categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2405.01263 | null | null | http://arxiv.org/pdf/2405.01263v2 | 2024-06-17T12:01:01Z | 2024-05-02T13:11:53Z | An Online Gradient-Based Caching Policy with Logarithmic Complexity and
Regret Guarantees | Commonly used caching policies, such as LRU (Least Recently Used) or LFU (Least Frequently Used), exhibit optimal performance only under specific traffic patterns. Even advanced machine learning-based methods, which detect patterns in historical request data, struggle when future requests deviate from past trends. Recently, a new class of policies has emerged that are robust to varying traffic patterns. These algorithms address an online optimization problem, enabling continuous adaptation to the context. They offer theoretical guarantees on the regret metric, which measures the performance gap between the online policy and the optimal static cache allocation in hindsight. However, the high computational complexity of these solutions hinders their practical adoption. In this study, we introduce a new variant of the gradient-based online caching policy that achieves groundbreaking logarithmic computational complexity relative to catalog size, while also providing regret guarantees. This advancement allows us to test the policy on large-scale, real-world traces featuring millions of requests and items - a significant achievement, as such scales have been beyond the reach of existing policies with regret guarantees. To the best of our knowledge, our experimental results demonstrate for the first time that the regret guarantees of gradient-based caching policies offer substantial benefits in practical scenarios. | [
"['Damiano Carra' 'Giovanni Neglia']"
]
|
null | null | 2405.01270 | null | null | http://arxiv.org/pdf/2405.01270v1 | 2024-05-02T13:26:18Z | 2024-05-02T13:26:18Z | The Importance of Model Inspection for Better Understanding Performance
Characteristics of Graph Neural Networks | This study highlights the importance of conducting comprehensive model inspection as part of comparative performance analyses. Here, we investigate the effect of modelling choices on the feature learning characteristics of graph neural networks applied to a brain shape classification task. Specifically, we analyse the effect of using parameter-efficient, shared graph convolutional submodels compared to structure-specific, non-shared submodels. Further, we assess the effect of mesh registration as part of the data harmonisation pipeline. We find substantial differences in the feature embeddings at different layers of the models. Our results highlight that test accuracy alone is insufficient to identify important model characteristics such as encoded biases related to data source or potentially non-discriminative features learned in submodels. Our model inspection framework offers a valuable tool for practitioners to better understand performance characteristics of deep learning models in medical imaging. | [
"['Nairouz Shehata' 'Carolina Piçarra' 'Anees Kazi' 'Ben Glocker']"
]
|
null | null | 2405.01277 | null | null | http://arxiv.org/pdf/2405.01277v1 | 2024-05-02T13:35:15Z | 2024-05-02T13:35:15Z | Quantifying Spatial Domain Explanations in BCI using Earth Mover's
Distance | Brain-computer interface (BCI) systems facilitate unique communication between humans and computers, benefiting severely disabled individuals. Despite decades of research, BCIs are not fully integrated into clinical and commercial settings. It's crucial to assess and explain BCI performance, offering clear explanations for potential users to avoid frustration when it doesn't work as expected. This work investigates the efficacy of different deep learning and Riemannian geometry-based classification models in the context of motor imagery (MI) based BCI using electroencephalography (EEG). We then propose an optimal transport theory-based approach using earth mover's distance (EMD) to quantify the comparison of the feature relevance map with the domain knowledge of neuroscience. For this, we utilized explainable AI (XAI) techniques for generating feature relevance in the spatial domain to identify important channels for model outcomes. Three state-of-the-art models are implemented - 1) Riemannian geometry-based classifier, 2) EEGNet, and 3) EEG Conformer, and the observed trend in the model's accuracy across different architectures on the dataset correlates with the proposed feature relevance metrics. The models with diverse architectures perform significantly better when trained on channels relevant to motor imagery than data-driven channel selection. This work focuses attention on the necessity for interpretability and incorporating metrics beyond accuracy, underscores the value of combining domain knowledge and quantifying model interpretations with data-driven approaches in creating reliable and robust Brain-Computer Interfaces (BCIs). | [
"['Param Rajpura' 'Hubert Cecotti' 'Yogesh Kumar Meena']"
]
|
null | null | 2405.01284 | null | null | http://arxiv.org/pdf/2405.01284v1 | 2024-05-02T13:43:22Z | 2024-05-02T13:43:22Z | Behavior Imitation for Manipulator Control and Grasping with Deep
Reinforcement Learning | The existing Motion Imitation models typically require expert data obtained through MoCap devices, but the vast amount of training data needed is difficult to acquire, necessitating substantial investments of financial resources, manpower, and time. This project combines 3D human pose estimation with reinforcement learning, proposing a novel model that simplifies Motion Imitation into a prediction problem of joint angle values in reinforcement learning. This significantly reduces the reliance on vast amounts of training data, enabling the agent to learn an imitation policy from just a few seconds of video and exhibit strong generalization capabilities. It can quickly apply the learned policy to imitate human arm motions in unfamiliar videos. The model first extracts skeletal motions of human arms from a given video using 3D human pose estimation. These extracted arm motions are then morphologically retargeted onto a robotic manipulator. Subsequently, the retargeted motions are used to generate reference motions. Finally, these reference motions are used to formulate a reinforcement learning problem, enabling the agent to learn a policy for imitating human arm motions. This project excels at imitation tasks and demonstrates robust transferability, accurately imitating human arm motions from other unfamiliar videos. This project provides a lightweight, convenient, efficient, and accurate Motion Imitation model. While simplifying the complex process of Motion Imitation, it achieves notably outstanding performance. | [
"['Liu Qiyuan']"
]
|
null | null | 2405.01292 | null | null | http://arxiv.org/pdf/2405.01292v1 | 2024-05-02T13:54:21Z | 2024-05-02T13:54:21Z | Koopman Data-Driven Predictive Control with Robust Stability and
Recursive Feasibility Guarantees | In this paper, we consider the design of data-driven predictive controllers for nonlinear systems from input-output data via linear-in-control input Koopman lifted models. Instead of identifying and simulating a Koopman model to predict future outputs, we design a subspace predictive controller in the Koopman space. This allows us to learn the observables minimizing the multi-step output prediction error of the Koopman subspace predictor, preventing the propagation of prediction errors. To avoid losing feasibility of our predictive control scheme due to prediction errors, we compute a terminal cost and terminal set in the Koopman space and we obtain recursive feasibility guarantees through an interpolated initial state. As a third contribution, we introduce a novel regularization cost yielding input-to-state stability guarantees with respect to the prediction error for the resulting closed-loop system. The performance of the developed Koopman data-driven predictive control methodology is illustrated on a nonlinear benchmark example from the literature. | [
"['Thomas de Jong' 'Valentina Breschi' 'Maarten Schoukens' 'Mircea Lazar']"
]
|
null | null | 2405.01299 | null | null | http://arxiv.org/pdf/2405.01299v1 | 2024-05-02T14:00:22Z | 2024-05-02T14:00:22Z | The Effectiveness of LLMs as Annotators: A Comparative Overview and
Empirical Analysis of Direct Representation | Large Language Models (LLMs) have emerged as powerful support tools across various natural language tasks and a range of application domains. Recent studies focus on exploring their capabilities for data annotation. This paper provides a comparative overview of twelve studies investigating the potential of LLMs in labelling data. While the models demonstrate promising cost and time-saving benefits, there exist considerable limitations, such as representativeness, bias, sensitivity to prompt variations and English language preference. Leveraging insights from these studies, our empirical analysis further examines the alignment between human and GPT-generated opinion distributions across four subjective datasets. In contrast to the studies examining representation, our methodology directly obtains the opinion distribution from GPT. Our analysis thereby supports the minority of studies that are considering diverse perspectives when evaluating data annotation tasks and highlights the need for further research in this direction. | [
"['Maja Pavlovic' 'Massimo Poesio']"
]
|
null | null | 2405.01306 | null | null | http://arxiv.org/pdf/2405.01306v1 | 2024-05-02T14:12:58Z | 2024-05-02T14:12:58Z | Graph is all you need? Lightweight data-agnostic neural architecture
search without training | Neural architecture search (NAS) enables the automatic design of neural network models. However, training the candidates generated by the search algorithm for performance evaluation incurs considerable computational overhead. Our method, dubbed nasgraph, remarkably reduces the computational costs by converting neural architectures to graphs and using the average degree, a graph measure, as the proxy in lieu of the evaluation metric. Our training-free NAS method is data-agnostic and light-weight. It can find the best architecture among 200 randomly sampled architectures from NAS-Bench201 in 217 CPU seconds. Besides, our method is able to achieve competitive performance on various datasets including NASBench-101, NASBench-201, and NDS search spaces. We also demonstrate that nasgraph generalizes to more challenging tasks on Micro TransNAS-Bench-101. | [
"['Zhenhan Huang' 'Tejaswini Pedapati' 'Pin-Yu Chen' 'Chunhen Jiang'\n 'Jianxi Gao']"
]
|
null | null | 2405.01314 | null | null | http://arxiv.org/pdf/2405.01314v1 | 2024-05-02T14:21:29Z | 2024-05-02T14:21:29Z | Non-iterative Optimization of Trajectory and Radio Resource for Aerial
Network | We address a joint trajectory planning, user association, resource allocation, and power control problem to maximize proportional fairness in the aerial IoT network, considering practical end-to-end quality-of-service (QoS) and communication schedules. Though the problem is rather ancient, apart from the fact that the previous approaches have never considered user- and time-specific QoS, we point out a prevalent mistake in coordinate optimization approaches adopted by the majority of the literature. Coordinate optimization approaches, which repetitively optimize radio resources for a fixed trajectory and vice versa, generally converge to local optima when all variables are differentiable. However, these methods often stagnate at a non-stationary point, significantly degrading the network utility in mixed-integer problems such as joint trajectory and radio resource optimization. We detour this problem by converting the formulated problem into the Markov decision process (MDP). Exploiting the beneficial characteristics of the MDP, we design a non-iterative framework that cooperatively optimizes trajectory and radio resources without initial trajectory choice. The proposed framework can incorporate various trajectory planning algorithms such as the genetic algorithm, tree search, and reinforcement learning. Extensive comparisons with diverse baselines verify that the proposed framework significantly outperforms the state-of-the-art method, nearly achieving the global optimum. Our implementation code is available at https://github.com/hslyu/dbspf. | [
"['Hyeonsu Lyu' 'Jonggyu Jang' 'Harim Lee' 'Hyun Jong Yang']"
]
|
null | null | 2405.01319 | null | null | http://arxiv.org/pdf/2405.01319v1 | 2024-05-02T14:24:56Z | 2024-05-02T14:24:56Z | Data Scoping: Effectively Learning the Evolution of Generic Transport
PDEs | Transport phenomena (e.g., fluid flows) are governed by time-dependent partial differential equations (PDEs) describing mass, momentum, and energy conservation, and are ubiquitous in many engineering applications. However, deep learning architectures are fundamentally incompatible with the simulation of these PDEs. This paper clearly articulates and then solves this incompatibility. The local-dependency of generic transport PDEs implies that it only involves local information to predict the physical properties at a location in the next time step. However, the deep learning architecture will inevitably increase the scope of information to make such predictions as the number of layers increases, which can cause sluggish convergence and compromise generalizability. This paper aims to solve this problem by proposing a distributed data scoping method with linear time complexity to strictly limit the scope of information to predict the local properties. The numerical experiments over multiple physics show that our data scoping method significantly accelerates training convergence and improves the generalizability of benchmark models on large-scale engineering simulations. Specifically, over the geometries not included in the training data for heat transferring simulation, it can increase the accuracy of Convolutional Neural Networks (CNNs) by 21.7 % and that of Fourier Neural Operators (FNOs) by 38.5 % on average. | [
"['Jiangce Chen' 'Wenzhuo Xu' 'Zeda Xu' 'Noelia Grande Gutiérrez'\n 'Sneha Prabha Narra' 'Christopher McComb']"
]
|
null | null | 2405.01327 | null | null | http://arxiv.org/pdf/2405.01327v2 | 2024-05-03T17:24:11Z | 2024-05-02T14:31:52Z | Constrained Reinforcement Learning Under Model Mismatch | Existing studies on constrained reinforcement learning (RL) may obtain a well-performing policy in the training environment. However, when deployed in a real environment, it may easily violate constraints that were originally satisfied during training because there might be model mismatch between the training and real environments. To address the above challenge, we formulate the problem as constrained RL under model uncertainty, where the goal is to learn a good policy that optimizes the reward and at the same time satisfy the constraint under model mismatch. We develop a Robust Constrained Policy Optimization (RCPO) algorithm, which is the first algorithm that applies to large/continuous state space and has theoretical guarantees on worst-case reward improvement and constraint violation at each iteration during the training. We demonstrate the effectiveness of our algorithm on a set of RL tasks with constraints. | [
"['Zhongchang Sun' 'Sihong He' 'Fei Miao' 'Shaofeng Zou']"
]
|
null | null | 2405.01349 | null | null | http://arxiv.org/pdf/2405.01349v1 | 2024-05-02T14:58:44Z | 2024-05-02T14:58:44Z | Position Paper: Beyond Robustness Against Single Attack Types | Current research on defending against adversarial examples focuses primarily on achieving robustness against a single attack type such as $ell_2$ or $ell_{infty}$-bounded attacks. However, the space of possible perturbations is much larger and currently cannot be modeled by a single attack type. The discrepancy between the focus of current defenses and the space of attacks of interest calls to question the practicality of existing defenses and the reliability of their evaluation. In this position paper, we argue that the research community should look beyond single attack robustness, and we draw attention to three potential directions involving robustness against multiple attacks: simultaneous multiattack robustness, unforeseen attack robustness, and a newly defined problem setting which we call continual adaptive robustness. We provide a unified framework which rigorously defines these problem settings, synthesize existing research in these fields, and outline open directions. We hope that our position paper inspires more research in simultaneous multiattack, unforeseen attack, and continual adaptive robustness. | [
"['Sihui Dai' 'Chong Xiang' 'Tong Wu' 'Prateek Mittal']"
]
|
null | null | 2405.01350 | null | null | http://arxiv.org/pdf/2405.01350v1 | 2024-05-02T14:59:58Z | 2024-05-02T14:59:58Z | Community-Invariant Graph Contrastive Learning | Graph augmentation has received great attention in recent years for graph contrastive learning (GCL) to learn well-generalized node/graph representations. However, mainstream GCL methods often favor randomly disrupting graphs for augmentation, which shows limited generalization and inevitably leads to the corruption of high-level graph information, i.e., the graph community. Moreover, current knowledge-based graph augmentation methods can only focus on either topology or node features, causing the model to lack robustness against various types of noise. To address these limitations, this research investigated the role of the graph community in graph augmentation and figured out its crucial advantage for learnable graph augmentation. Based on our observations, we propose a community-invariant GCL framework to maintain graph community structure during learnable graph augmentation. By maximizing the spectral changes, this framework unifies the constraints of both topology and feature augmentation, enhancing the model's robustness. Empirical evidence on 21 benchmark datasets demonstrates the exclusive merits of our framework. Code is released on Github (https://github.com/ShiyinTan/CI-GCL.git). | [
"['Shiyin Tan' 'Dongyuan Li' 'Renhe Jiang' 'Ying Zhang' 'Manabu Okumura']"
]
|
null | null | 2405.01365 | null | null | http://arxiv.org/pdf/2405.01365v1 | 2024-05-02T15:09:59Z | 2024-05-02T15:09:59Z | Dynamic Online Ensembles of Basis Expansions | Practical Bayesian learning often requires (1) online inference, (2) dynamic models, and (3) ensembling over multiple different models. Recent advances have shown how to use random feature approximations to achieve scalable, online ensembling of Gaussian processes with desirable theoretical properties and fruitful applications. One key to these methods' success is the inclusion of a random walk on the model parameters, which makes models dynamic. We show that these methods can be generalized easily to any basis expansion model and that using alternative basis expansions, such as Hilbert space Gaussian processes, often results in better performance. To simplify the process of choosing a specific basis expansion, our method's generality also allows the ensembling of several entirely different models, for example, a Gaussian process and polynomial regression. Finally, we propose a novel method to ensemble static and dynamic models together. | [
"['Daniel Waxman' 'Petar M. Djurić']"
]
|
null | null | 2405.01389 | null | null | http://arxiv.org/pdf/2405.01389v5 | 2024-05-17T04:14:34Z | 2024-05-02T15:34:14Z | Invariant Risk Minimization Is A Total Variation Model | Invariant risk minimization (IRM) is an arising approach to generalize invariant features to different environments in machine learning. While most related works focus on new IRM settings or new application scenarios, the mathematical essence of IRM remains to be properly explained. We verify that IRM is essentially a total variation based on $L^2$ norm (TV-$ell_2$) of the learning risk with respect to the classifier variable. Moreover, we propose a novel IRM framework based on the TV-$ell_1$ model. It not only expands the classes of functions that can be used as the learning risk and the feature extractor, but also has robust performance in denoising and invariant feature preservation based on the coarea formula. We also illustrate some requirements for IRM-TV-$ell_1$ to achieve out-of-distribution generalization. Experimental results show that the proposed framework achieves competitive performance in several benchmark machine learning scenarios. | [
"['Zhao-Rong Lai' 'Weiwen Wang']"
]
|
null | null | 2405.01392 | null | null | http://arxiv.org/pdf/2405.01392v1 | 2024-04-13T03:33:17Z | 2024-04-13T03:33:17Z | LLMSat: A Large Language Model-Based Goal-Oriented Agent for Autonomous
Space Exploration | As spacecraft journey further from Earth with more complex missions, systems of greater autonomy and onboard intelligence are called for. Reducing reliance on human-based mission control becomes increasingly critical if we are to increase our rate of solar-system-wide exploration. Recent work has explored AI-based goal-oriented systems to increase the level of autonomy in mission execution. These systems make use of symbolic reasoning managers to make inferences from the state of a spacecraft and a handcrafted knowledge base, enabling autonomous generation of tasks and re-planning. Such systems have proven to be successful in controlled cases, but they are difficult to implement as they require human-crafted ontological models to allow the spacecraft to understand the world. Reinforcement learning has been applied to train robotic agents to pursue a goal. A new architecture for autonomy is called for. This work explores the application of Large Language Models (LLMs) as the high-level control system of a spacecraft. Using a systems engineering approach, this work presents the design and development of an agentic spacecraft controller by leveraging an LLM as a reasoning engine, to evaluate the utility of such an architecture in achieving higher levels of spacecraft autonomy. A series of deep space mission scenarios simulated within the popular game engine Kerbal Space Program (KSP) are used as case studies to evaluate the implementation against the requirements. It is shown the reasoning and planning abilities of present-day LLMs do not scale well as the complexity of a mission increases, but this can be alleviated with adequate prompting frameworks and strategic selection of the agent's level of authority over the host spacecraft. This research evaluates the potential of LLMs in augmenting autonomous decision-making systems for future robotic space applications. | [
"['David Maranto']"
]
|
null | null | 2405.01402 | null | null | http://arxiv.org/pdf/2405.01402v2 | 2024-05-20T12:15:56Z | 2024-05-02T15:53:43Z | Learning Force Control for Legged Manipulation | Controlling contact forces during interactions is critical for locomotion and manipulation tasks. While sim-to-real reinforcement learning (RL) has succeeded in many contact-rich problems, current RL methods achieve forceful interactions implicitly without explicitly regulating forces. We propose a method for training RL policies for direct force control without requiring access to force sensing. We showcase our method on a whole-body control platform of a quadruped robot with an arm. Such force control enables us to perform gravity compensation and impedance control, unlocking compliant whole-body manipulation. The learned whole-body controller with variable compliance makes it intuitive for humans to teleoperate the robot by only commanding the manipulator, and the robot's body adjusts automatically to achieve the desired position and force. Consequently, a human teleoperator can easily demonstrate a wide variety of loco-manipulation tasks. To the best of our knowledge, we provide the first deployment of learned whole-body force control in legged manipulators, paving the way for more versatile and adaptable legged robots. | [
"['Tifanny Portela' 'Gabriel B. Margolis' 'Yandong Ji' 'Pulkit Agrawal']"
]
|
null | null | 2405.01404 | null | null | http://arxiv.org/pdf/2405.01404v2 | 2024-06-21T09:58:51Z | 2024-05-02T15:54:46Z | Random Pareto front surfaces | The goal of multi-objective optimisation is to identify the Pareto front surface which is the set obtained by connecting the best trade-off points. Typically this surface is computed by evaluating the objectives at different points and then interpolating between the subset of the best evaluated trade-off points. In this work, we propose to parameterise the Pareto front surface using polar coordinates. More precisely, we show that any Pareto front surface can be equivalently represented using a scalar-valued length function which returns the projected length along any positive radial direction. We then use this representation in order to rigorously develop the theory and applications of stochastic Pareto front surfaces. In particular, we derive many Pareto front surface statistics of interest such as the expectation, covariance and quantiles. We then discuss how these can be used in practice within a design of experiments setting, where the goal is to both infer and use the Pareto front surface distribution in order to make effective decisions. Our framework allows for clear uncertainty quantification and we also develop advanced visualisation techniques for this purpose. Finally we discuss the applicability of our ideas within multivariate extreme value theory and illustrate our methodology in a variety of numerical examples, including a case study with a real-world air pollution data set. | [
"['Ben Tu' 'Nikolas Kantas' 'Robert M. Lee' 'Behrang Shafei']"
]
|
null | null | 2405.01413 | null | null | http://arxiv.org/pdf/2405.01413v1 | 2024-05-02T16:04:30Z | 2024-05-02T16:04:30Z | MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language
Models using 2D Priors | Large 2D vision-language models (2D-LLMs) have gained significant attention by bridging Large Language Models (LLMs) with images using a simple projector. Inspired by their success, large 3D point cloud-language models (3D-LLMs) also integrate point clouds into LLMs. However, directly aligning point clouds with LLM requires expensive training costs, typically in hundreds of GPU-hours on A100, which hinders the development of 3D-LLMs. In this paper, we introduce MiniGPT-3D, an efficient and powerful 3D-LLM that achieves multiple SOTA results while training for only 27 hours on one RTX 3090. Specifically, we propose to align 3D point clouds with LLMs using 2D priors from 2D-LLMs, which can leverage the similarity between 2D and 3D visual information. We introduce a novel four-stage training strategy for modality alignment in a cascaded way, and a mixture of query experts module to adaptively aggregate features with high efficiency. Moreover, we utilize parameter-efficient fine-tuning methods LoRA and Norm fine-tuning, resulting in only 47.8M learnable parameters, which is up to 260x fewer than existing methods. Extensive experiments show that MiniGPT-3D achieves SOTA on 3D object classification and captioning tasks, with significantly cheaper training costs. Notably, MiniGPT-3D gains an 8.12 increase on GPT-4 evaluation score for the challenging object captioning task compared to ShapeLLM-13B, while the latter costs 160 total GPU-hours on 8 A800. We are the first to explore the efficient 3D-LLM, offering new insights to the community. Code and weights are available at https://github.com/TangYuan96/MiniGPT-3D. | [
"['Yuan Tang' 'Xu Han' 'Xianzhi Li' 'Qiao Yu' 'Yixue Hao' 'Long Hu'\n 'Min Chen']"
]
|
null | null | 2405.01425 | null | null | http://arxiv.org/pdf/2405.01425v1 | 2024-05-02T16:15:46Z | 2024-05-02T16:15:46Z | In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies | We present a new random walk for uniformly sampling high-dimensional convex bodies. It achieves state-of-the-art runtime complexity with stronger guarantees on the output than previously known, namely in R'enyi divergence (which implies TV, $mathcal{W}_2$, KL, $chi^2$). The proof departs from known approaches for polytime algorithms for the problem -- we utilize a stochastic diffusion perspective to show contraction to the target distribution with the rate of convergence determined by functional isoperimetric constants of the stationary density. | [
"['Yunbum Kook' 'Santosh S. Vempala' 'Matthew S. Zhang']"
]
|
null | null | 2405.01435 | null | null | http://arxiv.org/pdf/2405.01435v1 | 2024-03-28T14:31:37Z | 2024-03-28T14:31:37Z | Closed-form congestion control via deep symbolic regression | As mobile networks embrace the 5G era, the interest in adopting Reinforcement Learning (RL) algorithms to handle challenges in ultra-low-latency and high throughput scenarios increases. Simultaneously, the advent of packetized fronthaul networks imposes demanding requirements that traditional congestion control mechanisms cannot accomplish, highlighting the potential of RL-based congestion control algorithms. Although learning RL policies optimized for satisfying the stringent fronthaul requirements is feasible, the adoption of neural network models in real deployments still poses some challenges regarding real-time inference and interpretability. This paper proposes a methodology to deal with such challenges while maintaining the performance and generalization capabilities provided by a baseline RL policy. The method consists of (1) training a congestion control policy specialized in fronthaul-like networks via reinforcement learning, (2) collecting state-action experiences from the baseline, and (3) performing deep symbolic regression on the collected dataset. The proposed process overcomes the challenges related to inference-time limitations through closed-form expressions that approximate the baseline performance (link utilization, delay, and fairness) and which can be directly implemented in any programming language. Finally, we analyze the inner workings of the closed-form expressions. | [
"['Jean Martins' 'Igor Almeida' 'Ricardo Souza' 'Silvia Lins']"
]
|
null | null | 2405.01440 | null | null | http://arxiv.org/pdf/2405.01440v1 | 2024-04-12T08:32:54Z | 2024-04-12T08:32:54Z | A Review of Reward Functions for Reinforcement Learning in the context
of Autonomous Driving | Reinforcement learning has emerged as an important approach for autonomous driving. A reward function is used in reinforcement learning to establish the learned skill objectives and guide the agent toward the optimal policy. Since autonomous driving is a complex domain with partly conflicting objectives with varying degrees of priority, developing a suitable reward function represents a fundamental challenge. This paper aims to highlight the gap in such function design by assessing different proposed formulations in the literature and dividing individual objectives into Safety, Comfort, Progress, and Traffic Rules compliance categories. Additionally, the limitations of the reviewed reward functions are discussed, such as objectives aggregation and indifference to driving context. Furthermore, the reward categories are frequently inadequately formulated and lack standardization. This paper concludes by proposing future research that potentially addresses the observed shortcomings in rewards, including a reward validation framework and structured rewards that are context-aware and able to resolve conflicts. | [
"['Ahmed Abouelazm' 'Jonas Michel' 'J. Marius Zoellner']"
]
|
null | null | 2405.01451 | null | null | http://arxiv.org/pdf/2405.01451v1 | 2024-05-02T16:35:07Z | 2024-05-02T16:35:07Z | Test-time Assessment of a Model's Performance on Unseen Domains via
Optimal Transport | Gauging the performance of ML models on data from unseen domains at test-time is essential yet a challenging problem due to the lack of labels in this setting. Moreover, the performance of these models on in-distribution data is a poor indicator of their performance on data from unseen domains. Thus, it is essential to develop metrics that can provide insights into the model's performance at test time and can be computed only with the information available at test time (such as their model parameters, the training data or its statistics, and the unlabeled test data). To this end, we propose a metric based on Optimal Transport that is highly correlated with the model's performance on unseen domains and is efficiently computable only using information available at test time. Concretely, our metric characterizes the model's performance on unseen domains using only a small amount of unlabeled data from these domains and data or statistics from the training (source) domain(s). Through extensive empirical evaluation using standard benchmark datasets, and their corruptions, we demonstrate the utility of our metric in estimating the model's performance in various practical applications. These include the problems of selecting the source data and architecture that leads to the best performance on data from an unseen domain and the problem of predicting a deployed model's performance at test time on unseen domains. Our empirical results show that our metric, which uses information from both the source and the unseen domain, is highly correlated with the model's performance, achieving a significantly better correlation than that obtained via the popular prediction entropy-based metric, which is computed solely using the data from the unseen domain. | [
"['Akshay Mehra' 'Yunbei Zhang' 'Jihun Hamm']"
]
|
null | null | 2405.01453 | null | null | http://arxiv.org/pdf/2405.01453v1 | 2024-05-02T16:36:26Z | 2024-05-02T16:36:26Z | Creative Problem Solving in Large Language and Vision Models -- What
Would it Take? | In this paper, we discuss approaches for integrating Computational Creativity (CC) with research in large language and vision models (LLVMs) to address a key limitation of these models, i.e., creative problem solving. We present preliminary experiments showing how CC principles can be applied to address this limitation through augmented prompting. With this work, we hope to foster discussions of Computational Creativity in the context of ML algorithms for creative problem solving in LLVMs. Our code is at: https://github.com/lnairGT/creative-problem-solving-LLMs | [
"['Lakshmi Nair' 'Evana Gizzi' 'Jivko Sinapov']"
]
|
null | null | 2405.01458 | null | null | http://arxiv.org/pdf/2405.01458v1 | 2024-05-02T16:44:31Z | 2024-05-02T16:44:31Z | UQA: Corpus for Urdu Question Answering | This paper introduces UQA, a novel dataset for question answering and text comprehension in Urdu, a low-resource language with over 70 million native speakers. UQA is generated by translating the Stanford Question Answering Dataset (SQuAD2.0), a large-scale English QA dataset, using a technique called EATS (Enclose to Anchor, Translate, Seek), which preserves the answer spans in the translated context paragraphs. The paper describes the process of selecting and evaluating the best translation model among two candidates: Google Translator and Seamless M4T. The paper also benchmarks several state-of-the-art multilingual QA models on UQA, including mBERT, XLM-RoBERTa, and mT5, and reports promising results. For XLM-RoBERTa-XL, we have an F1 score of 85.99 and 74.56 EM. UQA is a valuable resource for developing and testing multilingual NLP systems for Urdu and for enhancing the cross-lingual transferability of existing models. Further, the paper demonstrates the effectiveness of EATS for creating high-quality datasets for other languages and domains. The UQA dataset and the code are publicly available at www.github.com/sameearif/UQA. | [
"['Samee Arif' 'Sualeha Farid' 'Awais Athar' 'Agha Ali Raza']"
]
|
null | null | 2405.01460 | null | null | http://arxiv.org/pdf/2405.01460v2 | 2024-05-06T06:50:10Z | 2024-05-02T16:49:25Z | Purify Unlearnable Examples via Rate-Constrained Variational
Autoencoders | Unlearnable examples (UEs) seek to maximize testing error by making subtle modifications to training examples that are correctly labeled. Defenses against these poisoning attacks can be categorized based on whether specific interventions are adopted during training. The first approach is training-time defense, such as adversarial training, which can mitigate poisoning effects but is computationally intensive. The other approach is pre-training purification, e.g., image short squeezing, which consists of several simple compressions but often encounters challenges in dealing with various UEs. Our work provides a novel disentanglement mechanism to build an efficient pre-training purification method. Firstly, we uncover rate-constrained variational autoencoders (VAEs), demonstrating a clear tendency to suppress the perturbations in UEs. We subsequently conduct a theoretical analysis for this phenomenon. Building upon these insights, we introduce a disentangle variational autoencoder (D-VAE), capable of disentangling the perturbations with learnable class-wise embeddings. Based on this network, a two-stage purification approach is naturally developed. The first stage focuses on roughly eliminating perturbations, while the second stage produces refined, poison-free results, ensuring effectiveness and robustness across various scenarios. Extensive experiments demonstrate the remarkable performance of our method across CIFAR-10, CIFAR-100, and a 100-class ImageNet-subset. Code is available at https://github.com/yuyi-sd/D-VAE. | [
"['Yi Yu' 'Yufei Wang' 'Song Xia' 'Wenhan Yang' 'Shijian Lu' 'Yap-Peng Tan'\n 'Alex C. Kot']"
]
|
null | null | 2405.01462 | null | null | http://arxiv.org/pdf/2405.01462v1 | 2024-05-02T16:50:47Z | 2024-05-02T16:50:47Z | Uncertainty for Active Learning on Graphs | Uncertainty Sampling is an Active Learning strategy that aims to improve the data efficiency of machine learning models by iteratively acquiring labels of data points with the highest uncertainty. While it has proven effective for independent data its applicability to graphs remains under-explored. We propose the first extensive study of Uncertainty Sampling for node classification: (1) We benchmark Uncertainty Sampling beyond predictive uncertainty and highlight a significant performance gap to other Active Learning strategies. (2) We develop ground-truth Bayesian uncertainty estimates in terms of the data generating process and prove their effectiveness in guiding Uncertainty Sampling toward optimal queries. We confirm our results on synthetic data and design an approximate approach that consistently outperforms other uncertainty estimators on real datasets. (3) Based on this analysis, we relate pitfalls in modeling uncertainty to existing methods. Our analysis enables and informs the development of principled uncertainty estimation on graphs. | [
"['Dominik Fuchsgruber' 'Tom Wollschläger' 'Bertrand Charpentier'\n 'Antonio Oroz' 'Stephan Günnemann']"
]
|
null | null | 2405.01463 | null | null | http://arxiv.org/pdf/2405.01463v2 | 2024-05-13T20:42:46Z | 2024-05-02T16:52:09Z | Dynamic Local Average Treatment Effects | We consider Dynamic Treatment Regimes (DTRs) with One Sided Noncompliance that arise in applications such as digital recommendations and adaptive medical trials. These are settings where decision makers encourage individuals to take treatments over time, but adapt encouragements based on previous encouragements, treatments, states, and outcomes. Importantly, individuals may not comply with encouragements based on unobserved confounders. For settings with binary treatments and encouragements, we provide nonparametric identification, estimation, and inference for Dynamic Local Average Treatment Effects (LATEs), which are expected values of multiple time period treatment contrasts for the respective complier subpopulations. Under standard assumptions in the Instrumental Variable and DTR literature, we show that one can identify Dynamic LATEs that correspond to treating at single time steps. Under an additional cross-period effect-compliance independence assumption, which is satisfied in Staggered Adoption settings and a generalization of them, which we define as Staggered Compliance settings, we identify Dynamic LATEs for treating in multiple time periods. | [
"['Ravi B. Sojitra' 'Vasilis Syrgkanis']"
]
|
null | null | 2405.01468 | null | null | http://arxiv.org/pdf/2405.01468v1 | 2024-05-02T16:59:05Z | 2024-05-02T16:59:05Z | Understanding Retrieval-Augmented Task Adaptation for Vision-Language
Models | Pre-trained contrastive vision-language models have demonstrated remarkable performance across a wide range of tasks. However, they often struggle on fine-trained datasets with categories not adequately represented during pre-training, which makes adaptation necessary. Recent works have shown promising results by utilizing samples from web-scale databases for retrieval-augmented adaptation, especially in low-data regimes. Despite the empirical success, understanding how retrieval impacts the adaptation of vision-language models remains an open research question. In this work, we adopt a reflective perspective by presenting a systematic study to understand the roles of key components in retrieval-augmented adaptation. We unveil new insights on uni-modal and cross-modal retrieval and highlight the critical role of logit ensemble for effective adaptation. We further present theoretical underpinnings that directly support our empirical observations. | [
"['Yifei Ming' 'Yixuan Li']"
]
|
null | null | 2405.01480 | null | null | http://arxiv.org/pdf/2405.01480v1 | 2024-05-02T17:12:25Z | 2024-05-02T17:12:25Z | Common pitfalls to avoid while using multiobjective optimization in
machine learning | Recently, there has been an increasing interest in exploring the application of multiobjective optimization (MOO) in machine learning (ML). The interest is driven by the numerous situations in real-life applications where multiple objectives need to be optimized simultaneously. A key aspect of MOO is the existence of a Pareto set, rather than a single optimal solution, which illustrates the inherent trade-offs between objectives. Despite its potential, there is a noticeable lack of satisfactory literature that could serve as an entry-level guide for ML practitioners who want to use MOO. Hence, our goal in this paper is to produce such a resource. We critically review previous studies, particularly those involving MOO in deep learning (using Physics-Informed Neural Networks (PINNs) as a guiding example), and identify misconceptions that highlight the need for a better grasp of MOO principles in ML. Using MOO of PINNs as a case study, we demonstrate the interplay between the data loss and the physics loss terms. We highlight the most common pitfalls one should avoid while using MOO techniques in ML. We begin by establishing the groundwork for MOO, focusing on well-known approaches such as the weighted sum (WS) method, alongside more complex techniques like the multiobjective gradient descent algorithm (MGDA). Additionally, we compare the results obtained from the WS and MGDA with one of the most common evolutionary algorithms, NSGA-II. We emphasize the importance of understanding the specific problem, the objective space, and the selected MOO method, while also noting that neglecting factors such as convergence can result in inaccurate outcomes and, consequently, a non-optimal solution. Our goal is to offer a clear and practical guide for ML practitioners to effectively apply MOO, particularly in the context of DL. | [
"['Junaid Akhter' 'Paul David Fährmann' 'Konstantin Sonntag'\n 'Sebastian Peitz']"
]
|
null | null | 2405.01481 | null | null | http://arxiv.org/pdf/2405.01481v1 | 2024-05-02T17:13:40Z | 2024-05-02T17:13:40Z | NeMo-Aligner: Scalable Toolkit for Efficient Model Alignment | Aligning Large Language Models (LLMs) with human values and preferences is essential for making them helpful and safe. However, building efficient tools to perform alignment can be challenging, especially for the largest and most competent LLMs which often contain tens or hundreds of billions of parameters. We create NeMo-Aligner, a toolkit for model alignment that can efficiently scale to using hundreds of GPUs for training. NeMo-Aligner comes with highly optimized and scalable implementations for major paradigms of model alignment such as: Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO), SteerLM, and Self-Play Fine-Tuning (SPIN). Additionally, our toolkit supports running most of the alignment techniques in a Parameter Efficient Fine-Tuning (PEFT) setting. NeMo-Aligner is designed for extensibility, allowing support for other alignment techniques with minimal effort. It is open-sourced with Apache 2.0 License and we invite community contributions at https://github.com/NVIDIA/NeMo-Aligner | [
"['Gerald Shen' 'Zhilin Wang' 'Olivier Delalleau' 'Jiaqi Zeng' 'Yi Dong'\n 'Daniel Egert' 'Shengyang Sun' 'Jimmy Zhang' 'Sahil Jain'\n 'Ali Taghibakhshi' 'Markel Sanz Ausin' 'Ashwath Aithal'\n 'Oleksii Kuchaiev']"
]
|
null | null | 2405.01484 | null | null | http://arxiv.org/pdf/2405.01484v1 | 2024-05-02T17:15:30Z | 2024-05-02T17:15:30Z | Designing Algorithmic Recommendations to Achieve Human-AI
Complementarity | Algorithms frequently assist, rather than replace, human decision-makers. However, the design and analysis of algorithms often focus on predicting outcomes and do not explicitly model their effect on human decisions. This discrepancy between the design and role of algorithmic assistants becomes of particular concern in light of empirical evidence that suggests that algorithmic assistants again and again fail to improve human decisions. In this article, we formalize the design of recommendation algorithms that assist human decision-makers without making restrictive ex-ante assumptions about how recommendations affect decisions. We formulate an algorithmic-design problem that leverages the potential-outcomes framework from causal inference to model the effect of recommendations on a human decision-maker's binary treatment choice. Within this model, we introduce a monotonicity assumption that leads to an intuitive classification of human responses to the algorithm. Under this monotonicity assumption, we can express the human's response to algorithmic recommendations in terms of their compliance with the algorithm and the decision they would take if the algorithm sends no recommendation. We showcase the utility of our framework using an online experiment that simulates a hiring task. We argue that our approach explains the relative performance of different recommendation algorithms in the experiment, and can help design solutions that realize human-AI complementarity. | [
"['Bryce McLaughlin' 'Jann Spiess']"
]
|
null | null | 2405.01488 | null | null | http://arxiv.org/pdf/2405.01488v1 | 2024-05-02T17:23:04Z | 2024-05-02T17:23:04Z | Digital Twin Generators for Disease Modeling | A patient's digital twin is a computational model that describes the evolution of their health over time. Digital twins have the potential to revolutionize medicine by enabling individual-level computer simulations of human health, which can be used to conduct more efficient clinical trials or to recommend personalized treatment options. Due to the overwhelming complexity of human biology, machine learning approaches that leverage large datasets of historical patients' longitudinal health records to generate patients' digital twins are more tractable than potential mechanistic models. In this manuscript, we describe a neural network architecture that can learn conditional generative models of clinical trajectories, which we call Digital Twin Generators (DTGs), that can create digital twins of individual patients. We show that the same neural network architecture can be trained to generate accurate digital twins for patients across 13 different indications simply by changing the training set and tuning hyperparameters. By introducing a general purpose architecture, we aim to unlock the ability to scale machine learning approaches to larger datasets and across more indications so that a digital twin could be created for any patient in the world. | [
"['Nameyeh Alam' 'Jake Basilico' 'Daniele Bertolini' 'Satish Casie Chetty'\n \"Heather D'Angelo\" 'Ryan Douglas' 'Charles K. Fisher' 'Franklin Fuller'\n 'Melissa Gomes' 'Rishabh Gupta' 'Alex Lang' 'Anton Loukianov'\n 'Rachel Mak-McCully' 'Cary Murray' 'Hanalei Pham' 'Susanna Qiao'\n 'Elena Ryapolova-Webb' 'Aaron Smith' 'Dimitri Theoharatos' 'Anil Tolwani'\n 'Eric W. Tramel' 'Anna Vidovszky' 'Judy Viduya' 'Jonathan R. Walsh']"
]
|
null | null | 2405.01491 | null | null | http://arxiv.org/pdf/2405.01491v2 | 2024-05-06T15:45:46Z | 2024-05-02T17:25:32Z | FeNNol: an Efficient and Flexible Library for Building
Force-field-enhanced Neural Network Potentials | Neural network interatomic potentials (NNPs) have recently proven to be powerful tools to accurately model complex molecular systems while bypassing the high numerical cost of ab-initio molecular dynamics simulations. In recent years, numerous advances in model architectures as well as the development of hybrid models combining machine-learning (ML) with more traditional, physically-motivated, force-field interactions have considerably increased the design space of ML potentials. In this paper, we present FeNNol, a new library for building, training and running force-field-enhanced neural network potentials. It provides a flexible and modular system for building hybrid models, allowing to easily combine state-of-the-art embeddings with ML-parameterized physical interaction terms without the need for explicit programming. Furthermore, FeNNol leverages the automatic differentiation and just-in-time compilation features of the Jax Python library to enable fast evaluation of NNPs, shrinking the performance gap between ML potentials and standard force-fields. This is demonstrated with the popular ANI-2x model reaching simulation speeds nearly on par with the AMOEBA polarizable force-field on commodity GPUs (GPU=Graphics processing unit). We hope that FeNNol will facilitate the development and application of new hybrid NNP architectures for a wide range of molecular simulation problems. | [
"['Thomas Plé' 'Olivier Adjoua' 'Louis Lagardère' 'Jean-Philip Piquemal']"
]
|
null | null | 2405.01494 | null | null | http://arxiv.org/pdf/2405.01494v1 | 2024-05-02T17:26:52Z | 2024-05-02T17:26:52Z | Navigating Heterogeneity and Privacy in One-Shot Federated Learning with
Diffusion Models | Federated learning (FL) enables multiple clients to train models collectively while preserving data privacy. However, FL faces challenges in terms of communication cost and data heterogeneity. One-shot federated learning has emerged as a solution by reducing communication rounds, improving efficiency, and providing better security against eavesdropping attacks. Nevertheless, data heterogeneity remains a significant challenge, impacting performance. This work explores the effectiveness of diffusion models in one-shot FL, demonstrating their applicability in addressing data heterogeneity and improving FL performance. Additionally, we investigate the utility of our diffusion model approach, FedDiff, compared to other one-shot FL methods under differential privacy (DP). Furthermore, to improve generated sample quality under DP settings, we propose a pragmatic Fourier Magnitude Filtering (FMF) method, enhancing the effectiveness of generated data for global model training. | [
"['Matias Mendieta' 'Guangyu Sun' 'Chen Chen']"
]
|
null | null | 2405.01502 | null | null | http://arxiv.org/pdf/2405.01502v1 | 2024-05-02T17:32:59Z | 2024-05-02T17:32:59Z | Analyzing the Role of Semantic Representations in the Era of Large
Language Models | Traditionally, natural language processing (NLP) models often use a rich set of features created by linguistic expertise, such as semantic representations. However, in the era of large language models (LLMs), more and more tasks are turned into generic, end-to-end sequence generation problems. In this paper, we investigate the question: what is the role of semantic representations in the era of LLMs? Specifically, we investigate the effect of Abstract Meaning Representation (AMR) across five diverse NLP tasks. We propose an AMR-driven chain-of-thought prompting method, which we call AMRCoT, and find that it generally hurts performance more than it helps. To investigate what AMR may have to offer on these tasks, we conduct a series of analysis experiments. We find that it is difficult to predict which input examples AMR may help or hurt on, but errors tend to arise with multi-word expressions, named entities, and in the final inference step where the LLM must connect its reasoning over the AMR to its prediction. We recommend focusing on these areas for future work in semantic representations for LLMs. Our code: https://github.com/causalNLP/amr_llm. | [
"['Zhijing Jin' 'Yuen Chen' 'Fernando Gonzalez' 'Jiarui Liu' 'Jiayi Zhang'\n 'Julian Michael' 'Bernhard Schölkopf' 'Mona Diab']"
]
|
null | null | 2405.01507 | null | null | http://arxiv.org/pdf/2405.01507v3 | 2024-05-07T17:12:13Z | 2024-05-02T17:37:39Z | Accelerating Convergence in Bayesian Few-Shot Classification | Bayesian few-shot classification has been a focal point in the field of few-shot learning. This paper seamlessly integrates mirror descent-based variational inference into Gaussian process-based few-shot classification, addressing the challenge of non-conjugate inference. By leveraging non-Euclidean geometry, mirror descent achieves accelerated convergence by providing the steepest descent direction along the corresponding manifold. It also exhibits the parameterization invariance property concerning the variational distribution. Experimental results demonstrate competitive classification accuracy, improved uncertainty quantification, and faster convergence compared to baseline models. Additionally, we investigate the impact of hyperparameters and components. Code is publicly available at https://github.com/keanson/MD-BSFC. | [
"['Tianjun Ke' 'Haoqun Cao' 'Feng Zhou']"
]
|
null | null | 2405.01521 | null | null | http://arxiv.org/pdf/2405.01521v1 | 2024-05-02T17:50:53Z | 2024-05-02T17:50:53Z | Transformer-Aided Semantic Communications | The transformer structure employed in large language models (LLMs), as a specialized category of deep neural networks (DNNs) featuring attention mechanisms, stands out for their ability to identify and highlight the most relevant aspects of input data. Such a capability is particularly beneficial in addressing a variety of communication challenges, notably in the realm of semantic communication where proper encoding of the relevant data is critical especially in systems with limited bandwidth. In this work, we employ vision transformers specifically for the purpose of compression and compact representation of the input image, with the goal of preserving semantic information throughout the transmission process. Through the use of the attention mechanism inherent in transformers, we create an attention mask. This mask effectively prioritizes critical segments of images for transmission, ensuring that the reconstruction phase focuses on key objects highlighted by the mask. Our methodology significantly improves the quality of semantic communication and optimizes bandwidth usage by encoding different parts of the data in accordance with their semantic information content, thus enhancing overall efficiency. We evaluate the effectiveness of our proposed framework using the TinyImageNet dataset, focusing on both reconstruction quality and accuracy. Our evaluation results demonstrate that our framework successfully preserves semantic information, even when only a fraction of the encoded data is transmitted, according to the intended compression rates. | [
"['Matin Mortaheb' 'Erciyes Karakaya' 'Mohammad A. Amir Khojastepour'\n 'Sennur Ulukus']"
]
|
null | null | 2405.01524 | null | null | http://arxiv.org/pdf/2405.01524v2 | 2024-05-03T16:03:57Z | 2024-05-02T17:54:35Z | A separability-based approach to quantifying generalization: which layer
is best? | Generalization to unseen data remains poorly understood for deep learning classification and foundation models. How can one assess the ability of networks to adapt to new or extended versions of their input space in the spirit of few-shot learning, out-of-distribution generalization, and domain adaptation? Which layers of a network are likely to generalize best? We provide a new method for evaluating the capacity of networks to represent a sampled domain, regardless of whether the network has been trained on all classes in the domain. Our approach is the following: after fine-tuning state-of-the-art pre-trained models for visual classification on a particular domain, we assess their performance on data from related but distinct variations in that domain. Generalization power is quantified as a function of the latent embeddings of unseen data from intermediate layers for both unsupervised and supervised settings. Working throughout all stages of the network, we find that (i) high classification accuracy does not imply high generalizability; and (ii) deeper layers in a model do not always generalize the best, which has implications for pruning. Since the trends observed across datasets are largely consistent, we conclude that our approach reveals (a function of) the intrinsic capacity of the different layers of a model to generalize. | [
"['Luciano Dyballa' 'Evan Gerritz' 'Steven W. Zucker']"
]
|
null | null | 2405.01531 | null | null | http://arxiv.org/pdf/2405.01531v1 | 2024-05-02T17:59:01Z | 2024-05-02T17:59:01Z | Improving Intervention Efficacy via Concept Realignment in Concept
Bottleneck Models | Concept Bottleneck Models (CBMs) ground image classification on human-understandable concepts to allow for interpretable model decisions. Crucially, the CBM design inherently allows for human interventions, in which expert users are given the ability to modify potentially misaligned concept choices to influence the decision behavior of the model in an interpretable fashion. However, existing approaches often require numerous human interventions per image to achieve strong performances, posing practical challenges in scenarios where obtaining human feedback is expensive. In this paper, we find that this is noticeably driven by an independent treatment of concepts during intervention, wherein a change of one concept does not influence the use of other ones in the model's final decision. To address this issue, we introduce a trainable concept intervention realignment module, which leverages concept relations to realign concept assignments post-intervention. Across standard, real-world benchmarks, we find that concept realignment can significantly improve intervention efficacy; significantly reducing the number of interventions needed to reach a target classification performance or concept prediction accuracy. In addition, it easily integrates into existing concept-based architectures without requiring changes to the models themselves. This reduced cost of human-model collaboration is crucial to enhancing the feasibility of CBMs in resource-constrained environments. | [
"['Nishad Singhi' 'Jae Myung Kim' 'Karsten Roth' 'Zeynep Akata']"
]
|
null | null | 2405.01534 | null | null | http://arxiv.org/pdf/2405.01534v1 | 2024-05-02T17:59:31Z | 2024-05-02T17:59:31Z | Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon
Robotics Tasks | Large Language Models (LLMs) have been shown to be capable of performing high-level planning for long-horizon robotics tasks, yet existing methods require access to a pre-defined skill library (e.g. picking, placing, pulling, pushing, navigating). However, LLM planning does not address how to design or learn those behaviors, which remains challenging particularly in long-horizon settings. Furthermore, for many tasks of interest, the robot needs to be able to adjust its behavior in a fine-grained manner, requiring the agent to be capable of modifying low-level control actions. Can we instead use the internet-scale knowledge from LLMs for high-level policies, guiding reinforcement learning (RL) policies to efficiently solve robotic control tasks online without requiring a pre-determined set of skills? In this paper, we propose Plan-Seq-Learn (PSL): a modular approach that uses motion planning to bridge the gap between abstract language and learned low-level control for solving long-horizon robotics tasks from scratch. We demonstrate that PSL achieves state-of-the-art results on over 25 challenging robotics tasks with up to 10 stages. PSL solves long-horizon tasks from raw visual input spanning four benchmarks at success rates of over 85%, out-performing language-based, classical, and end-to-end approaches. Video results and code at https://mihdalal.github.io/planseqlearn/ | [
"['Murtaza Dalal' 'Tarun Chiruvolu' 'Devendra Chaplot'\n 'Ruslan Salakhutdinov']"
]
|
null | null | 2405.01536 | null | null | http://arxiv.org/pdf/2405.01536v1 | 2024-05-02T17:59:52Z | 2024-05-02T17:59:52Z | Customizing Text-to-Image Models with a Single Image Pair | Art reinterpretation is the practice of creating a variation of a reference work, making a paired artwork that exhibits a distinct artistic style. We ask if such an image pair can be used to customize a generative model to capture the demonstrated stylistic difference. We propose Pair Customization, a new customization method that learns stylistic difference from a single image pair and then applies the acquired style to the generation process. Unlike existing methods that learn to mimic a single concept from a collection of images, our method captures the stylistic difference between paired images. This allows us to apply a stylistic change without overfitting to the specific image content in the examples. To address this new task, we employ a joint optimization method that explicitly separates the style and content into distinct LoRA weight spaces. We optimize these style and content weights to reproduce the style and content images while encouraging their orthogonality. During inference, we modify the diffusion process via a new style guidance based on our learned weights. Both qualitative and quantitative experiments show that our method can effectively learn style while avoiding overfitting to image content, highlighting the potential of modeling such stylistic differences from a single image pair. | [
"['Maxwell Jones' 'Sheng-Yu Wang' 'Nupur Kumari' 'David Bau' 'Jun-Yan Zhu']"
]
|
null | null | 2405.01538 | null | null | http://arxiv.org/pdf/2405.01538v1 | 2024-05-02T17:59:57Z | 2024-05-02T17:59:57Z | Multi-Space Alignments Towards Universal LiDAR Segmentation | A unified and versatile LiDAR segmentation model with strong robustness and generalizability is desirable for safe autonomous driving perception. This work presents M3Net, a one-of-a-kind framework for fulfilling multi-task, multi-dataset, multi-modality LiDAR segmentation in a universal manner using just a single set of parameters. To better exploit data volume and diversity, we first combine large-scale driving datasets acquired by different types of sensors from diverse scenes and then conduct alignments in three spaces, namely data, feature, and label spaces, during the training. As a result, M3Net is capable of taming heterogeneous data for training state-of-the-art LiDAR segmentation models. Extensive experiments on twelve LiDAR segmentation datasets verify our effectiveness. Notably, using a shared set of parameters, M3Net achieves 75.1%, 83.1%, and 72.4% mIoU scores, respectively, on the official benchmarks of SemanticKITTI, nuScenes, and Waymo Open. | [
"['Youquan Liu' 'Lingdong Kong' 'Xiaoyang Wu' 'Runnan Chen' 'Xin Li'\n 'Liang Pan' 'Ziwei Liu' 'Yuexin Ma']"
]
|
null | null | 2405.01540 | null | null | http://arxiv.org/pdf/2405.01540v1 | 2024-02-02T00:07:15Z | 2024-02-02T00:07:15Z | Universal Imitation Games | Alan Turing proposed in 1950 a framework called an imitation game to decide if a machine could think. Using mathematics developed largely after Turing -- category theory -- we analyze a broader class of universal imitation games (UIGs), which includes static, dynamic, and evolutionary games. In static games, the participants are in a steady state. In dynamic UIGs, "learner" participants are trying to imitate "teacher" participants over the long run. In evolutionary UIGs, the participants are competing against each other in an evolutionary game, and participants can go extinct and be replaced by others with higher fitness. We use the framework of category theory -- in particular, two influential results by Yoneda -- to characterize each type of imitation game. Universal properties in categories are defined by initial and final objects. We characterize dynamic UIGs where participants are learning by inductive inference as initial algebras over well-founded sets, and contrast them with participants learning by conductive inference over the final coalgebra of non-well-founded sets. We briefly discuss the extension of our categorical framework for UIGs to imitation games on quantum computers. | [
"['Sridhar Mahadevan']"
]
|
null | null | 2405.01554 | null | null | http://arxiv.org/pdf/2405.01554v1 | 2024-03-16T15:10:50Z | 2024-03-16T15:10:50Z | Early-stage detection of cognitive impairment by hybrid
quantum-classical algorithm using resting-state functional MRI time-series | Following the recent development of quantum machine learning techniques, the literature has reported several quantum machine learning algorithms for disease detection. This study explores the application of a hybrid quantum-classical algorithm for classifying region-of-interest time-series data obtained from resting-state functional magnetic resonance imaging in patients with early-stage cognitive impairment based on the importance of cognitive decline for dementia or aging. Classical one-dimensional convolutional layers are used together with quantum convolutional neural networks in our hybrid algorithm. In the classical simulation, the proposed hybrid algorithms showed higher balanced accuracies than classical convolutional neural networks under the similar training conditions. Moreover, a total of nine brain regions (left precentral gyrus, right superior temporal gyrus, left rolandic operculum, right rolandic operculum, left parahippocampus, right hippocampus, left medial frontal gyrus, right cerebellum crus, and cerebellar vermis) among 116 brain regions were found to be relatively effective brain regions for the classification based on the model performances. The associations of the selected nine regions with cognitive decline, as found in previous studies, were additionally validated through seed-based functional connectivity analysis. We confirmed both the improvement of model performance with the quantum convolutional neural network and neuroscientific validities of brain regions from our hybrid quantum-classical model. | [
"['Junggu Choi' 'Tak Hur' 'Daniel K. Park' 'Na-Young Shin' 'Seung-Koo Lee'\n 'Hakbae Lee' 'Sanghoon Han']"
]
|
null | null | 2405.01557 | null | null | http://arxiv.org/pdf/2405.01557v2 | 2024-06-24T16:08:51Z | 2024-03-22T13:08:22Z | An Experimental Study on the Rashomon Effect of Balancing Methods in
Imbalanced Classification | Predictive models may generate biased predictions when classifying imbalanced datasets. This happens when the model favors the majority class, leading to low performance in accurately predicting the minority class. To address this issue, balancing or resampling methods are critical pre-processing steps in the modeling process. However, there have been debates and questioning of the functionality of these methods in recent years. In particular, many candidate models may exhibit very similar predictive performance, which is called the Rashomon effect, in model selection. Selecting one of them without considering predictive multiplicity which is the case of yielding conflicting models' predictions for any sample may lead to a loss of using another model. In this study, in addition to the existing debates, the impact of balancing methods on predictive multiplicity is examined through the Rashomon effect. It is important because the blind model selection is risky from a set of approximately equally accurate models. This may lead to serious problems in model selection, validation, and explanation. To tackle this matter, we conducted real dataset experiments to observe the impact of balancing methods on predictive multiplicity through the Rashomon effect. Our findings showed that balancing methods inflate the predictive multiplicity, and they yield varying results. To monitor the trade-off between performance and predictive multiplicity for conducting the modeling process responsibly, we proposed using the extended performance-gain plot for the Rashomon effect. | [
"['Mustafa Cavus' 'Przemysław Biecek']"
]
|
null | null | 2405.01558 | null | null | http://arxiv.org/pdf/2405.01558v2 | 2024-05-06T09:02:58Z | 2024-03-24T13:57:30Z | Configurable Learned Holography | In the pursuit of advancing holographic display technology, we face a unique yet persistent roadblock: the inflexibility of learned holography in adapting to various hardware configurations. This is due to the variances in the complex optical components and system settings in existing holographic displays. Although the emerging learned approaches have enabled rapid and high-quality hologram generation, any alteration in display hardware still requires a retraining of the model. Our work introduces a configurable learned model that interactively computes 3D holograms from RGB-only 2D images for a variety of holographic displays. The model can be conditioned to predefined hardware parameters of existing holographic displays such as working wavelengths, pixel pitch, propagation distance, and peak brightness without having to retrain. In addition, our model accommodates various hologram types, including conventional single-color and emerging multi-color holograms that simultaneously use multiple color primaries in holographic displays. Notably, we enabled our hologram computations to rely on identifying the correlation between depth estimation and 3D hologram synthesis tasks within the learning domain for the first time in the literature. We employ knowledge distillation via a student-teacher learning strategy to streamline our model for interactive performance. Achieving up to a 2x speed improvement compared to state-of-the-art models while consistently generating high-quality 3D holograms with different hardware configurations. | [
"['Yicheng Zhan' 'Liang Shi' 'Wojciech Matusik' 'Qi Sun' 'Kaan Akşit']"
]
|
null | null | 2405.01559 | null | null | http://arxiv.org/pdf/2405.01559v1 | 2024-03-26T18:53:17Z | 2024-03-26T18:53:17Z | Untangling Knots: Leveraging LLM for Error Resolution in Computational
Notebooks | Computational notebooks became indispensable tools for research-related development, offering unprecedented interactivity and flexibility in the development process. However, these benefits come at the cost of reproducibility and an increased potential for bugs. There are many tools for bug fixing; however, they are generally targeted at the classical linear code. With the rise of code-fluent Large Language Models, a new stream of smart bug-fixing tools has emerged. However, the applicability of those tools is still problematic for non-linear computational notebooks. In this paper, we propose a potential solution for resolving errors in computational notebooks via an iterative LLM-based agent. We discuss the questions raised by this approach and share a novel dataset of computational notebooks containing bugs to facilitate the research of the proposed approach. | [
"['Konstantin Grotov' 'Sergey Titov' 'Yaroslav Zharov' 'Timofey Bryksin']"
]
|
null | null | 2405.01563 | null | null | http://arxiv.org/pdf/2405.01563v1 | 2024-04-04T11:32:03Z | 2024-04-04T11:32:03Z | Mitigating LLM Hallucinations via Conformal Abstention | We develop a principled procedure for determining when a large language model (LLM) should abstain from responding (e.g., by saying "I don't know") in a general domain, instead of resorting to possibly "hallucinating" a non-sensical or incorrect answer. Building on earlier approaches that use self-consistency as a more reliable measure of model confidence, we propose using the LLM itself to self-evaluate the similarity between each of its sampled responses for a given query. We then further leverage conformal prediction techniques to develop an abstention procedure that benefits from rigorous theoretical guarantees on the hallucination rate (error rate). Experimentally, our resulting conformal abstention method reliably bounds the hallucination rate on various closed-book, open-domain generative question answering datasets, while also maintaining a significantly less conservative abstention rate on a dataset with long responses (Temporal Sequences) compared to baselines using log-probability scores to quantify uncertainty, while achieveing comparable performance on a dataset with short answers (TriviaQA). To evaluate the experiments automatically, one needs to determine if two responses are equivalent given a question. Following standard practice, we use a thresholded similarity function to determine if two responses match, but also provide a method for calibrating the threshold based on conformal prediction, with theoretical guarantees on the accuracy of the match prediction, which might be of independent interest. | [
"['Yasin Abbasi Yadkori' 'Ilja Kuzborskij' 'David Stutz' 'András György'\n 'Adam Fisch' 'Arnaud Doucet' 'Iuliya Beloshapka' 'Wei-Hung Weng'\n 'Yao-Yuan Yang' 'Csaba Szepesvári' 'Ali Taylan Cemgil' 'Nenad Tomasev']"
]
|
null | null | 2405.01576 | null | null | http://arxiv.org/pdf/2405.01576v1 | 2024-04-25T17:29:53Z | 2024-04-25T17:29:53Z | Uncovering Deceptive Tendencies in Language Models: A Simulated Company
AI Assistant | We study the tendency of AI systems to deceive by constructing a realistic simulation setting of a company AI assistant. The simulated company employees provide tasks for the assistant to complete, these tasks spanning writing assistance, information retrieval and programming. We then introduce situations where the model might be inclined to behave deceptively, while taking care to not instruct or otherwise pressure the model to do so. Across different scenarios, we find that Claude 3 Opus 1) complies with a task of mass-generating comments to influence public perception of the company, later deceiving humans about it having done so, 2) lies to auditors when asked questions, and 3) strategically pretends to be less capable than it is during capability evaluations. Our work demonstrates that even models trained to be helpful, harmless and honest sometimes behave deceptively in realistic scenarios, without notable external pressure to do so. | [
"['Olli Järviniemi' 'Evan Hubinger']"
]
|
null | null | 2405.01577 | null | null | http://arxiv.org/pdf/2405.01577v1 | 2024-04-26T05:29:35Z | 2024-04-26T05:29:35Z | HateTinyLLM : Hate Speech Detection Using Tiny Large Language Models | Hate speech encompasses verbal, written, or behavioral communication that targets derogatory or discriminatory language against individuals or groups based on sensitive characteristics. Automated hate speech detection plays a crucial role in curbing its propagation, especially across social media platforms. Various methods, including recent advancements in deep learning, have been devised to address this challenge. In this study, we introduce HateTinyLLM, a novel framework based on fine-tuned decoder-only tiny large language models (tinyLLMs) for efficient hate speech detection. Our experimental findings demonstrate that the fine-tuned HateTinyLLM outperforms the pretrained mixtral-7b model by a significant margin. We explored various tiny LLMs, including PY007/TinyLlama-1.1B-step-50K-105b, Microsoft/phi-2, and facebook/opt-1.3b, and fine-tuned them using LoRA and adapter methods. Our observations indicate that all LoRA-based fine-tuned models achieved over 80% accuracy. | [
"['Tanmay Sen' 'Ansuman Das' 'Mrinmay Sen']"
]
|
null | null | 2405.01579 | null | null | http://arxiv.org/pdf/2405.01579v1 | 2024-04-26T14:03:19Z | 2024-04-26T14:03:19Z | Mining patterns in syntax trees to automate code reviews of student
solutions for programming exercises | In programming education, providing manual feedback is essential but labour-intensive, posing challenges in consistency and timeliness. We introduce ECHO, a machine learning method to automate the reuse of feedback in educational code reviews by analysing patterns in abstract syntax trees. This study investigates two primary questions: whether ECHO can predict feedback annotations to specific lines of student code based on previously added annotations by human reviewers (RQ1), and whether its training and prediction speeds are suitable for using ECHO for real-time feedback during live code reviews by human reviewers (RQ2). Our results, based on annotations from both automated linting tools and human reviewers, show that ECHO can accurately and quickly predict appropriate feedback annotations. Its efficiency in processing and its flexibility in adapting to feedback patterns can significantly reduce the time and effort required for manual feedback provisioning in educational settings. | [
"['Charlotte Van Petegem' 'Kasper Demeyere' 'Rien Maertens' 'Niko Strijbol'\n 'Bram De Wever' 'Bart Mesuere' 'Peter Dawyndt']"
]
|
null | null | 2405.01582 | null | null | http://arxiv.org/pdf/2405.01582v3 | 2024-05-10T23:35:53Z | 2024-04-26T18:01:25Z | Text Quality-Based Pruning for Efficient Training of Language Models | In recent times training Language Models (LMs) have relied on computationally heavy training over massive datasets which makes this training process extremely laborious. In this paper we propose a novel method for numerically evaluating text quality in large unlabelled NLP datasets in a model agnostic manner to assign the text instances a "quality score". By proposing the text quality metric, the paper establishes a framework to identify and eliminate low-quality text instances, leading to improved training efficiency for LM models. Experimental results over multiple models and datasets demonstrate the efficacy of this approach, showcasing substantial gains in training effectiveness and highlighting the potential for resource-efficient LM training. For example, we observe an absolute accuracy improvement of 0.9% averaged over 14 downstream evaluation tasks for multiple LM models while using 40% lesser data and training 42% faster when training on the OpenWebText dataset and 0.8% average absolute accuracy improvement while using 20% lesser data and training 21% faster on the Wikipedia dataset. | [
"['Vasu Sharma' 'Karthik Padthe' 'Newsha Ardalani' 'Kushal Tirumala'\n 'Russell Howes' 'Hu Xu' 'Po-Yao Huang' 'Shang-Wen Li' 'Armen Aghajanyan'\n 'Gargi Ghosh' 'Luke Zettlemoyer']"
]
|
null | null | 2405.01583 | null | null | http://arxiv.org/pdf/2405.01583v1 | 2024-04-27T20:03:47Z | 2024-04-27T20:03:47Z | MediFact at MEDIQA-M3G 2024: Medical Question Answering in Dermatology
with Multimodal Learning | The MEDIQA-M3G 2024 challenge necessitates novel solutions for Multilingual & Multimodal Medical Answer Generation in dermatology (wai Yim et al., 2024a). This paper addresses the limitations of traditional methods by proposing a weakly supervised learning approach for open-ended medical question-answering (QA). Our system leverages readily available MEDIQA-M3G images via a VGG16-CNN-SVM model, enabling multilingual (English, Chinese, Spanish) learning of informative skin condition representations. Using pre-trained QA models, we further bridge the gap between visual and textual information through multimodal fusion. This approach tackles complex, open-ended questions even without predefined answer choices. We empower the generation of comprehensive answers by feeding the ViT-CLIP model with multiple responses alongside images. This work advances medical QA research, paving the way for clinical decision support systems and ultimately improving healthcare delivery. | [
"['Nadia Saeed']"
]
|
null | null | 2405.01584 | null | null | http://arxiv.org/pdf/2405.01584v1 | 2024-04-28T10:11:52Z | 2024-04-28T10:11:52Z | Lightweight Conceptual Dictionary Learning for Text Classification Using
Information Compression | We propose a novel, lightweight supervised dictionary learning framework for text classification based on data compression and representation. This two-phase algorithm initially employs the Lempel-Ziv-Welch (LZW) algorithm to construct a dictionary from text datasets, focusing on the conceptual significance of dictionary elements. Subsequently, dictionaries are refined considering label data, optimizing dictionary atoms to enhance discriminative power based on mutual information and class distribution. This process generates discriminative numerical representations, facilitating the training of simple classifiers such as SVMs and neural networks. We evaluate our algorithm's information-theoretic performance using information bottleneck principles and introduce the information plane area rank (IPAR) as a novel metric to quantify the information-theoretic performance. Tested on six benchmark text datasets, our algorithm competes closely with top models, especially in limited-vocabulary contexts, using significantly fewer parameters. review{Our algorithm closely matches top-performing models, deviating by only ~2% on limited-vocabulary datasets, using just 10% of their parameters. However, it falls short on diverse-vocabulary datasets, likely due to the LZW algorithm's constraints with low-repetition data. This contrast highlights its efficiency and limitations across different dataset types. | [
"['Li Wan' 'Tansu Alpcan' 'Margreta Kuijper' 'Emanuele Viterbo']"
]
|
null | null | 2405.01587 | null | null | http://arxiv.org/abs/2405.01587v1 | 2024-04-28T19:11:08Z | 2024-04-28T19:11:08Z | Improve Academic Query Resolution through BERT-based Question Extraction
from Images | Providing fast and accurate resolution to the student's query is an essential solution provided by Edtech organizations. This is generally provided with a chat-bot like interface to enable students to ask their doubts easily. One preferred format for student queries is images, as it allows students to capture and post questions without typing complex equations and information. However, this format also presents difficulties, as images may contain multiple questions or textual noise that lowers the accuracy of existing single-query answering solutions. In this paper, we propose a method for extracting questions from text or images using a BERT-based deep learning model and compare it to the other rule-based and layout-based methods. Our method aims to improve the accuracy and efficiency of student query resolution in Edtech organizations. | [
"['Nidhi Kamal' 'Saurabh Yadav' 'Jorawar Singh' 'Aditi Avasthi']"
]
|
null | null | 2405.01600 | null | null | http://arxiv.org/pdf/2405.01600v1 | 2024-05-01T06:05:13Z | 2024-05-01T06:05:13Z | Deep Learning Descriptor Hybridization with Feature Reduction for
Accurate Cervical Cancer Colposcopy Image Classification | Cervical cancer stands as a predominant cause of female mortality, underscoring the need for regular screenings to enable early diagnosis and preemptive treatment of pre-cancerous conditions. The transformation zone in the cervix, where cellular differentiation occurs, plays a critical role in the detection of abnormalities. Colposcopy has emerged as a pivotal tool in cervical cancer prevention since it provides a meticulous examination of cervical abnormalities. However, challenges in visual evaluation necessitate the development of Computer Aided Diagnosis (CAD) systems. We propose a novel CAD system that combines the strengths of various deep-learning descriptors (ResNet50, ResNet101, and ResNet152) with appropriate feature normalization (min-max) as well as feature reduction technique (LDA). The combination of different descriptors ensures that all the features (low-level like edges and colour, high-level like shape and texture) are captured, feature normalization prevents biased learning, and feature reduction avoids overfitting. We do experiments on the IARC dataset provided by WHO. The dataset is initially segmented and balanced. Our approach achieves exceptional performance in the range of 97%-100% for both the normal-abnormal and the type classification. A competitive approach for type classification on the same dataset achieved 81%-91% performance. | [
"['Saurabh Saini' 'Kapil Ahuja' 'Siddartha Chennareddy'\n 'Karthik Boddupalli']"
]
|
null | null | 2405.01601 | null | null | http://arxiv.org/pdf/2405.01601v1 | 2024-05-01T08:55:16Z | 2024-05-01T08:55:16Z | Efficient Sample-Specific Encoder Perturbations | Encoder-decoder foundation models have displayed state-of-the-art performance on a range of autoregressive sequence tasks. This paper proposes a simple and lightweight modification to such systems to control the behaviour according to a specific attribute of interest. This paper proposes a novel inference-efficient approach to modifying the behaviour of an encoder-decoder system according to a specific attribute of interest. Specifically, we show that a small proxy network can be used to find a sample-by-sample perturbation of the encoder output of a frozen foundation model to trigger the decoder to generate improved decodings. This work explores a specific realization of this framework focused on improving the COMET performance of Flan-T5 on Machine Translation and the WER of Whisper foundation models on Speech Recognition. Results display consistent improvements in performance evaluated through COMET and WER respectively. Furthermore, experiments also show that the proxies are robust to the exact nature of the data used to train them and can extend to other domains. | [
"['Yassir Fathullah' 'Mark J. F. Gales']"
]
|
null | null | 2405.01603 | null | null | http://arxiv.org/pdf/2405.01603v1 | 2024-05-01T21:58:04Z | 2024-05-01T21:58:04Z | KITE: A Kernel-based Improved Transferability Estimation Method | Transferability estimation has emerged as an important problem in transfer learning. A transferability estimation method takes as inputs a set of pre-trained models and decides which pre-trained model can deliver the best transfer learning performance. Existing methods tackle this problem by analyzing the output of the pre-trained model or by comparing the pre-trained model with a probe model trained on the target dataset. However, neither is sufficient to provide reliable and efficient transferability estimations. In this paper, we present a novel perspective and introduce Kite, as a Kernel-based Improved Transferability Estimation method. Kite is based on the key observations that the separability of the pre-trained features and the similarity of the pre-trained features to random features are two important factors for estimating transferability. Inspired by kernel methods, Kite adopts centered kernel alignment as an effective way to assess feature separability and feature similarity. Kite is easy to interpret, fast to compute, and robust to the target dataset size. We evaluate the performance of Kite on a recently introduced large-scale model selection benchmark. The benchmark contains 8 source dataset, 6 target datasets and 4 architectures with a total of 32 pre-trained models. Extensive results show that Kite outperforms existing methods by a large margin for transferability estimation. | [
"['Yunhui Guo']"
]
|
null | null | 2405.01604 | null | null | http://arxiv.org/pdf/2405.01604v1 | 2024-05-01T22:28:55Z | 2024-05-01T22:28:55Z | Portfolio Management using Deep Reinforcement Learning | Algorithmic trading or Financial robots have been conquering the stock markets with their ability to fathom complex statistical trading strategies. But with the recent development of deep learning technologies, these strategies are becoming impotent. The DQN and A2C models have previously outperformed eminent humans in game-playing and robotics. In our work, we propose a reinforced portfolio manager offering assistance in the allocation of weights to assets. The environment proffers the manager the freedom to go long and even short on the assets. The weight allocation advisements are restricted to the choice of portfolio assets and tested empirically to knock benchmark indices. The manager performs financial transactions in a postulated liquid market without any transaction charges. This work provides the conclusion that the proposed portfolio manager with actions centered on weight allocations can surpass the risk-adjusted returns of conventional portfolio managers. | [
"['Ashish Anil Pawar' 'Vishnureddy Prashant Muskawar' 'Ritesh Tiku']"
]
|
null | null | 2405.01606 | null | null | http://arxiv.org/pdf/2405.01606v1 | 2024-05-02T00:57:23Z | 2024-05-02T00:57:23Z | Improving Trainability of Variational Quantum Circuits via
Regularization Strategies | In the era of noisy intermediate-scale quantum (NISQ), variational quantum circuits (VQCs) have been widely applied in various domains, advancing the superiority of quantum circuits against classic models. Similar to classic models, regular VQCs can be optimized by various gradient-based methods. However, the optimization may be initially trapped in barren plateaus or eventually entangled in saddle points during training. These gradient issues can significantly undermine the trainability of VQC. In this work, we propose a strategy that regularizes model parameters with prior knowledge of the train data and Gaussian noise diffusion. We conduct ablation studies to verify the effectiveness of our strategy across four public datasets and demonstrate that our method can improve the trainability of VQCs against the above-mentioned gradient issues. | [
"['Jun Zhuang' 'Jack Cunningham' 'Chaowen Guan']"
]
|
null | null | 2405.01607 | null | null | http://arxiv.org/pdf/2405.01607v1 | 2024-05-02T04:53:42Z | 2024-05-02T04:53:42Z | Wildfire Risk Prediction: A Review | Wildfires have significant impacts on global vegetation, wildlife, and humans. They destroy plant communities and wildlife habitats and contribute to increased emissions of carbon dioxide, nitrogen oxides, methane, and other pollutants. The prediction of wildfires relies on various independent variables combined with regression or machine learning methods. In this technical review, we describe the options for independent variables, data processing techniques, models, independent variables collinearity and importance estimation methods, and model performance evaluation metrics. First, we divide the independent variables into 4 aspects, including climate and meteorology conditions, socio-economical factors, terrain and hydrological features, and wildfire historical records. Second, preprocessing methods are described for different magnitudes, different spatial-temporal resolutions, and different formats of data. Third, the collinearity and importance evaluation methods of independent variables are also considered. Fourth, we discuss the application of statistical models, traditional machine learning models, and deep learning models in wildfire risk prediction. In this subsection, compared with other reviews, this manuscript particularly discusses the evaluation metrics and recent advancements in deep learning methods. Lastly, addressing the limitations of current research, this paper emphasizes the need for more effective deep learning time series forecasting algorithms, the utilization of three-dimensional data including ground and trunk fuel, extraction of more accurate historical fire point data, and improved model evaluation metrics. | [
"['Zhengsen Xu' 'Jonathan Li' 'Linlin Xu']"
]
|
null | null | 2405.01611 | null | null | http://arxiv.org/pdf/2405.01611v1 | 2024-05-02T13:19:21Z | 2024-05-02T13:19:21Z | Unifying and extending Precision Recall metrics for assessing generative
models | With the recent success of generative models in image and text, the evaluation of generative models has gained a lot of attention. Whereas most generative models are compared in terms of scalar values such as Frechet Inception Distance (FID) or Inception Score (IS), in the last years (Sajjadi et al., 2018) proposed a definition of precision-recall curve to characterize the closeness of two distributions. Since then, various approaches to precision and recall have seen the light (Kynkaanniemi et al., 2019; Naeem et al., 2020; Park & Kim, 2023). They center their attention on the extreme values of precision and recall, but apart from this fact, their ties are elusive. In this paper, we unify most of these approaches under the same umbrella, relying on the work of (Simon et al., 2019). Doing so, we were able not only to recover entire curves, but also to expose the sources of the accounted pitfalls of the concerned metrics. We also provide consistency results that go well beyond the ones presented in the corresponding literature. Last, we study the different behaviors of the curves obtained experimentally. | [
"['Benjamin Sykes' 'Loic Simon' 'Julien Rabin']"
]
|
null | null | 2405.01614 | null | null | http://arxiv.org/pdf/2405.01614v1 | 2024-05-02T16:17:29Z | 2024-05-02T16:17:29Z | A probabilistic estimation of remaining useful life from censored
time-to-event data | Predicting the remaining useful life (RUL) of ball bearings plays an important role in predictive maintenance. A common definition of the RUL is the time until a bearing is no longer functional, which we denote as an event, and many data-driven methods have been proposed to predict the RUL. However, few studies have addressed the problem of censored data, where this event of interest is not observed, and simply ignoring these observations can lead to an overestimation of the failure risk. In this paper, we propose a probabilistic estimation of RUL using survival analysis that supports censored data. First, we analyze sensor readings from ball bearings in the frequency domain and annotate when a bearing starts to deteriorate by calculating the Kullback-Leibler (KL) divergence between the probability density function (PDF) of the current process and a reference PDF. Second, we train several survival models on the annotated bearing dataset, capable of predicting the RUL over a finite time horizon using the survival function. This function is guaranteed to be strictly monotonically decreasing and is an intuitive estimation of the remaining lifetime. We demonstrate our approach in the XJTU-SY dataset using cross-validation and find that Random Survival Forests consistently outperforms both non-neural networks and neural networks in terms of the mean absolute error (MAE). Our work encourages the inclusion of censored data in predictive maintenance models and highlights the unique advantages that survival analysis offers when it comes to probabilistic RUL estimation and early fault detection. | [
"['Christian Marius Lillelund' 'Fernando Pannullo' 'Morten Opprud Jakobsen'\n 'Manuel Morante' 'Christian Fischer Pedersen']"
]
|
null | null | 2405.01615 | null | null | http://arxiv.org/pdf/2405.01615v1 | 2024-05-02T16:19:48Z | 2024-05-02T16:19:48Z | Hard-Thresholding Meets Evolution Strategies in Reinforcement Learning | Evolution Strategies (ES) have emerged as a competitive alternative for model-free reinforcement learning, showcasing exemplary performance in tasks like Mujoco and Atari. Notably, they shine in scenarios with imperfect reward functions, making them invaluable for real-world applications where dense reward signals may be elusive. Yet, an inherent assumption in ES, that all input features are task-relevant, poses challenges, especially when confronted with irrelevant features common in real-world problems. This work scrutinizes this limitation, particularly focusing on the Natural Evolution Strategies (NES) variant. We propose NESHT, a novel approach that integrates Hard-Thresholding (HT) with NES to champion sparsity, ensuring only pertinent features are employed. Backed by rigorous analysis and empirical tests, NESHT demonstrates its promise in mitigating the pitfalls of irrelevant features and shines in complex decision-making problems like noisy Mujoco and Atari tasks. | [
"['Chengqian Gao' 'William de Vazelhes' 'Hualin Zhang' 'Bin Gu'\n 'Zhiqiang Xu']"
]
|
null | null | 2405.01616 | null | null | http://arxiv.org/pdf/2405.01616v1 | 2024-05-02T16:39:21Z | 2024-05-02T16:39:21Z | Generative Active Learning for the Search of Small-molecule Protein
Binders | Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH. | [
"['Maksym Korablyov' 'Cheng-Hao Liu' 'Moksh Jain' 'Almer M. van der Sloot'\n 'Eric Jolicoeur' 'Edward Ruediger' 'Andrei Cristian Nica'\n 'Emmanuel Bengio' 'Kostiantyn Lapchevskyi' 'Daniel St-Cyr'\n 'Doris Alexandra Schuetz' 'Victor Ion Butoi' 'Jarrid Rector-Brooks'\n 'Simon Blackburn' 'Leo Feng' 'Hadi Nekoei' 'SaiKrishna Gottipati'\n 'Priyesh Vijayan' 'Prateek Gupta' 'Ladislav Rampášek' 'Sasikanth Avancha'\n 'Pierre-Luc Bacon' 'William L. Hamilton' 'Brooks Paige' 'Sanchit Misra'\n 'Stanislaw Kamil Jastrzebski' 'Bharat Kaul' 'Doina Precup'\n 'José Miguel Hernández-Lobato' 'Marwin Segler' 'Michael Bronstein'\n 'Anne Marinier' 'Mike Tyers' 'Yoshua Bengio']"
]
|
null | null | 2405.01617 | null | null | http://arxiv.org/pdf/2405.01617v1 | 2024-05-02T16:51:22Z | 2024-05-02T16:51:22Z | An Explainable and Conformal AI Model to Detect Temporomandibular Joint
Involvement in Children Suffering from Juvenile Idiopathic Arthritis | Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease during childhood and adolescence. The temporomandibular joints (TMJ) are among the most frequently affected joints in patients with JIA, and mandibular growth is especially vulnerable to arthritic changes of the TMJ in children. A clinical examination is the most cost-effective method to diagnose TMJ involvement, but clinicians find it difficult to interpret and inaccurate when used only on clinical examinations. This study implemented an explainable artificial intelligence (AI) model that can help clinicians assess TMJ involvement. The classification model was trained using Random Forest on 6154 clinical examinations of 1035 pediatric patients (67% female, 33% male) and evaluated on its ability to correctly classify TMJ involvement or not on a separate test set. Most notably, the results show that the model can classify patients within two years of their first examination as having TMJ involvement with a precision of 0.86 and a sensitivity of 0.7. The results show promise for an AI model in the assessment of TMJ involvement in children and as a decision support tool. | [
"['Lena Todnem Bach Christensen' 'Dikte Straadt' 'Stratos Vassis'\n 'Christian Marius Lillelund' 'Peter Bangsgaard Stoustrup' 'Ruben Pauwels'\n 'Thomas Klit Pedersen' 'Christian Fischer Pedersen']"
]
|
null | null | 2405.01656 | null | null | http://arxiv.org/pdf/2405.01656v2 | 2024-06-27T15:07:39Z | 2024-05-02T18:26:15Z | S4: Self-Supervised Sensing Across the Spectrum | Satellite image time series (SITS) segmentation is crucial for many applications like environmental monitoring, land cover mapping and agricultural crop type classification. However, training models for SITS segmentation remains a challenging task due to the lack of abundant training data, which requires fine grained annotation. We propose S4 a new self-supervised pre-training approach that significantly reduces the requirement for labeled training data by utilizing two new insights: (a) Satellites capture images in different parts of the spectrum such as radio frequencies, and visible frequencies. (b) Satellite imagery is geo-registered allowing for fine-grained spatial alignment. We use these insights to formulate pre-training tasks in S4. We also curate m2s2-SITS, a large-scale dataset of unlabeled, spatially-aligned, multi-modal and geographic specific SITS that serves as representative pre-training data for S4. Finally, we evaluate S4 on multiple SITS segmentation datasets and demonstrate its efficacy against competing baselines while using limited labeled data. | [
"['Jayanth Shenoy' 'Xingjian Davis Zhang' 'Shlok Mehrotra' 'Bill Tao'\n 'Rem Yang' 'Han Zhao' 'Deepak Vasisht']"
]
|
null | null | 2405.01661 | null | null | http://arxiv.org/pdf/2405.01661v1 | 2024-05-02T18:31:47Z | 2024-05-02T18:31:47Z | When a Relation Tells More Than a Concept: Exploring and Evaluating
Classifier Decisions with CoReX | Explanations for Convolutional Neural Networks (CNNs) based on relevance of input pixels might be too unspecific to evaluate which and how input features impact model decisions. Especially in complex real-world domains like biomedicine, the presence of specific concepts (e.g., a certain type of cell) and of relations between concepts (e.g., one cell type is next to another) might be discriminative between classes (e.g., different types of tissue). Pixel relevance is not expressive enough to convey this type of information. In consequence, model evaluation is limited and relevant aspects present in the data and influencing the model decisions might be overlooked. This work presents a novel method to explain and evaluate CNN models, which uses a concept- and relation-based explainer (CoReX). It explains the predictive behavior of a model on a set of images by masking (ir-)relevant concepts from the decision-making process and by constraining relations in a learned interpretable surrogate model. We test our approach with several image data sets and CNN architectures. Results show that CoReX explanations are faithful to the CNN model in terms of predictive outcomes. We further demonstrate that CoReX is a suitable tool for evaluating CNNs supporting identification and re-classification of incorrect or ambiguous classifications. | [
"['Bettina Finzel' 'Patrick Hilme' 'Johannes Rabold' 'Ute Schmid']"
]
|
null | null | 2405.01663 | null | null | http://arxiv.org/pdf/2405.01663v1 | 2024-05-02T18:33:41Z | 2024-05-02T18:33:41Z | ATNPA: A Unified View of Oversmoothing Alleviation in Graph Neural
Networks | Oversmoothing is a commonly observed challenge in graph neural network (GNN) learning, where, as layers increase, embedding features learned from GNNs quickly become similar/indistinguishable, making them incapable of differentiating network proximity. A GNN with shallow layer architectures can only learn short-term relation or localized structure information, limiting its power of learning long-term connection, evidenced by their inferior learning performance on heterophilous graphs. Tackling oversmoothing is crucial to harness deep-layer architectures for GNNs. To date, many methods have been proposed to alleviate oversmoothing. The vast difference behind their design principles, combined with graph complications, make it difficult to understand and even compare their difference in tackling the oversmoothing. In this paper, we propose ATNPA, a unified view with five key steps: Augmentation, Transformation, Normalization, Propagation, and Aggregation, to summarize GNN oversmoothing alleviation approaches. We first outline three themes to tackle oversmoothing, and then separate all methods into six categories, followed by detailed reviews of representative methods, including their relation to the ATNPA, and discussion about their niche, strength, and weakness. The review not only draws in-depth understanding of existing methods in the field, but also shows a clear road map for future study. | [
"['Yufei Jin' 'Xingquan Zhu']"
]
|
null | null | 2405.01677 | null | null | http://arxiv.org/pdf/2405.01677v2 | 2024-06-07T05:18:04Z | 2024-05-02T19:07:14Z | Balance Reward and Safety Optimization for Safe Reinforcement Learning:
A Perspective of Gradient Manipulation | Ensuring the safety of Reinforcement Learning (RL) is crucial for its deployment in real-world applications. Nevertheless, managing the trade-off between reward and safety during exploration presents a significant challenge. Improving reward performance through policy adjustments may adversely affect safety performance. In this study, we aim to address this conflicting relation by leveraging the theory of gradient manipulation. Initially, we analyze the conflict between reward and safety gradients. Subsequently, we tackle the balance between reward and safety optimization by proposing a soft switching policy optimization method, for which we provide convergence analysis. Based on our theoretical examination, we provide a safe RL framework to overcome the aforementioned challenge, and we develop a Safety-MuJoCo Benchmark to assess the performance of safe RL algorithms. Finally, we evaluate the effectiveness of our method on the Safety-MuJoCo Benchmark and a popular safe RL benchmark, Omnisafe. Experimental results demonstrate that our algorithms outperform several state-of-the-art baselines in terms of balancing reward and safety optimization. | [
"['Shangding Gu' 'Bilgehan Sel' 'Yuhao Ding' 'Lu Wang' 'Qingwei Lin'\n 'Ming Jin' 'Alois Knoll']"
]
|
null | null | 2405.01680 | null | null | http://arxiv.org/pdf/2405.01680v2 | 2024-06-13T00:39:43Z | 2024-05-02T19:08:59Z | Physics-Informed Neural Networks: Minimizing Residual Loss with Wide
Networks and Effective Activations | The residual loss in Physics-Informed Neural Networks (PINNs) alters the simple recursive relation of layers in a feed-forward neural network by applying a differential operator, resulting in a loss landscape that is inherently different from those of common supervised problems. Therefore, relying on the existing theory leads to unjustified design choices and suboptimal performance. In this work, we analyze the residual loss by studying its characteristics at critical points to find the conditions that result in effective training of PINNs. Specifically, we first show that under certain conditions, the residual loss of PINNs can be globally minimized by a wide neural network. Furthermore, our analysis also reveals that an activation function with well-behaved high-order derivatives plays a crucial role in minimizing the residual loss. In particular, to solve a $k$-th order PDE, the $k$-th derivative of the activation function should be bijective. The established theory paves the way for designing and choosing effective activation functions for PINNs and explains why periodic activations have shown promising performance in certain cases. Finally, we verify our findings by conducting a set of experiments on several PDEs. Our code is publicly available at https://github.com/nimahsn/pinns_tf2. | [
"['Nima Hosseini Dashtbayaz' 'Ghazal Farhani' 'Boyu Wang' 'Charles X. Ling']"
]
|
null | null | 2405.01684 | null | null | http://arxiv.org/pdf/2405.01684v1 | 2024-05-02T19:15:00Z | 2024-05-02T19:15:00Z | Intelligent Switching for Reset-Free RL | In the real world, the strong episode resetting mechanisms that are needed to train agents in simulation are unavailable. The textit{resetting} assumption limits the potential of reinforcement learning in the real world, as providing resets to an agent usually requires the creation of additional handcrafted mechanisms or human interventions. Recent work aims to train agents (textit{forward}) with learned resets by constructing a second (textit{backward}) agent that returns the forward agent to the initial state. We find that the termination and timing of the transitions between these two agents are crucial for algorithm success. With this in mind, we create a new algorithm, Reset Free RL with Intelligently Switching Controller (RISC) which intelligently switches between the two agents based on the agent's confidence in achieving its current goal. Our new method achieves state-of-the-art performance on several challenging environments for reset-free RL. | [
"['Darshan Patil' 'Janarthanan Rajendran' 'Glen Berseth' 'Sarath Chandar']"
]
|
null | null | 2405.01691 | null | null | http://arxiv.org/pdf/2405.01691v1 | 2024-05-02T19:27:28Z | 2024-05-02T19:27:28Z | Language-Enhanced Latent Representations for Out-of-Distribution
Detection in Autonomous Driving | Out-of-distribution (OOD) detection is essential in autonomous driving, to determine when learning-based components encounter unexpected inputs. Traditional detectors typically use encoder models with fixed settings, thus lacking effective human interaction capabilities. With the rise of large foundation models, multimodal inputs offer the possibility of taking human language as a latent representation, thus enabling language-defined OOD detection. In this paper, we use the cosine similarity of image and text representations encoded by the multimodal model CLIP as a new representation to improve the transparency and controllability of latent encodings used for visual anomaly detection. We compare our approach with existing pre-trained encoders that can only produce latent representations that are meaningless from the user's standpoint. Our experiments on realistic driving data show that the language-based latent representation performs better than the traditional representation of the vision encoder and helps improve the detection performance when combined with standard representations. | [
"['Zhenjiang Mao' 'Dong-You Jhong' 'Ao Wang' 'Ivan Ruchkin']"
]
|
null | null | 2405.01702 | null | null | http://arxiv.org/pdf/2405.01702v2 | 2024-06-05T12:27:17Z | 2024-05-02T19:55:30Z | Optimization without Retraction on the Random Generalized Stiefel
Manifold | Optimization over the set of matrices $X$ that satisfy $X^top B X = I_p$, referred to as the generalized Stiefel manifold, appears in many applications involving sampled covariance matrices such as the canonical correlation analysis (CCA), independent component analysis (ICA), and the generalized eigenvalue problem (GEVP). Solving these problems is typically done by iterative methods that require a fully formed $B$. We propose a cheap stochastic iterative method that solves the optimization problem while having access only to a random estimates of $B$. Our method does not enforce the constraint in every iteration; instead, it produces iterations that converge to critical points on the generalized Stiefel manifold defined in expectation. The method has lower per-iteration cost, requires only matrix multiplications, and has the same convergence rates as its Riemannian optimization counterparts that require the full matrix $B$. Experiments demonstrate its effectiveness in various machine learning applications involving generalized orthogonality constraints, including CCA, ICA, and the GEVP. | [
"['Simon Vary' 'Pierre Ablin' 'Bin Gao' 'P. -A. Absil']"
]
|
null | null | 2405.01704 | null | null | http://arxiv.org/pdf/2405.01704v1 | 2024-05-02T20:03:13Z | 2024-05-02T20:03:13Z | Privacy-aware Berrut Approximated Coded Computing for Federated Learning | Federated Learning (FL) is an interesting strategy that enables the collaborative training of an AI model among different data owners without revealing their private datasets. Even so, FL has some privacy vulnerabilities that have been tried to be overcome by applying some techniques like Differential Privacy (DP), Homomorphic Encryption (HE), or Secure Multi-Party Computation (SMPC). However, these techniques have some important drawbacks that might narrow their range of application: problems to work with non-linear functions and to operate large matrix multiplications and high communication and computational costs to manage semi-honest nodes. In this context, we propose a solution to guarantee privacy in FL schemes that simultaneously solves the previously mentioned problems. Our proposal is based on the Berrut Approximated Coded Computing, a technique from the Coded Distributed Computing paradigm, adapted to a Secret Sharing configuration, to provide input privacy to FL in a scalable way. It can be applied for computing non-linear functions and treats the special case of distributed matrix multiplication, a key primitive at the core of many automated learning tasks. Because of these characteristics, it could be applied in a wide range of FL scenarios, since it is independent of the machine learning models or aggregation algorithms used in the FL scheme. We provide analysis of the achieve privacy and complexity of our solution and, due to the extensive numerical results performed, it can be observed a good trade-off between privacy and precision. | [
"['Xavier Martínez Luaña' 'Rebeca P. Díaz Redondo' 'Manuel Fernández Veiga']"
]
|
null | null | 2405.01708 | null | null | http://arxiv.org/pdf/2405.01708v1 | 2024-05-02T20:06:06Z | 2024-05-02T20:06:06Z | A deep causal inference model for fully-interpretable travel behaviour
analysis | Transport policy assessment often involves causal questions, yet the causal inference capabilities of traditional travel behavioural models are at best limited. We present the deep CAusal infeRence mOdel for traveL behavIour aNAlysis (CAROLINA), a framework that explicitly models causality in travel behaviour, enhances predictive accuracy, and maintains interpretability by leveraging causal inference, deep learning, and traditional discrete choice modelling. Within this framework, we introduce a Generative Counterfactual model for forecasting human behaviour by adapting the Normalizing Flow method. Through the case studies of virtual reality-based pedestrian crossing behaviour, revealed preference travel behaviour from London, and synthetic data, we demonstrate the effectiveness of our proposed models in uncovering causal relationships, prediction accuracy, and assessing policy interventions. Our results show that intervention mechanisms that can reduce pedestrian stress levels lead to a 38.5% increase in individuals experiencing shorter waiting times. Reducing the travel distances in London results in a 47% increase in sustainable travel modes. | [
"['Kimia Kamal' 'Bilal Farooq']"
]
|
null | null | 2405.01711 | null | null | http://arxiv.org/pdf/2405.01711v2 | 2024-05-07T19:55:01Z | 2024-05-02T20:15:25Z | Individual Fairness Through Reweighting and Tuning | Inherent bias within society can be amplified and perpetuated by artificial intelligence (AI) systems. To address this issue, a wide range of solutions have been proposed to identify and mitigate bias and enforce fairness for individuals and groups. Recently, Graph Laplacian Regularizer (GLR), a regularization technique from the semi-supervised learning literature has been used as a substitute for the common Lipschitz condition to enhance individual fairness. Notable prior work has shown that enforcing individual fairness through a GLR can improve the transfer learning accuracy of AI models under covariate shifts. However, the prior work defines a GLR on the source and target data combined, implicitly assuming that the target data are available at train time, which might not hold in practice. In this work, we investigated whether defining a GLR independently on the train and target data could maintain similar accuracy. Furthermore, we introduced the Normalized Fairness Gain score (NFG) to measure individual fairness by measuring the amount of gained fairness when a GLR is used versus not. We evaluated the new and original methods under NFG, the Prediction Consistency (PC), and traditional classification metrics on the German Credit Approval dataset. The results showed that the two models achieved similar statistical mean performances over five-fold cross-validation. Furthermore, the proposed metric showed that PC scores can be misleading as the scores can be high and statistically similar to fairness-enhanced models while NFG scores are small. This work therefore provides new insights into when a GLR effectively enhances individual fairness and the pitfalls of PC. | [
"['Abdoul Jalil Djiberou Mahamadou' 'Lea Goetz' 'Russ Altman']"
]
|
null | null | 2405.01714 | null | null | http://arxiv.org/pdf/2405.01714v3 | 2024-05-21T21:02:59Z | 2024-05-02T20:19:07Z | Interpretable Vital Sign Forecasting with Model Agnostic Attention Maps | Sepsis is a leading cause of mortality in intensive care units (ICUs), representing a substantial medical challenge. The complexity of analyzing diverse vital signs to predict sepsis further aggravates this issue. While deep learning techniques have been advanced for early sepsis prediction, their 'black-box' nature obscures the internal logic, impairing interpretability in critical settings like ICUs. This paper introduces a framework that combines a deep learning model with an attention mechanism that highlights the critical time steps in the forecasting process, thus improving model interpretability and supporting clinical decision-making. We show that the attention mechanism could be adapted to various black box time series forecasting models such as N-HiTS and N-BEATS. Our method preserves the accuracy of conventional deep learning models while enhancing interpretability through attention-weight-generated heatmaps. We evaluated our model on the eICU-CRD dataset, focusing on forecasting vital signs for sepsis patients. We assessed its performance using mean squared error (MSE) and dynamic time warping (DTW) metrics. We explored the attention maps of N-HiTS and N-BEATS, examining the differences in their performance and identifying crucial factors influencing vital sign forecasting. | [
"['Yuwei Liu' 'Chen Dan' 'Anubhav Bhatti' 'Bingjie Shen' 'Divij Gupta'\n 'Suraj Parmar' 'San Lee']"
]
|
null | null | 2405.01718 | null | null | http://arxiv.org/pdf/2405.01718v1 | 2024-05-02T20:28:49Z | 2024-05-02T20:28:49Z | Robust Risk-Sensitive Reinforcement Learning with Conditional
Value-at-Risk | Robust Markov Decision Processes (RMDPs) have received significant research interest, offering an alternative to standard Markov Decision Processes (MDPs) that often assume fixed transition probabilities. RMDPs address this by optimizing for the worst-case scenarios within ambiguity sets. While earlier studies on RMDPs have largely centered on risk-neutral reinforcement learning (RL), with the goal of minimizing expected total discounted costs, in this paper, we analyze the robustness of CVaR-based risk-sensitive RL under RMDP. Firstly, we consider predetermined ambiguity sets. Based on the coherency of CVaR, we establish a connection between robustness and risk sensitivity, thus, techniques in risk-sensitive RL can be adopted to solve the proposed problem. Furthermore, motivated by the existence of decision-dependent uncertainty in real-world problems, we study problems with state-action-dependent ambiguity sets. To solve this, we define a new risk measure named NCVaR and build the equivalence of NCVaR optimization and robust CVaR optimization. We further propose value iteration algorithms and validate our approach in simulation experiments. | [
"['Xinyi Ni' 'Lifeng Lai']"
]
|
null | null | 2405.01719 | null | null | http://arxiv.org/pdf/2405.01719v2 | 2024-05-06T15:09:50Z | 2024-05-02T20:28:54Z | Inherent Trade-Offs between Diversity and Stability in Multi-Task
Benchmarks | We examine multi-task benchmarks in machine learning through the lens of social choice theory. We draw an analogy between benchmarks and electoral systems, where models are candidates and tasks are voters. This suggests a distinction between cardinal and ordinal benchmark systems. The former aggregate numerical scores into one model ranking; the latter aggregate rankings for each task. We apply Arrow's impossibility theorem to ordinal benchmarks to highlight the inherent limitations of ordinal systems, particularly their sensitivity to the inclusion of irrelevant models. Inspired by Arrow's theorem, we empirically demonstrate a strong trade-off between diversity and sensitivity to irrelevant changes in existing multi-task benchmarks. Our result is based on new quantitative measures of diversity and sensitivity that we introduce. Sensitivity quantifies the impact that irrelevant changes to tasks have on a benchmark. Diversity captures the degree of disagreement in model rankings across tasks. We develop efficient approximation algorithms for both measures, as exact computation is computationally challenging. Through extensive experiments on seven cardinal benchmarks and eleven ordinal benchmarks, we demonstrate a clear trade-off between diversity and stability: The more diverse a multi-task benchmark, the more sensitive to trivial changes it is. Additionally, we show that the aggregated rankings of existing benchmarks are highly unstable under irrelevant changes. The codes and data are available at https://socialfoundations.github.io/benchbench/. | [
"['Guanhua Zhang' 'Moritz Hardt']"
]
|
null | null | 2405.01725 | null | null | http://arxiv.org/pdf/2405.01725v1 | 2024-05-02T20:43:58Z | 2024-05-02T20:43:58Z | Development of Skip Connection in Deep Neural Networks for Computer
Vision and Medical Image Analysis: A Survey | Deep learning has made significant progress in computer vision, specifically in image classification, object detection, and semantic segmentation. The skip connection has played an essential role in the architecture of deep neural networks,enabling easier optimization through residual learning during the training stage and improving accuracy during testing. Many neural networks have inherited the idea of residual learning with skip connections for various tasks, and it has been the standard choice for designing neural networks. This survey provides a comprehensive summary and outlook on the development of skip connections in deep neural networks. The short history of skip connections is outlined, and the development of residual learning in deep neural networks is surveyed. The effectiveness of skip connections in the training and testing stages is summarized, and future directions for using skip connections in residual learning are discussed. Finally, we summarize seminal papers, source code, models, and datasets that utilize skip connections in computer vision, including image classification, object detection, semantic segmentation, and image reconstruction. We hope this survey could inspire peer researchers in the community to develop further skip connections in various forms and tasks and the theory of residual learning in deep neural networks. The project page can be found at https://github.com/apple1986/Residual_Learning_For_Images | [
"['Guoping Xu' 'Xiaxia Wang' 'Xinglong Wu' 'Xuesong Leng' 'Yongchao Xu']"
]
|
null | null | 2405.01726 | null | null | http://arxiv.org/pdf/2405.01726v6 | 2024-06-20T07:31:53Z | 2024-05-02T20:44:26Z | SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral
Image Denoising | Denoising is a crucial preprocessing step for hyperspectral images (HSIs) due to noise arising from intraimaging mechanisms and environmental factors. Long-range spatial-spectral correlation modeling is beneficial for HSI denoising but often comes with high computational complexity. Based on the state space model (SSM), Mamba is known for its remarkable long-range dependency modeling capabilities and computational efficiency. Building on this, we introduce a memory-efficient spatial-spectral UMamba (SSUMamba) for HSI denoising, with the spatial-spectral continuous scan (SSCS) Mamba being the core component. SSCS Mamba alternates the row, column, and band in six different orders to generate the sequence and uses the bidirectional SSM to exploit long-range spatial-spectral dependencies. In each order, the images are rearranged between adjacent scans to ensure spatial-spectral continuity. Additionally, 3D convolutions are embedded into the SSCS Mamba to enhance local spatial-spectral modeling. Experiments demonstrate that SSUMamba achieves superior denoising results with lower memory consumption per batch compared to transformer-based methods. The source code is available at https://github.com/lronkitty/SSUMamba. | [
"['Guanyiman Fu' 'Fengchao Xiong' 'Jianfeng Lu' 'Jun Zhou']"
]
|
null | null | 2405.01731 | null | null | http://arxiv.org/pdf/2405.01731v1 | 2024-05-02T21:04:20Z | 2024-05-02T21:04:20Z | Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization | We propose a novel algorithm that extends the methods of ball smoothing and Gaussian smoothing for noisy derivative-free optimization by accounting for the heterogeneous curvature of the objective function. The algorithm dynamically adapts the shape of the smoothing kernel to approximate the Hessian of the objective function around a local optimum. This approach significantly reduces the error in estimating the gradient from noisy evaluations through sampling. We demonstrate the efficacy of our method through numerical experiments on artificial problems. Additionally, we show improved performance when tuning NP-hard combinatorial optimization solvers compared to existing state-of-the-art heuristic derivative-free and Bayesian optimization methods. | [
"['Sam Reifenstein' 'Timothee Leleu' 'Yoshihisa Yamamoto']"
]
|
null | null | 2405.01737 | null | null | http://arxiv.org/pdf/2405.01737v1 | 2024-05-02T21:13:34Z | 2024-05-02T21:13:34Z | Sample-efficient neural likelihood-free Bayesian inference of implicit
HMMs | Likelihood-free inference methods based on neural conditional density estimation were shown to drastically reduce the simulation burden in comparison to classical methods such as ABC. When applied in the context of any latent variable model, such as a Hidden Markov model (HMM), these methods are designed to only estimate the parameters, rather than the joint distribution of the parameters and the hidden states. Naive application of these methods to a HMM, ignoring the inference of this joint posterior distribution, will thus produce an inaccurate estimate of the posterior predictive distribution, in turn hampering the assessment of goodness-of-fit. To rectify this problem, we propose a novel, sample-efficient likelihood-free method for estimating the high-dimensional hidden states of an implicit HMM. Our approach relies on learning directly the intractable posterior distribution of the hidden states, using an autoregressive-flow, by exploiting the Markov property. Upon evaluating our approach on some implicit HMMs, we found that the quality of the estimates retrieved using our method is comparable to what can be achieved using a much more computationally expensive SMC algorithm. | [
"['Sanmitra Ghosh' 'Paul J. Birrell' 'Daniela De Angelis']"
]
|
null | null | 2405.01739 | null | null | http://arxiv.org/pdf/2405.01739v1 | 2024-05-02T21:18:06Z | 2024-05-02T21:18:06Z | Enhancing User Experience in On-Device Machine Learning with Gated
Compression Layers | On-device machine learning (ODML) enables powerful edge applications, but power consumption remains a key challenge for resource-constrained devices. To address this, developers often face a trade-off between model accuracy and power consumption, employing either computationally intensive models on high-power cores or pared-down models on low-power cores. Both approaches typically lead to a compromise in user experience (UX). This work focuses on the use of Gated Compression (GC) layer to enhance ODML model performance while conserving power and maximizing cost-efficiency, especially for always-on use cases. GC layers dynamically regulate data flow by selectively gating activations of neurons within the neural network and effectively filtering out non-essential inputs, which reduces power needs without compromising accuracy, and enables more efficient execution on heterogeneous compute cores. These improvements enhance UX through prolonged battery life, improved device responsiveness, and greater user comfort. In this work, we have integrated GC layers into vision and speech domain models including the transformer-based ViT model. Our experiments demonstrate theoretical power efficiency gains ranging from 158x to 30,000x for always-on scenarios. This substantial improvement empowers ODML applications with enhanced UX benefits. | [
"['Haiguang Li' 'Usama Pervaiz' 'Joseph Antognini' 'Michał Matuszak'\n 'Lawrence Au' 'Gilles Roux' 'Trausti Thormundsson']"
]
|
null | null | 2405.01741 | null | null | http://arxiv.org/pdf/2405.01741v3 | 2024-06-11T22:37:33Z | 2024-05-02T21:23:34Z | PVF (Parameter Vulnerability Factor): A Scalable Metric for
Understanding AI Vulnerability Against SDCs in Model Parameters | Reliability of AI systems is a fundamental concern for the successful deployment and widespread adoption of AI technologies. Unfortunately, the escalating complexity and heterogeneity of AI hardware systems make them increasingly susceptible to hardware faults, e.g., silent data corruptions (SDC), that can potentially corrupt model parameters. When this occurs during AI inference/servicing, it can potentially lead to incorrect or degraded model output for users, ultimately affecting the quality and reliability of AI services. In light of the escalating threat, it is crucial to address key questions: How vulnerable are AI models to parameter corruptions, and how do different components (such as modules, layers) of the models exhibit varying vulnerabilities to parameter corruptions? To systematically address this question, we propose a novel quantitative metric, Parameter Vulnerability Factor (PVF), inspired by architectural vulnerability factor (AVF) in computer architecture community, aiming to standardize the quantification of AI model vulnerability against parameter corruptions. We define a model parameter's PVF as the probability that a corruption in that particular model parameter will result in an incorrect output. In this paper, we present several use cases on applying PVF to three types of tasks/models during inference -- recommendation (DLRM), vision classification (CNN), and text classification (BERT), while presenting an in-depth vulnerability analysis on DLRM. PVF can provide pivotal insights to AI hardware designers in balancing the tradeoff between fault protection and performance/efficiency such as mapping vulnerable AI parameter components to well-protected hardware modules. PVF metric is applicable to any AI model and has a potential to help unify and standardize AI vulnerability/resilience evaluation practice. | [
"['Xun Jiao' 'Fred Lin' 'Harish D. Dixit' 'Joel Coburn' 'Abhinav Pandey'\n 'Han Wang' 'Venkat Ramesh' 'Jianyu Huang' 'Wang Xu' 'Daniel Moore'\n 'Sriram Sankar']"
]
|
null | null | 2405.01744 | null | null | http://arxiv.org/pdf/2405.01744v1 | 2024-05-02T21:27:45Z | 2024-05-02T21:27:45Z | ALCM: Autonomous LLM-Augmented Causal Discovery Framework | To perform effective causal inference in high-dimensional datasets, initiating the process with causal discovery is imperative, wherein a causal graph is generated based on observational data. However, obtaining a complete and accurate causal graph poses a formidable challenge, recognized as an NP-hard problem. Recently, the advent of Large Language Models (LLMs) has ushered in a new era, indicating their emergent capabilities and widespread applicability in facilitating causal reasoning across diverse domains, such as medicine, finance, and science. The expansive knowledge base of LLMs holds the potential to elevate the field of causal reasoning by offering interpretability, making inferences, generalizability, and uncovering novel causal structures. In this paper, we introduce a new framework, named Autonomous LLM-Augmented Causal Discovery Framework (ALCM), to synergize data-driven causal discovery algorithms and LLMs, automating the generation of a more resilient, accurate, and explicable causal graph. The ALCM consists of three integral components: causal structure learning, causal wrapper, and LLM-driven causal refiner. These components autonomously collaborate within a dynamic environment to address causal discovery questions and deliver plausible causal graphs. We evaluate the ALCM framework by implementing two demonstrations on seven well-known datasets. Experimental results demonstrate that ALCM outperforms existing LLM methods and conventional data-driven causal reasoning mechanisms. This study not only shows the effectiveness of the ALCM but also underscores new research directions in leveraging the causal reasoning capabilities of LLMs. | [
"['Elahe Khatibi' 'Mahyar Abbasian' 'Zhongqi Yang' 'Iman Azimi'\n 'Amir M. Rahmani']"
]
|
null | null | 2405.01745 | null | null | http://arxiv.org/pdf/2405.01745v1 | 2024-05-02T21:30:10Z | 2024-05-02T21:30:10Z | Large Language Models for UAVs: Current State and Pathways to the Future | Unmanned Aerial Vehicles (UAVs) have emerged as a transformative technology across diverse sectors, offering adaptable solutions to complex challenges in both military and civilian domains. Their expanding capabilities present a platform for further advancement by integrating cutting-edge computational tools like Artificial Intelligence (AI) and Machine Learning (ML) algorithms. These advancements have significantly impacted various facets of human life, fostering an era of unparalleled efficiency and convenience. Large Language Models (LLMs), a key component of AI, exhibit remarkable learning and adaptation capabilities within deployed environments, demonstrating an evolving form of intelligence with the potential to approach human-level proficiency. This work explores the significant potential of integrating UAVs and LLMs to propel the development of autonomous systems. We comprehensively review LLM architectures, evaluating their suitability for UAV integration. Additionally, we summarize the state-of-the-art LLM-based UAV architectures and identify novel opportunities for LLM embedding within UAV frameworks. Notably, we focus on leveraging LLMs to refine data analysis and decision-making processes, specifically for enhanced spectral sensing and sharing in UAV applications. Furthermore, we investigate how LLM integration expands the scope of existing UAV applications, enabling autonomous data processing, improved decision-making, and faster response times in emergency scenarios like disaster response and network restoration. Finally, we highlight crucial areas for future research that are critical for facilitating the effective integration of LLMs and UAVs. | [
"['Shumaila Javaid' 'Nasir Saeed' 'Bin He']"
]
|
null | null | 2405.01758 | null | null | http://arxiv.org/pdf/2405.01758v1 | 2024-05-02T21:50:26Z | 2024-05-02T21:50:26Z | CGD: Constraint-Guided Diffusion Policies for UAV Trajectory Planning | Traditional optimization-based planners, while effective, suffer from high computational costs, resulting in slow trajectory generation. A successful strategy to reduce computation time involves using Imitation Learning (IL) to develop fast neural network (NN) policies from those planners, which are treated as expert demonstrators. Although the resulting NN policies are effective at quickly generating trajectories similar to those from the expert, (1) their output does not explicitly account for dynamic feasibility, and (2) the policies do not accommodate changes in the constraints different from those used during training. To overcome these limitations, we propose Constraint-Guided Diffusion (CGD), a novel IL-based approach to trajectory planning. CGD leverages a hybrid learning/online optimization scheme that combines diffusion policies with a surrogate efficient optimization problem, enabling the generation of collision-free, dynamically feasible trajectories. The key ideas of CGD include dividing the original challenging optimization problem solved by the expert into two more manageable sub-problems: (a) efficiently finding collision-free paths, and (b) determining a dynamically-feasible time-parametrization for those paths to obtain a trajectory. Compared to conventional neural network architectures, we demonstrate through numerical evaluations significant improvements in performance and dynamic feasibility under scenarios with new constraints never encountered during training. | [
"['Kota Kondo' 'Andrea Tagliabue' 'Xiaoyi Cai' 'Claudius Tewari'\n 'Olivia Garcia' 'Marcos Espitia-Alvarez' 'Jonathan P. How']"
]
|
null | null | 2405.01760 | null | null | http://arxiv.org/pdf/2405.01760v1 | 2024-05-02T21:52:24Z | 2024-05-02T21:52:24Z | Reinforcement Learning-Guided Semi-Supervised Learning | In recent years, semi-supervised learning (SSL) has gained significant attention due to its ability to leverage both labeled and unlabeled data to improve model performance, especially when labeled data is scarce. However, most current SSL methods rely on heuristics or predefined rules for generating pseudo-labels and leveraging unlabeled data. They are limited to exploiting loss functions and regularization methods within the standard norm. In this paper, we propose a novel Reinforcement Learning (RL) Guided SSL method, RLGSSL, that formulates SSL as a one-armed bandit problem and deploys an innovative RL loss based on weighted reward to adaptively guide the learning process of the prediction model. RLGSSL incorporates a carefully designed reward function that balances the use of labeled and unlabeled data to enhance generalization performance. A semi-supervised teacher-student framework is further deployed to increase the learning stability. We demonstrate the effectiveness of RLGSSL through extensive experiments on several benchmark datasets and show that our approach achieves consistent superior performance compared to state-of-the-art SSL methods. | [
"['Marzi Heidari' 'Hanping Zhang' 'Yuhong Guo']"
]
|
null | null | 2405.01761 | null | null | http://arxiv.org/pdf/2405.01761v1 | 2024-05-02T21:53:32Z | 2024-05-02T21:53:32Z | Multivariate Bayesian Last Layer for Regression: Uncertainty
Quantification and Disentanglement | We present new Bayesian Last Layer models in the setting of multivariate regression under heteroscedastic noise, and propose an optimization algorithm for parameter learning. Bayesian Last Layer combines Bayesian modelling of the predictive distribution with neural networks for parameterization of the prior, and has the attractive property of uncertainty quantification with a single forward pass. The proposed framework is capable of disentangling the aleatoric and epistemic uncertainty, and can be used to transfer a canonically trained deep neural network to new data domains with uncertainty-aware capability. | [
"['Han Wang' 'Eiji Kawasaki' 'Guillaume Damblin' 'Geoffrey Daniel']"
]
|
null | null | 2405.01762 | null | null | http://arxiv.org/pdf/2405.01762v2 | 2024-05-16T14:55:47Z | 2024-05-02T21:55:12Z | EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in
Linear Time | Understanding and explaining the predictions of Graph Neural Networks (GNNs), is crucial for enhancing their safety and trustworthiness. Subgraph-level explanations are gaining attention for their intuitive appeal. However, most existing subgraph-level explainers face efficiency challenges in explaining GNNs due to complex search processes. The key challenge is to find a balance between intuitiveness and efficiency while ensuring transparency. Additionally, these explainers usually induce subgraphs by nodes, which may introduce less-intuitive disconnected nodes in the subgraph-level explanations or omit many important subgraph structures. In this paper, we reveal that inducing subgraph explanations by edges is more comprehensive than other subgraph inducing techniques. We also emphasize the need of determining the subgraph explanation size for each data instance, as different data instances may involve different important substructures. Building upon these considerations, we introduce a training-free approach, named EiG-Search. We employ an efficient linear-time search algorithm over the edge-induced subgraphs, where the edges are ranked by an enhanced gradient-based importance. We conduct extensive experiments on a total of seven datasets, demonstrating its superior performance and efficiency both quantitatively and qualitatively over the leading baselines. | [
"['Shengyao Lu' 'Bang Liu' 'Keith G. Mills' 'Jiao He' 'Di Niu']"
]
|
null | null | 2405.01775 | null | null | http://arxiv.org/pdf/2405.01775v2 | 2024-05-06T15:27:31Z | 2024-05-02T23:21:52Z | Torch2Chip: An End-to-end Customizable Deep Neural Network Compression
and Deployment Toolkit for Prototype Hardware Accelerator Design | The development of model compression is continuously motivated by the evolution of various neural network accelerators with ASIC or FPGA. On the algorithm side, the ultimate goal of quantization or pruning is accelerating the expensive DNN computations on low-power hardware. However, such a "design-and-deploy" workflow faces under-explored challenges in the current hardware-algorithm co-design community. First, although the state-of-the-art quantization algorithm can achieve low precision with negligible degradation of accuracy, the latest deep learning framework (e.g., PyTorch) can only support non-customizable 8-bit precision, data format, and parameter extraction. Secondly, the objective of quantization is to enable the computation with low-precision data. However, the current SoTA algorithm treats the quantized integer as an intermediate result, while the final output of the quantizer is the "discretized" floating-point values, ignoring the practical needs and adding additional workload to hardware designers for integer parameter extraction and layer fusion. Finally, the compression toolkits designed by the industry are constrained to their in-house product or a handful of algorithms. The limited degree of freedom in the current toolkit and the under-explored customization hinder the prototype ASIC or FPGA-based accelerator design. To resolve these challenges, we propose Torch2Chip, an open-sourced, fully customizable, and high-performance toolkit that supports user-designed compression followed by automatic model fusion and parameter extraction. Torch2Chip incorporates the hierarchical design workflow, and the user-customized compression algorithm will be directly packed into the deployment-ready format for prototype chip verification with either CNN or vision transformer (ViT). The code is available at https://github.com/SeoLabCornell/torch2chip. | [
"['Jian Meng' 'Yuan Liao' 'Anupreetham Anupreetham' 'Ahmed Hasssan'\n 'Shixing Yu' 'Han-sok Suh' 'Xiaofeng Hu' 'Jae-sun Seo']"
]
|
null | null | 2405.01776 | null | null | http://arxiv.org/abs/2405.01776v1 | 2024-05-02T23:24:27Z | 2024-05-02T23:24:27Z | An Approach to Systematic Data Acquisition and Data-Driven Simulation
for the Safety Testing of Automated Driving Functions | With growing complexity and criticality of automated driving functions in road traffic and their operational design domains (ODD), there is increasing demand for covering significant proportions of development, validation, and verification in virtual environments and through simulation models. If, however, simulations are meant not only to augment real-world experiments, but to replace them, quantitative approaches are required that measure to what degree and under which preconditions simulation models adequately represent reality, and thus, using their results accordingly. Especially in R&D areas related to the safety impact of the "open world", there is a significant shortage of real-world data to parameterize and/or validate simulations - especially with respect to the behavior of human traffic participants, whom automated driving functions will meet in mixed traffic. We present an approach to systematically acquire data in public traffic by heterogeneous means, transform it into a unified representation, and use it to automatically parameterize traffic behavior models for use in data-driven virtual validation of automated driving functions. | [
"['Leon Eisemann' 'Mirjam Fehling-Kaschek' 'Henrik Gommel' 'David Hermann'\n 'Marvin Klemp' 'Martin Lauer' 'Benjamin Lickert' 'Florian Luettner'\n 'Robin Moss' 'Nicole Neis' 'Maria Pohle' 'Simon Romanski'\n 'Daniel Stadler' 'Alexander Stolz' 'Jens Ziehn' 'Jingxing Zhou']"
]
|
null | null | 2405.01778 | null | null | http://arxiv.org/pdf/2405.01778v1 | 2024-05-02T23:32:22Z | 2024-05-02T23:32:22Z | Hierarchical mixture of discriminative Generalized Dirichlet classifiers | This paper presents a discriminative classifier for compositional data. This classifier is based on the posterior distribution of the Generalized Dirichlet which is the discriminative counterpart of Generalized Dirichlet mixture model. Moreover, following the mixture of experts paradigm, we proposed a hierarchical mixture of this classifier. In order to learn the models parameters, we use a variational approximation by deriving an upper-bound for the Generalized Dirichlet mixture. To the best of our knownledge, this is the first time this bound is proposed in the literature. Experimental results are presented for spam detection and color space identification. | [
"['Elvis Togban' 'Djemel Ziou']"
]
|
null | null | 2405.01780 | null | null | http://arxiv.org/pdf/2405.01780v1 | 2024-05-02T23:45:29Z | 2024-05-02T23:45:29Z | Quantum Machine Learning: Quantum Kernel Methods | Quantum algorithms based on quantum kernel methods have been investigated previously [1]. A quantum advantage is derived from the fact that it is possible to construct a family of datasets for which, only quantum processing can recognise the intrinsic labelling patterns, while for classical computers the dataset looks like noise. This is due to the algorithm leveraging inherent efficiencies in the computation of logarithms in a cyclic group. The discrete log problem.is a well-known advantage of quantum vs classical computation: where it is possible to generate all the members of the group using a single mathematical operation. Kernel methods are a powerful and popular technique in classical Machine Learning. The use of a quantum feature space that can only be calculated efficiently on a quantum computer potentially allows for deriving a quantum advantage. In this paper, we intend to first describe the application of such a kernel method to a Quantum version of the classical Support Vector Machine (SVM) algorithm to identify conditions under which, a quantum advantage is realised. A data dependent projected quantum kernel was shown to provide significant advantage over classical kernels. Further, we present results of investigations and ideas pertaining to extending the use of quantum kernels as a feature extraction layer in a Convolutional Neural Networks (CNN) that is a widely used architecture in deep-learning applications. | [
"['Sanjeev Naguleswaran']"
]
|
null | null | 2405.01792 | null | null | http://arxiv.org/abs/2405.01792v1 | 2024-05-03T00:29:20Z | 2024-05-03T00:29:20Z | Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged
Robots | Autonomous wheeled-legged robots have the potential to transform logistics systems, improving operational efficiency and adaptability in urban environments. Navigating urban environments, however, poses unique challenges for robots, necessitating innovative solutions for locomotion and navigation. These challenges include the need for adaptive locomotion across varied terrains and the ability to navigate efficiently around complex dynamic obstacles. This work introduces a fully integrated system comprising adaptive locomotion control, mobility-aware local navigation planning, and large-scale path planning within the city. Using model-free reinforcement learning (RL) techniques and privileged learning, we develop a versatile locomotion controller. This controller achieves efficient and robust locomotion over various rough terrains, facilitated by smooth transitions between walking and driving modes. It is tightly integrated with a learned navigation controller through a hierarchical RL framework, enabling effective navigation through challenging terrain and various obstacles at high speed. Our controllers are integrated into a large-scale urban navigation system and validated by autonomous, kilometer-scale navigation missions conducted in Zurich, Switzerland, and Seville, Spain. These missions demonstrate the system's robustness and adaptability, underscoring the importance of integrated control systems in achieving seamless navigation in complex environments. Our findings support the feasibility of wheeled-legged robots and hierarchical RL for autonomous navigation, with implications for last-mile delivery and beyond. | [
"['Joonho Lee' 'Marko Bjelonic' 'Alexander Reske' 'Lorenz Wellhausen'\n 'Takahiro Miki' 'Marco Hutter']"
]
|
null | null | 2405.01810 | null | null | http://arxiv.org/pdf/2405.01810v1 | 2024-05-03T01:50:03Z | 2024-05-03T01:50:03Z | Non-linear Welfare-Aware Strategic Learning | This paper studies algorithmic decision-making in the presence of strategic individual behaviors, where an ML model is used to make decisions about human agents and the latter can adapt their behavior strategically to improve their future data. Existing results on strategic learning have largely focused on the linear setting where agents with linear labeling functions best respond to a (noisy) linear decision policy. Instead, this work focuses on general non-linear settings where agents respond to the decision policy with only "local information" of the policy. Moreover, we simultaneously consider the objectives of maximizing decision-maker welfare (model prediction accuracy), social welfare (agent improvement caused by strategic behaviors), and agent welfare (the extent that ML underestimates the agents). We first generalize the agent best response model in previous works to the non-linear setting, then reveal the compatibility of welfare objectives. We show the three welfare can attain the optimum simultaneously only under restrictive conditions which are challenging to achieve in non-linear settings. The theoretical results imply that existing works solely maximizing the welfare of a subset of parties inevitably diminish the welfare of the others. We thus claim the necessity of balancing the welfare of each party in non-linear settings and propose an irreducible optimization algorithm suitable for general strategic learning. Experiments on synthetic and real data validate the proposed algorithm. | [
"['Tian Xie' 'Xueru Zhang']"
]
|
null | null | 2405.01814 | null | null | http://arxiv.org/pdf/2405.01814v1 | 2024-05-03T02:15:15Z | 2024-05-03T02:15:15Z | Efficient and Economic Large Language Model Inference with Attention
Offloading | Transformer-based large language models (LLMs) exhibit impressive performance in generative tasks but introduce significant challenges in real-world serving due to inefficient use of the expensive, computation-optimized accelerators. This mismatch arises from the autoregressive nature of LLMs, where the generation phase comprises operators with varying resource demands. Specifically, the attention operator is memory-intensive, exhibiting a memory access pattern that clashes with the strengths of modern accelerators, especially as context length increases. To enhance the efficiency and cost-effectiveness of LLM serving, we introduce the concept of attention offloading. This approach leverages a collection of cheap, memory-optimized devices for the attention operator while still utilizing high-end accelerators for other parts of the model. This heterogeneous setup ensures that each component is tailored to its specific workload, maximizing overall performance and cost efficiency. Our comprehensive analysis and experiments confirm the viability of splitting the attention computation over multiple devices. Also, the communication bandwidth required between heterogeneous devices proves to be manageable with prevalent networking technologies. To further validate our theory, we develop Lamina, an LLM inference system that incorporates attention offloading. Experimental results indicate that Lamina can provide 1.48x-12.1x higher estimated throughput per dollar than homogeneous solutions. | [
"['Shaoyuan Chen' 'Yutong Lin' 'Mingxing Zhang' 'Yongwei Wu']"
]
|
null | null | 2405.01817 | null | null | http://arxiv.org/pdf/2405.01817v1 | 2024-05-03T02:30:57Z | 2024-05-03T02:30:57Z | Uniformly Stable Algorithms for Adversarial Training and Beyond | In adversarial machine learning, neural networks suffer from a significant issue known as robust overfitting, where the robust test accuracy decreases over epochs (Rice et al., 2020). Recent research conducted by Xing et al.,2021; Xiao et al., 2022 has focused on studying the uniform stability of adversarial training. Their investigations revealed that SGD-based adversarial training fails to exhibit uniform stability, and the derived stability bounds align with the observed phenomenon of robust overfitting in experiments. This motivates us to develop uniformly stable algorithms specifically tailored for adversarial training. To this aim, we introduce Moreau envelope-$mathcal{A}$, a variant of the Moreau Envelope-type algorithm. We employ a Moreau envelope function to reframe the original problem as a min-min problem, separating the non-strong convexity and non-smoothness of the adversarial loss. Then, this approach alternates between solving the inner and outer minimization problems to achieve uniform stability without incurring additional computational overhead. In practical scenarios, we show the efficacy of ME-$mathcal{A}$ in mitigating the issue of robust overfitting. Beyond its application in adversarial training, this represents a fundamental result in uniform stability analysis, as ME-$mathcal{A}$ is the first algorithm to exhibit uniform stability for weakly-convex, non-smooth problems. | [
"['Jiancong Xiao' 'Jiawei Zhang' 'Zhi-Quan Luo' 'Asuman Ozdaglar']"
]
|
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