the degenerate solutions of our loss. Interestingly, our simple fine-tuning approach achieves highly competitive performances in comparison to state-of-the-art methods, while being applicable to black-box settings and removing the need for specific episodic pre-training strategies. Furthermore, we introduce a new benchmark to assess the robustness of the competing methods to domain shifts. In this setting, our fine-tuning baseline obtains consistently better results than meta-learning methods.
|
[
"['Philippe Formont' 'Hugo Jeannin' 'Pablo Piantanida' 'Ismail Ben Ayed']"
] |
null | null |
2404.02319
| null | null |
http://arxiv.org/pdf/2404.02319v2
|
2024-06-27T23:22:14Z
|
2024-04-02T21:35:54Z
|
Symbolic Prompt Program Search: A Structure-Aware Approach to Efficient
Compile-Time Prompt Optimization
|
In many modern LLM applications, such as retrieval augmented generation, prompts have become programs themselves. In these settings, prompt programs are repeatedly called with different user queries or data instances. A big practical challenge is optimizing such prompt programs. Recent work has mostly focused on either simple prompt programs or assumed that the general structure of a prompt program is fixed. We introduce SAMMO, a framework to perform symbolic prompt program search for compile-time optimizations of prompt programs. SAMMO represents prompt programs on a symbolic level which allows for a rich set of transformations that can be searched over during optimization. We show that SAMMO generalizes previous methods and improves the performance of complex prompts on (1) instruction tuning, (2) RAG pipeline tuning, and (3) prompt compression, across several different LLMs. We make all code available open-source at https://github.com/microsoft/sammo .
|
[
"['Tobias Schnabel' 'Jennifer Neville']"
] |
null | null |
2404.02325
| null | null |
http://arxiv.org/pdf/2404.02325v1
|
2024-04-02T21:51:39Z
|
2024-04-02T21:51:39Z
|
Heat Death of Generative Models in Closed-Loop Learning
|
Improvement and adoption of generative machine learning models is rapidly accelerating, as exemplified by the popularity of LLMs (Large Language Models) for text, and diffusion models for image generation.As generative models become widespread, data they generate is incorporated into shared content through the public web. This opens the question of what happens when data generated by a model is fed back to the model in subsequent training campaigns. This is a question about the stability of the training process, whether the distribution of publicly accessible content, which we refer to as "knowledge", remains stable or collapses. Small scale empirical experiments reported in the literature show that this closed-loop training process is prone to degenerating. Models may start producing gibberish data, or sample from only a small subset of the desired data distribution (a phenomenon referred to as mode collapse). So far there has been only limited theoretical understanding of this process, in part due to the complexity of the deep networks underlying these generative models. The aim of this paper is to provide insights into this process (that we refer to as "generative closed-loop learning") by studying the learning dynamics of generative models that are fed back their own produced content in addition to their original training dataset. The sampling of many of these models can be controlled via a "temperature" parameter. Using dynamical systems tools, we show that, unless a sufficient amount of external data is introduced at each iteration, any non-trivial temperature leads the model to asymptotically degenerate. In fact, either the generative distribution collapses to a small set of outputs, or becomes uniform over a large set of outputs.
|
[
"['Matteo Marchi' 'Stefano Soatto' 'Pratik Chaudhari' 'Paulo Tabuada']"
] |
null | null |
2404.02343
| null | null |
http://arxiv.org/pdf/2404.02343v1
|
2024-04-02T22:37:22Z
|
2024-04-02T22:37:22Z
|
Improved model-free bounds for multi-asset options using option-implied
information and deep learning
|
We consider the computation of model-free bounds for multi-asset options in a setting that combines dependence uncertainty with additional information on the dependence structure. More specifically, we consider the setting where the marginal distributions are known and partial information, in the form of known prices for multi-asset options, is also available in the market. We provide a fundamental theorem of asset pricing in this setting, as well as a superhedging duality that allows to transform the maximization problem over probability measures in a more tractable minimization problem over trading strategies. The latter is solved using a penalization approach combined with a deep learning approximation using artificial neural networks. The numerical method is fast and the computational time scales linearly with respect to the number of traded assets. We finally examine the significance of various pieces of additional information. Empirical evidence suggests that "relevant" information, i.e. prices of derivatives with the same payoff structure as the target payoff, are more useful that other information, and should be prioritized in view of the trade-off between accuracy and computational efficiency.
|
[
"['Evangelia Dragazi' 'Shuaiqiang Liu' 'Antonis Papapantoleon']"
] |
null | null |
2404.02353
| null | null |
http://arxiv.org/pdf/2404.02353v1
|
2024-04-02T22:54:24Z
|
2024-04-02T22:54:24Z
|
Semantic Augmentation in Images using Language
|
Deep Learning models are incredibly data-hungry and require very large labeled datasets for supervised learning. As a consequence, these models often suffer from overfitting, limiting their ability to generalize to real-world examples. Recent advancements in diffusion models have enabled the generation of photorealistic images based on textual inputs. Leveraging the substantial datasets used to train these diffusion models, we propose a technique to utilize generated images to augment existing datasets. This paper explores various strategies for effective data augmentation to improve the out-of-domain generalization capabilities of deep learning models.
|
[
"['Sahiti Yerramilli' 'Jayant Sravan Tamarapalli' 'Tanmay Girish Kulkarni'\n 'Jonathan Francis' 'Eric Nyberg']"
] |
null | null |
2404.02359
| null | null |
http://arxiv.org/pdf/2404.02359v1
|
2024-04-02T23:05:56Z
|
2024-04-02T23:05:56Z
|
Attribution Regularization for Multimodal Paradigms
|
Multimodal machine learning has gained significant attention in recent years due to its potential for integrating information from multiple modalities to enhance learning and decision-making processes. However, it is commonly observed that unimodal models outperform multimodal models, despite the latter having access to richer information. Additionally, the influence of a single modality often dominates the decision-making process, resulting in suboptimal performance. This research project aims to address these challenges by proposing a novel regularization term that encourages multimodal models to effectively utilize information from all modalities when making decisions. The focus of this project lies in the video-audio domain, although the proposed regularization technique holds promise for broader applications in embodied AI research, where multiple modalities are involved. By leveraging this regularization term, the proposed approach aims to mitigate the issue of unimodal dominance and improve the performance of multimodal machine learning systems. Through extensive experimentation and evaluation, the effectiveness and generalizability of the proposed technique will be assessed. The findings of this research project have the potential to significantly contribute to the advancement of multimodal machine learning and facilitate its application in various domains, including multimedia analysis, human-computer interaction, and embodied AI research.
|
[
"['Sahiti Yerramilli' 'Jayant Sravan Tamarapalli' 'Jonathan Francis'\n 'Eric Nyberg']"
] |
null | null |
2404.02360
| null | null |
http://arxiv.org/pdf/2404.02360v1
|
2024-04-02T23:16:15Z
|
2024-04-02T23:16:15Z
|
FraGNNet: A Deep Probabilistic Model for Mass Spectrum Prediction
|
The process of identifying a compound from its mass spectrum is a critical step in the analysis of complex mixtures. Typical solutions for the mass spectrum to compound (MS2C) problem involve matching the unknown spectrum against a library of known spectrum-molecule pairs, an approach that is limited by incomplete library coverage. Compound to mass spectrum (C2MS) models can improve retrieval rates by augmenting real libraries with predicted spectra. Unfortunately, many existing C2MS models suffer from problems with prediction resolution, scalability, or interpretability. We develop a new probabilistic method for C2MS prediction, FraGNNet, that can efficiently and accurately predict high-resolution spectra. FraGNNet uses a structured latent space to provide insight into the underlying processes that define the spectrum. Our model achieves state-of-the-art performance in terms of prediction error, and surpasses existing C2MS models as a tool for retrieval-based MS2C.
|
[
"['Adamo Young' 'Fei Wang' 'David Wishart' 'Bo Wang' 'Hannes Röst'\n 'Russ Greiner']"
] |
null | null |
2404.02364
| null | null |
http://arxiv.org/pdf/2404.02364v2
|
2024-05-20T18:25:35Z
|
2024-04-02T23:34:39Z
|
Learning Intersections of Halfspaces with Distribution Shift: Improved
Algorithms and SQ Lower Bounds
|
Recent work of Klivans, Stavropoulos, and Vasilyan initiated the study of testable learning with distribution shift (TDS learning), where a learner is given labeled samples from training distribution $mathcal{D}$, unlabeled samples from test distribution $mathcal{D}'$, and the goal is to output a classifier with low error on $mathcal{D}'$ whenever the training samples pass a corresponding test. Their model deviates from all prior work in that no assumptions are made on $mathcal{D}'$. Instead, the test must accept (with high probability) when the marginals of the training and test distributions are equal. Here we focus on the fundamental case of intersections of halfspaces with respect to Gaussian training distributions and prove a variety of new upper bounds including a $2^{(k/epsilon)^{O(1)}} mathsf{poly}(d)$-time algorithm for TDS learning intersections of $k$ homogeneous halfspaces to accuracy $epsilon$ (prior work achieved $d^{(k/epsilon)^{O(1)}}$). We work under the mild assumption that the Gaussian training distribution contains at least an $epsilon$ fraction of both positive and negative examples ($epsilon$-balanced). We also prove the first set of SQ lower-bounds for any TDS learning problem and show (1) the $epsilon$-balanced assumption is necessary for $mathsf{poly}(d,1/epsilon)$-time TDS learning for a single halfspace and (2) a $d^{tilde{Omega}(log 1/epsilon)}$ lower bound for the intersection of two general halfspaces, even with the $epsilon$-balanced assumption. Our techniques significantly expand the toolkit for TDS learning. We use dimension reduction and coverings to give efficient algorithms for computing a localized version of discrepancy distance, a key metric from the domain adaptation literature.
|
[
"['Adam R. Klivans' 'Konstantinos Stavropoulos' 'Arsen Vasilyan']"
] |
null | null |
2404.02372
| null | null |
http://arxiv.org/pdf/2404.02372v1
|
2024-04-03T00:13:23Z
|
2024-04-03T00:13:23Z
|
Obfuscated Malware Detection: Investigating Real-world Scenarios through
Memory Analysis
|
In the era of the internet and smart devices, the detection of malware has become crucial for system security. Malware authors increasingly employ obfuscation techniques to evade advanced security solutions, making it challenging to detect and eliminate threats. Obfuscated malware, adept at hiding itself, poses a significant risk to various platforms, including computers, mobile devices, and IoT devices. Conventional methods like heuristic-based or signature-based systems struggle against this type of malware, as it leaves no discernible traces on the system. In this research, we propose a simple and cost-effective obfuscated malware detection system through memory dump analysis, utilizing diverse machine-learning algorithms. The study focuses on the CIC-MalMem-2022 dataset, designed to simulate real-world scenarios and assess memory-based obfuscated malware detection. We evaluate the effectiveness of machine learning algorithms, such as decision trees, ensemble methods, and neural networks, in detecting obfuscated malware within memory dumps. Our analysis spans multiple malware categories, providing insights into algorithmic strengths and limitations. By offering a comprehensive assessment of machine learning algorithms for obfuscated malware detection through memory analysis, this paper contributes to ongoing efforts to enhance cybersecurity and fortify digital ecosystems against evolving and sophisticated malware threats. The source code is made open-access for reproducibility and future research endeavours. It can be accessed at https://bit.ly/MalMemCode.
|
[
"['S M Rakib Hasan' 'Aakar Dhakal']"
] |
null | null |
2404.02378
| null | null |
http://arxiv.org/pdf/2404.02378v1
|
2024-04-03T00:41:19Z
|
2024-04-03T00:41:19Z
|
Faster Convergence of Stochastic Accelerated Gradient Descent under
Interpolation
|
We prove new convergence rates for a generalized version of stochastic Nesterov acceleration under interpolation conditions. Unlike previous analyses, our approach accelerates any stochastic gradient method which makes sufficient progress in expectation. The proof, which proceeds using the estimating sequences framework, applies to both convex and strongly convex functions and is easily specialized to accelerated SGD under the strong growth condition. In this special case, our analysis reduces the dependence on the strong growth constant from $rho$ to $sqrt{rho}$ as compared to prior work. This improvement is comparable to a square-root of the condition number in the worst case and address criticism that guarantees for stochastic acceleration could be worse than those for SGD.
|
[
"['Aaron Mishkin' 'Mert Pilanci' 'Mark Schmidt']"
] |
null | null |
2404.02387
| null | null |
http://arxiv.org/pdf/2404.02387v1
|
2024-04-03T01:02:06Z
|
2024-04-03T01:02:06Z
|
An inversion problem for optical spectrum data via physics-guided
machine learning
|
We propose the regularized recurrent inference machine (rRIM), a novel machine-learning approach to solve the challenging problem of deriving the pairing glue function from measured optical spectra. The rRIM incorporates physical principles into both training and inference and affords noise robustness, flexibility with out-of-distribution data, and reduced data requirements. It effectively obtains reliable pairing glue functions from experimental optical spectra and yields promising solutions for similar inverse problems of the Fredholm integral equation of the first kind.
|
[
"['Hwiwoo Park' 'Jun H. Park' 'Jungseek Hwang']"
] |
null | null |
2404.02395
| null | null |
http://arxiv.org/pdf/2404.02395v1
|
2024-04-03T01:42:30Z
|
2024-04-03T01:42:30Z
|
Optimal Batch Allocation for Wireless Federated Learning
|
Federated learning aims to construct a global model that fits the dataset distributed across local devices without direct access to private data, leveraging communication between a server and the local devices. In the context of a practical communication scheme, we study the completion time required to achieve a target performance. Specifically, we analyze the number of iterations required for federated learning to reach a specific optimality gap from a minimum global loss. Subsequently, we characterize the time required for each iteration under two fundamental multiple access schemes: time-division multiple access (TDMA) and random access (RA). We propose a step-wise batch allocation, demonstrated to be optimal for TDMA-based federated learning systems. Additionally, we show that the non-zero batch gap between devices provided by the proposed step-wise batch allocation significantly reduces the completion time for RA-based learning systems. Numerical evaluations validate these analytical results through real-data experiments, highlighting the remarkable potential for substantial completion time reduction.
|
[
"['Jaeyoung Song' 'Sang-Woon Jeon']"
] |
null | null |
2404.02396
| null | null |
http://arxiv.org/pdf/2404.02396v1
|
2024-04-03T01:55:15Z
|
2024-04-03T01:55:15Z
|
Enhancing Diffusion-based Point Cloud Generation with Smoothness
Constraint
|
Diffusion models have been popular for point cloud generation tasks. Existing works utilize the forward diffusion process to convert the original point distribution into a noise distribution and then learn the reverse diffusion process to recover the point distribution from the noise distribution. However, the reverse diffusion process can produce samples with non-smooth points on the surface because of the ignorance of the point cloud geometric properties. We propose alleviating the problem by incorporating the local smoothness constraint into the diffusion framework for point cloud generation. Experiments demonstrate the proposed model can generate realistic shapes and smoother point clouds, outperforming multiple state-of-the-art methods.
|
[
"['Yukun Li' 'Liping Liu']"
] |
null | null |
2404.02402
| null | null |
http://arxiv.org/pdf/2404.02402v1
|
2024-04-03T02:11:39Z
|
2024-04-03T02:11:39Z
|
Token Trails: Navigating Contextual Depths in Conversational AI with
ChatLLM
|
Conversational modeling using Large Language Models (LLMs) requires a nuanced understanding of context to generate coherent and contextually relevant responses. In this paper, we present Token Trails, a novel approach that leverages token-type embeddings to navigate the intricate contextual nuances within conversations. Our framework utilizes token-type embeddings to distinguish between user utterances and bot responses, facilitating the generation of context-aware replies. Through comprehensive experimentation and evaluation, we demonstrate the effectiveness of Token Trails in improving conversational understanding and response generation, achieving state-of-the-art performance. Our results highlight the significance of contextual modeling in conversational AI and underscore the promising potential of Token Trails to advance the field, paving the way for more sophisticated and contextually aware chatbot interactions.
|
[
"['Md. Kowsher' 'Ritesh Panditi' 'Nusrat Jahan Prottasha' 'Prakash Bhat'\n 'Anupam Kumar Bairagi' 'Mohammad Shamsul Arefin']"
] |
null | null |
2404.02403
| null | null |
http://arxiv.org/pdf/2404.02403v1
|
2024-04-03T02:12:29Z
|
2024-04-03T02:12:29Z
|
Benchmarking Large Language Models for Persian: A Preliminary Study
Focusing on ChatGPT
|
This paper explores the efficacy of large language models (LLMs) for Persian. While ChatGPT and consequent LLMs have shown remarkable performance in English, their efficiency for more low-resource languages remains an open question. We present the first comprehensive benchmarking study of LLMs across diverse Persian language tasks. Our primary focus is on GPT-3.5-turbo, but we also include GPT-4 and OpenChat-3.5 to provide a more holistic evaluation. Our assessment encompasses a diverse set of tasks categorized into classic, reasoning, and knowledge-based domains. To enable a thorough comparison, we evaluate LLMs against existing task-specific fine-tuned models. Given the limited availability of Persian datasets for reasoning tasks, we introduce two new benchmarks: one based on elementary school math questions and another derived from the entrance exams for 7th and 10th grades. Our findings reveal that while LLMs, especially GPT-4, excel in tasks requiring reasoning abilities and a broad understanding of general knowledge, they often lag behind smaller pre-trained models fine-tuned specifically for particular tasks. Additionally, we observe improved performance when test sets are translated to English before inputting them into GPT-3.5. These results highlight the significant potential for enhancing LLM performance in the Persian language. This is particularly noteworthy due to the unique attributes of Persian, including its distinct alphabet and writing styles.
|
[
"['Amirhossein Abaskohi' 'Sara Baruni' 'Mostafa Masoudi' 'Nesa Abbasi'\n 'Mohammad Hadi Babalou' 'Ali Edalat' 'Sepehr Kamahi'\n 'Samin Mahdizadeh Sani' 'Nikoo Naghavian' 'Danial Namazifard'\n 'Pouya Sadeghi' 'Yadollah Yaghoobzadeh']"
] |
null | null |
2404.02407
| null | null |
http://arxiv.org/pdf/2404.02407v1
|
2024-04-03T02:17:34Z
|
2024-04-03T02:17:34Z
|
Decision Transformer as a Foundation Model for Partially Observable
Continuous Control
|
Closed-loop control of nonlinear dynamical systems with partial-state observability demands expert knowledge of a diverse, less standardized set of theoretical tools. Moreover, it requires a delicate integration of controller and estimator designs to achieve the desired system behavior. To establish a general controller synthesis framework, we explore the Decision Transformer (DT) architecture. Specifically, we first frame the control task as predicting the current optimal action based on past observations, actions, and rewards, eliminating the need for a separate estimator design. Then, we leverage the pre-trained language models, i.e., the Generative Pre-trained Transformer (GPT) series, to initialize DT and subsequently train it for control tasks using low-rank adaptation (LoRA). Our comprehensive experiments across five distinct control tasks, ranging from maneuvering aerospace systems to controlling partial differential equations (PDEs), demonstrate DT's capability to capture the parameter-agnostic structures intrinsic to control tasks. DT exhibits remarkable zero-shot generalization abilities for completely new tasks and rapidly surpasses expert performance levels with a minimal amount of demonstration data. These findings highlight the potential of DT as a foundational controller for general control applications.
|
[
"['Xiangyuan Zhang' 'Weichao Mao' 'Haoran Qiu' 'Tamer Başar']"
] |
null | null |
2404.02422
| null | null |
http://arxiv.org/pdf/2404.02422v1
|
2024-04-03T03:24:19Z
|
2024-04-03T03:24:19Z
|
Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data
|
Large Language Models (LLMs) operating in 0-shot or few-shot settings achieve competitive results in Text Classification tasks. In-Context Learning (ICL) typically achieves better accuracy than the 0-shot setting, but it pays in terms of efficiency, due to the longer input prompt. In this paper, we propose a strategy to make LLMs as efficient as 0-shot text classifiers, while getting comparable or better accuracy than ICL. Our solution targets the low resource setting, i.e., when only 4 examples per class are available. Using a single LLM and few-shot real data we perform a sequence of generation, filtering and Parameter-Efficient Fine-Tuning steps to create a robust and efficient classifier. Experimental results show that our approach leads to competitive results on multiple text classification datasets.
|
[
"['Parth Patwa' 'Simone Filice' 'Zhiyu Chen' 'Giuseppe Castellucci'\n 'Oleg Rokhlenko' 'Shervin Malmasi']"
] |
null | null |
2404.02424
| null | null |
http://arxiv.org/pdf/2404.02424v2
|
2024-06-24T21:37:45Z
|
2024-04-03T03:27:01Z
|
Rethinking Pruning for Vision-Language Models: Strategies for Effective
Sparsity and Performance Restoration
|
Vision-Language Models (VLMs) integrate information from multiple modalities and have shown remarkable success across various tasks. However, deploying large-scale VLMs in resource-constrained scenarios is challenging. Pruning followed by finetuning offers a potential solution but remains underexplored for VLMs. This study addresses two key questions: how to distribute sparsity across different modality-specific models, and how to restore the performance of pruned sparse VLMs. Our preliminary studies identified two effective pruning settings: applying the same sparsity to both vision and language models, and pruning only the language models. While LoRA finetuning aims to restore sparse models, it faces challenges due to incompatibility with sparse models, disrupting the pruned sparsity. To overcome these issues, we propose SparseLoRA, which applies sparsity directly to LoRA weights. Our experimental results demonstrate significant improvements, including an 11.3% boost under 2:4 sparsity and a 47.6% enhancement under unstructured 70% sparsity. Code is released at: url{https://github.com/Shwai-He/VLM-Compression}.
|
[
"['Shwai He' 'Ang Li' 'Tianlong Chen']"
] |
null | null |
2404.02429
| null | null |
http://arxiv.org/pdf/2404.02429v1
|
2024-04-03T03:36:35Z
|
2024-04-03T03:36:35Z
|
AD4RL: Autonomous Driving Benchmarks for Offline Reinforcement Learning
with Value-based Dataset
|
Offline reinforcement learning has emerged as a promising technology by enhancing its practicality through the use of pre-collected large datasets. Despite its practical benefits, most algorithm development research in offline reinforcement learning still relies on game tasks with synthetic datasets. To address such limitations, this paper provides autonomous driving datasets and benchmarks for offline reinforcement learning research. We provide 19 datasets, including real-world human driver's datasets, and seven popular offline reinforcement learning algorithms in three realistic driving scenarios. We also provide a unified decision-making process model that can operate effectively across different scenarios, serving as a reference framework in algorithm design. Our research lays the groundwork for further collaborations in the community to explore practical aspects of existing reinforcement learning methods. Dataset and codes can be found in https://sites.google.com/view/ad4rl.
|
[
"['Dongsu Lee' 'Chanin Eom' 'Minhae Kwon']"
] |
null | null |
2404.02438
| null | null |
http://arxiv.org/pdf/2404.02438v1
|
2024-04-03T03:53:37Z
|
2024-04-03T03:53:37Z
|
From Narratives to Numbers: Valid Inference Using Language Model
Predictions from Verbal Autopsy Narratives
|
In settings where most deaths occur outside the healthcare system, verbal autopsies (VAs) are a common tool to monitor trends in causes of death (COD). VAs are interviews with a surviving caregiver or relative that are used to predict the decedent's COD. Turning VAs into actionable insights for researchers and policymakers requires two steps (i) predicting likely COD using the VA interview and (ii) performing inference with predicted CODs (e.g. modeling the breakdown of causes by demographic factors using a sample of deaths). In this paper, we develop a method for valid inference using outcomes (in our case COD) predicted from free-form text using state-of-the-art NLP techniques. This method, which we call multiPPI++, extends recent work in "prediction-powered inference" to multinomial classification. We leverage a suite of NLP techniques for COD prediction and, through empirical analysis of VA data, demonstrate the effectiveness of our approach in handling transportability issues. multiPPI++ recovers ground truth estimates, regardless of which NLP model produced predictions and regardless of whether they were produced by a more accurate predictor like GPT-4-32k or a less accurate predictor like KNN. Our findings demonstrate the practical importance of inference correction for public health decision-making and suggests that if inference tasks are the end goal, having a small amount of contextually relevant, high quality labeled data is essential regardless of the NLP algorithm.
|
[
"['Shuxian Fan' 'Adam Visokay' 'Kentaro Hoffman' 'Stephen Salerno' 'Li Liu'\n 'Jeffrey T. Leek' 'Tyler H. McCormick']"
] |
null | null |
2404.02446
| null | null |
http://arxiv.org/pdf/2404.02446v1
|
2024-04-03T04:23:01Z
|
2024-04-03T04:23:01Z
|
Masked Completion via Structured Diffusion with White-Box Transformers
|
Modern learning frameworks often train deep neural networks with massive amounts of unlabeled data to learn representations by solving simple pretext tasks, then use the representations as foundations for downstream tasks. These networks are empirically designed; as such, they are usually not interpretable, their representations are not structured, and their designs are potentially redundant. White-box deep networks, in which each layer explicitly identifies and transforms structures in the data, present a promising alternative. However, existing white-box architectures have only been shown to work at scale in supervised settings with labeled data, such as classification. In this work, we provide the first instantiation of the white-box design paradigm that can be applied to large-scale unsupervised representation learning. We do this by exploiting a fundamental connection between diffusion, compression, and (masked) completion, deriving a deep transformer-like masked autoencoder architecture, called CRATE-MAE, in which the role of each layer is mathematically fully interpretable: they transform the data distribution to and from a structured representation. Extensive empirical evaluations confirm our analytical insights. CRATE-MAE demonstrates highly promising performance on large-scale imagery datasets while using only ~30% of the parameters compared to the standard masked autoencoder with the same model configuration. The representations learned by CRATE-MAE have explicit structure and also contain semantic meaning. Code is available at https://github.com/Ma-Lab-Berkeley/CRATE .
|
[
"['Druv Pai' 'Ziyang Wu' 'Sam Buchanan' 'Yaodong Yu' 'Yi Ma']"
] |
null | null |
2404.02448
| null | null |
http://arxiv.org/pdf/2404.02448v2
|
2024-04-08T02:46:38Z
|
2024-04-03T04:27:07Z
|
Electric Vehicle Routing Problem for Emergency Power Supply: Towards
Telecom Base Station Relief
|
As a telecom provider, our company has a critical mission to maintain telecom services even during power outages. To accomplish the mission, it is essential to maintain the power of the telecom base stations. Here we consider a solution where electric vehicles (EVs) directly supply power to base stations by traveling to their locations. The goal is to find EV routes that minimize both the total travel distance of all EVs and the number of downed base stations. In this paper, we formulate this routing problem as a new variant of the Electric Vehicle Routing Problem (EVRP) and propose a solver that combines a rule-based vehicle selector and a reinforcement learning (RL)-based node selector. The rule of the vehicle selector ensures the exact environmental states when the selected EV starts to move. In addition, the node selection by the RL model enables fast route generation, which is critical in emergencies. We evaluate our solver on both synthetic datasets and real datasets. The results show that our solver outperforms baselines in terms of the objective value and computation time. Moreover, we analyze the generalization and scalability of our solver, demonstrating the capability toward unseen settings and large-scale problems. Check also our project page: https://ntt-dkiku.github.io/rl-evrpeps.
|
[
"['Daisuke Kikuta' 'Hiroki Ikeuchi' 'Kengo Tajiri' 'Yuta Toyama'\n 'Masaki Nakamura' 'Yuusuke Nakano']"
] |
null | null |
2404.02450
| null | null |
http://arxiv.org/pdf/2404.02450v1
|
2024-04-03T04:31:09Z
|
2024-04-03T04:31:09Z
|
Task Agnostic Architecture for Algorithm Induction via Implicit
Composition
|
Different fields in applied machine learning such as computer vision, speech or natural language processing have been building domain-specialised solutions. Currently, we are witnessing an opposing trend towards developing more generalist architectures, driven by Large Language Models and multi-modal foundational models. These architectures are designed to tackle a variety of tasks, including those previously unseen and using inputs across multiple modalities. Taking this trend of generalization to the extreme suggests the possibility of a single deep network architecture capable of solving all tasks. This position paper aims to explore developing such a unified architecture and proposes a theoretical framework of how it could be constructed. Our proposal is based on the following assumptions. Firstly, tasks are solved by following a sequence of instructions, typically implemented in code for conventional computing hardware, which inherently operates sequentially. Second, recent Generative AI, especially Transformer-based models, demonstrate potential as an architecture capable of constructing algorithms for a wide range of domains. For example, GPT-4 shows exceptional capability at in-context learning of novel tasks which is hard to explain in any other way than the ability to compose novel solutions from fragments on previously learnt algorithms. Third, the observation that the main missing component in developing a truly generalised network is an efficient approach for self-consistent input of previously learnt sub-steps of an algorithm and their (implicit) composition during the network's internal forward pass. Our exploration delves into current capabilities and limitations of Transformer-based and other methods in efficient and correct algorithm composition and proposes a Transformer-like architecture as well as a discrete learning framework to overcome these limitations.
|
[
"['Sahil J. Sindhi' 'Ignas Budvytis']"
] |
null | null |
2404.02456
| null | null |
http://arxiv.org/pdf/2404.02456v2
|
2024-04-05T04:55:24Z
|
2024-04-03T04:53:14Z
|
PhonologyBench: Evaluating Phonological Skills of Large Language Models
|
Phonology, the study of speech's structure and pronunciation rules, is a critical yet often overlooked component in Large Language Model (LLM) research. LLMs are widely used in various downstream applications that leverage phonology such as educational tools and poetry generation. Moreover, LLMs can potentially learn imperfect associations between orthographic and phonological forms from the training data. Thus, it is imperative to benchmark the phonological skills of LLMs. To this end, we present PhonologyBench, a novel benchmark consisting of three diagnostic tasks designed to explicitly test the phonological skills of LLMs in English: grapheme-to-phoneme conversion, syllable counting, and rhyme word generation. Despite having no access to speech data, LLMs showcased notable performance on the PhonologyBench tasks. However, we observe a significant gap of 17% and 45% on Rhyme Word Generation and Syllable counting, respectively, when compared to humans. Our findings underscore the importance of studying LLM performance on phonological tasks that inadvertently impact real-world applications. Furthermore, we encourage researchers to choose LLMs that perform well on the phonological task that is closely related to the downstream application since we find that no single model consistently outperforms the others on all the tasks.
|
[
"['Ashima Suvarna' 'Harshita Khandelwal' 'Nanyun Peng']"
] |
null | null |
2404.02461
| null | null |
http://arxiv.org/pdf/2404.02461v1
|
2024-04-03T05:04:06Z
|
2024-04-03T05:04:06Z
|
On the Efficiency and Robustness of Vibration-based Foundation Models
for IoT Sensing: A Case Study
|
This paper demonstrates the potential of vibration-based Foundation Models (FMs), pre-trained with unlabeled sensing data, to improve the robustness of run-time inference in (a class of) IoT applications. A case study is presented featuring a vehicle classification application using acoustic and seismic sensing. The work is motivated by the success of foundation models in the areas of natural language processing and computer vision, leading to generalizations of the FM concept to other domains as well, where significant amounts of unlabeled data exist that can be used for self-supervised pre-training. One such domain is IoT applications. Foundation models for selected sensing modalities in the IoT domain can be pre-trained in an environment-agnostic fashion using available unlabeled sensor data and then fine-tuned to the deployment at hand using a small amount of labeled data. The paper shows that the pre-training/fine-tuning approach improves the robustness of downstream inference and facilitates adaptation to different environmental conditions. More specifically, we present a case study in a real-world setting to evaluate a simple (vibration-based) FM-like model, called FOCAL, demonstrating its superior robustness and adaptation, compared to conventional supervised deep neural networks (DNNs). We also demonstrate its superior convergence over supervised solutions. Our findings highlight the advantages of vibration-based FMs (and FM-inspired selfsupervised models in general) in terms of inference robustness, runtime efficiency, and model adaptation (via fine-tuning) in resource-limited IoT settings.
|
[
"['Tomoyoshi Kimura' 'Jinyang Li' 'Tianshi Wang' 'Denizhan Kara'\n 'Yizhuo Chen' 'Yigong Hu' 'Ruijie Wang' 'Maggie Wigness' 'Shengzhong Liu'\n 'Mani Srivastava' 'Suhas Diggavi' 'Tarek Abdelzaher']"
] |
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