Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
2107.04422	Policy Gradient Methods for Distortion Risk Measures	We propose policy gradient algorithms which learn risk-sensitive policies in a reinforcement learning (RL) framework. Our proposed algorithms maximize the distortion risk measure (DRM) of the cumulative reward in an episodic Markov decision process in on-policy and off-policy RL settings, respectively. We derive a vari...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	245,456
2405.16191	Rocket Landing Control with Grid Fins and Path-following using MPC	In this project, we attempt to optimize a landing trajectory of a rocket. The goal is to minimize the total fuel consumption during the landing process using different techniques. Once the optimal and feasible trajectory is generated using batch approach, we attempt to follow the path using a Model Predictive Control (...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	457,301
1204.4347	Change-Of-Bases Abstractions for Non-Linear Systems	We present abstraction techniques that transform a given non-linear dynamical system into a linear system or an algebraic system described by polynomials of bounded degree, such that, invariant properties of the resulting abstraction can be used to infer invariants for the original system. The abstraction techniques re...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	15,588
2110.12403	Learning to Estimate Without Bias	The Gauss Markov theorem states that the weighted least squares estimator is a linear minimum variance unbiased estimation (MVUE) in linear models. In this paper, we take a first step towards extending this result to non linear settings via deep learning with bias constraints. The classical approach to designing non-li...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	262,838
2203.03872	Visual anomaly detection in video by variational autoencoder	Video anomalies detection is the intersection of anomaly detection and visual intelligence. It has commercial applications in surveillance, security, self-driving cars and crop monitoring. Videos can capture a variety of anomalies. Due to efforts needed to label training data, unsupervised approaches to train anomaly d...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	284,257
2412.00176	Art-Free Generative Models: Art Creation Without Graphic Art Knowledge	We explore the question: "How much prior art knowledge is needed to create art?" To investigate this, we propose a text-to-image generation model trained without access to art-related content. We then introduce a simple yet effective method to learn an art adapter using only a few examples of selected artistic styles. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	512,552
2208.09734	A Multi-Head Model for Continual Learning via Out-of-Distribution Replay	This paper studies class incremental learning (CIL) of continual learning (CL). Many approaches have been proposed to deal with catastrophic forgetting (CF) in CIL. Most methods incrementally construct a single classifier for all classes of all tasks in a single head network. To prevent CF, a popular approach is to mem...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	313,816
2501.06923	Optimal Online Bookmaking for Binary Games	In online betting, the bookmaker can update the payoffs it offers on a particular event many times before the event takes place, and the updated payoffs may depend on the bets accumulated thus far. We study the problem of bookmaking with the goal of maximizing the return in the worst-case, with respect to the gamblers'...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	524,186
2305.11381	Online Learning in a Creator Economy	The creator economy has revolutionized the way individuals can profit through online platforms. In this paper, we initiate the study of online learning in the creator economy by modeling the creator economy as a three-party game between the users, platform, and content creators, with the platform interacting with the c...	false	false	false	false	false	true	true	false	false	false	false	false	false	true	false	false	false	true	365,500
1806.05938	Query K-means Clustering and the Double Dixie Cup Problem	We consider the problem of approximate $K$-means clustering with outliers and side information provided by same-cluster queries and possibly noisy answers. Our solution shows that, under some mild assumptions on the smallest cluster size, one can obtain an $(1+\epsilon)$-approximation for the optimal potential with pro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	100,588
1911.05649	Air-Writing Translater: A Novel Unsupervised Domain Adaptation Method for Inertia-Trajectory Translation of In-air Handwriting	As a new way of human-computer interaction, inertial sensor based in-air handwriting can provide a natural and unconstrained interaction to express more complex and richer information in 3D space. However, most of the existing in-air handwriting work is mainly focused on handwritten character recognition, which makes t...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	153,329
2410.00030	AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification	Network monitoring generates massive volumes of IP flow records, posing significant challenges for storage and analysis. This paper presents a novel deep learning-based approach to compressing these records using autoencoders, enabling direct analysis of compressed data without requiring decompression. Unlike tradition...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	493,197
2301.09783	Several families of ternary negacyclic codes and their duals	Constacyclic codes contain cyclic codes as a subclass and have nice algebraic structures. Constacyclic codes have theoretical importance, as they are connected to a number of areas of mathematics and outperform cyclic codes in several aspects. Negacyclic codes are a subclass of constacyclic codes and are distance-optim...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	341,603
2105.04181	KDExplainer: A Task-oriented Attention Model for Explaining Knowledge Distillation	Knowledge distillation (KD) has recently emerged as an efficacious scheme for learning compact deep neural networks (DNNs). Despite the promising results achieved, the rationale that interprets the behavior of KD has yet remained largely understudied. In this paper, we introduce a novel task-oriented attention model, t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	234,417
2404.10641	A Cloud Resources Portfolio Optimization Business Model -- From Theory to Practice	Cloud resources have become increasingly important, with many businesses using cloud solutions to supplement or outright replace their existing IT infrastructure. However, as there is a plethora of providers with varying products, services, and markets, it has become increasingly more challenging to keep track of the b...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	447,185
1301.2293	Aggregating Learned Probabilistic Beliefs	We consider the task of aggregating beliefs of severalexperts. We assume that these beliefs are represented as probabilitydistributions. We argue that the evaluation of any aggregationtechnique depends on the semantic context of this task. We propose aframework, in which we assume that nature generates samples from a`t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	20,968
2109.09791	Prediction of severe thunderstorm events with ensemble deep learning and radar data	The problem of nowcasting extreme weather events can be addressed by applying either numerical methods for the solution of dynamic model equations or data-driven artificial intelligence algorithms. Within this latter framework, the present paper illustrates how a deep learning method, exploiting videos of radar reflect...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	256,392
2003.03913	FarSee-Net: Real-Time Semantic Segmentation by Efficient Multi-scale Context Aggregation and Feature Space Super-resolution	Real-time semantic segmentation is desirable in many robotic applications with limited computation resources. One challenge of semantic segmentation is to deal with the object scale variations and leverage the context. How to perform multi-scale context aggregation within limited computation budget is important. In thi...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	167,403
2209.04696	Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning	Flocking is a very challenging problem in a multi-agent system; traditional flocking methods also require complete knowledge of the environment and a precise model for control. In this paper, we propose Evolutionary Multi-Agent Reinforcement Learning (EMARL) in flocking tasks, a hybrid algorithm that combines cooperati...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	316,855
1312.3543	Optimal Distributed Control for Networked Control Systems with Delays	In networked control systems (NCS), sensing and control signals between the plant and controllers are typically transmitted wirelessly. Thus, the time delay plays an important role for the stability of NCS, especially with distributed controllers. In this paper, the optimal control strategy is derived for distributed c...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	29,048
2412.03735	VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large Language Models for Video Understanding	Multimodal large language models (MLLMs) have recently shown significant advancements in video understanding, excelling in content reasoning and instruction-following tasks. However, the problem of hallucination, where models generate inaccurate or misleading content, remains underexplored in the video domain. Building...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	514,080
1808.09658	APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning	We propose a method to perform automatic document summarisation without using reference summaries. Instead, our method interactively learns from users' preferences. The merit of preference-based interactive summarisation is that preferences are easier for users to provide than reference summaries. Existing preference-b...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	106,242
1501.01372	Weighted Schatten $p$-Norm Minimization for Image Denoising with Local and Nonlocal Regularization	This paper presents a patch-wise low-rank based image denoising method with constrained variational model involving local and nonlocal regularization. On one hand, recent patch-wise methods can be represented as a low-rank matrix approximation problem whose convex relaxation usually depends on nuclear norm minimization...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	39,080
2412.05232	LIAR: Leveraging Inference Time Alignment (Best-of-N) to Jailbreak LLMs in Seconds	Traditional jailbreaks have successfully exposed vulnerabilities in LLMs, primarily relying on discrete combinatorial optimization, while more recent methods focus on training LLMs to generate adversarial prompts. However, both approaches are computationally expensive and slow, often requiring significant resources to ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	514,743
1912.13007	World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces	Some of the most important tasks take place in environments which lack cheap and perfect simulators, thus hampering the application of model-free reinforcement learning (RL). While model-based RL aims to learn a dynamics model, in a more general case the learner does not know a priori what the action space is. Here we ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	158,988
2009.03836	Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learning	Reinforcement learning (RL) is increasingly adopted in job shop scheduling problems (JSSP). But RL for JSSP is usually done using a vectorized representation of machine features as the state space. It has three major problems: (1) the relationship between the machine units and the job sequence is not fully captured, (2...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	194,904
1902.09525	Fast Decoder for Overloaded Uniquely Decodable Synchronous Optical CDMA	In this paper, we propose a fast decoder algorithm for uniquely decodable (errorless) code sets for overloaded synchronous optical code-division multiple-access (O-CDMA) systems. The proposed decoder is designed in a such a way that the users can uniquely recover the information bits with a very simple decoder, which u...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	122,431
1507.00567	Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution	Cloud controllers aim at responding to application demands by automatically scaling the compute resources at runtime to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of adaptation rules. However, for a cloud provider, ap...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	true	44,772
1910.02035	Manufacturing Dispatching using Reinforcement and Transfer Learning	Efficient dispatching rule in manufacturing industry is key to ensure product on-time delivery and minimum past-due and inventory cost. Manufacturing, especially in the developed world, is moving towards on-demand manufacturing meaning a high mix, low volume product mix. This requires efficient dispatching that can wor...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	148,112
2109.10385	Learning to Guide Human Attention on Mobile Telepresence Robots with 360 Vision	Mobile telepresence robots (MTRs) allow people to navigate and interact with a remote environment that is in a place other than the person's true location. Thanks to the recent advances in 360 degree vision, many MTRs are now equipped with an all-degree visual perception capability. However, people's visual field horiz...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	256,587
2410.10735	Embedding Self-Correction as an Inherent Ability in Large Language Models for Enhanced Mathematical Reasoning	Accurate mathematical reasoning with Large Language Models (LLMs) is crucial in revolutionizing domains that heavily rely on such reasoning. However, LLMs often encounter difficulties in certain aspects of mathematical reasoning, leading to flawed reasoning and erroneous results. To mitigate these issues, we introduce ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	498,204
cs/0511028	MIMO Diversity in the Presence of Double Scattering	The potential benefits of multiple-antenna systems may be limited by two types of channel degradations rank deficiency and spatial fading correlation of the channel. In this paper, we assess the effects of these degradations on the diversity performance of multiple-input multiple-output (MIMO) systems, with an emphasis...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	539,066
1901.01091	Adaptive Density Estimation for Generative Models	Unsupervised learning of generative models has seen tremendous progress over recent years, in particular due to generative adversarial networks (GANs), variational autoencoders, and flow-based models. GANs have dramatically improved sample quality, but suffer from two drawbacks: (i) they mode-drop, i.e., do not cover t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	117,919
2110.05882	Concepts of Self-maintaining Robots and Their Design	This paper proposes an initial theory for robotic systems that can be fully self-maintaining. The new design principles focus on functional survival of the robots over long periods of time without human maintenance. Self-maintaining semi-autonomous mobile robots are in great demand in nuclear disposal sites from where ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	260,440
1810.03988	GPU based Parallel Optimization for Real Time Panoramic Video Stitching	Panoramic video is a sort of video recorded at the same point of view to record the full scene. With the development of video surveillance and the requirement for 3D converged video surveillance in smart cities, CPU and GPU are required to possess strong processing abilities to make panoramic video. The traditional pan...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	109,935
1811.09747	Amortized Bayesian inference for clustering models	We develop methods for efficient amortized approximate Bayesian inference over posterior distributions of probabilistic clustering models, such as Dirichlet process mixture models. The approach is based on mapping distributed, symmetry-invariant representations of cluster arrangements into conditional probabilities. Th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	114,310
2304.00615	An Intrinsic Framework of Information Retrieval Evaluation Measures	Information retrieval (IR) evaluation measures are cornerstones for determining the suitability and task performance efficiency of retrieval systems. Their metric and scale properties enable to compare one system against another to establish differences or similarities. Based on the representational theory of measureme...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	355,752
1808.06306	Reed-Solomon codes over small fields with constrained generator matrices	We give constructions of some special cases of $[n,k]$ Reed-Solomon codes over finite fields of size at least $n$ and $n+1$ whose generator matrices have constrained support. Furthermore, we consider a generalisation of the GM-MDS conjecture proposed by Lovett in 2018. We show that Lovett's conjecture is false in gener...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	105,509
1804.03821	ExFuse: Enhancing Feature Fusion for Semantic Segmentation	Modern semantic segmentation frameworks usually combine low-level and high-level features from pre-trained backbone convolutional models to boost performance. In this paper, we first point out that a simple fusion of low-level and high-level features could be less effective because of the gap in semantic levels and spa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	94,702
2003.12088	An Efficient Machine Learning Approach for Accurate Short Term Solar Power Prediction	Solar based electricity generations have experienced strong and impactful growth in recent years. The regulation, scheduling, dispatching, and unit commitment of intermittent solar power is dependent on the accuracy of the forecasting methods. In this paper, a robust Expanded Extreme Learning Machine (EELM) is proposed...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	169,807
2305.13628	Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning	In cross-lingual named entity recognition (NER), self-training is commonly used to bridge the linguistic gap by training on pseudo-labeled target-language data. However, due to sub-optimal performance on target languages, the pseudo labels are often noisy and limit the overall performance. In this work, we aim to impro...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	366,590
2307.02720	On-Device Constrained Self-Supervised Speech Representation Learning for Keyword Spotting via Knowledge Distillation	Large self-supervised models are effective feature extractors, but their application is challenging under on-device budget constraints and biased dataset collection, especially in keyword spotting. To address this, we proposed a knowledge distillation-based self-supervised speech representation learning (S3RL) architec...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	377,784
2106.11918	From SIR to SEAIRD: a novel data-driven modeling approach based on the Grey-box System Theory to predict the dynamics of COVID-19	Common compartmental modeling for COVID-19 is based on a priori knowledge and numerous assumptions. Additionally, they do not systematically incorporate asymptomatic cases. Our study aimed at providing a framework for data-driven approaches, by leveraging the strengths of the grey-box system theory or grey-box identifi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	242,556
2408.16907	Ig3D: Integrating 3D Face Representations in Facial Expression Inference	Reconstructing 3D faces with facial geometry from single images has allowed for major advances in animation, generative models, and virtual reality. However, this ability to represent faces with their 3D features is not as fully explored by the facial expression inference (FEI) community. This study therefore aims to i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	484,497
2007.08001	Computation Offloading in Beyond 5G Networks: A Distributed Learning Framework and Applications	Facing the trend of merging wireless communications and multi-access edge computing (MEC), this article studies computation offloading in the beyond fifth-generation networks. To address the technical challenges originating from the uncertainties and the sharing of limited resource in an MEC system, we formulate the co...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	187,486
1609.07114	A Stable FDTD Method with Embedded Reduced-Order Models	The computational efficiency of the Finite-Difference Time-Domain (FDTD) method can be significantly reduced by the presence of complex objects with fine features. Small geometrical details impose a fine mesh and a reduced time step, significantly increasing computational cost. Model order reduction has been proposed a...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	61,394
2102.00397	Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting	Probabilistic time series forecasting involves estimating the distribution of future based on its history, which is essential for risk management in downstream decision-making. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are pa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	217,767
2204.08651	Evolving Programmable Computational Metamaterials	Granular metamaterials are a promising choice for the realization of mechanical computing devices. As preliminary evidence of this, we demonstrate here how to embed Boolean logic gates (AND and XOR) into a granular metamaterial by evolving where particular grains are placed in the material. Our results confirm the exis...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	292,164
2406.13663	Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation	Ensuring the verifiability of model answers is a fundamental challenge for retrieval-augmented generation (RAG) in the question answering (QA) domain. Recently, self-citation prompting was proposed to make large language models (LLMs) generate citations to supporting documents along with their answers. However, self-ci...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	465,944
2409.01534	Think Twice Before Recognizing: Large Multimodal Models for General Fine-grained Traffic Sign Recognition	We propose a new strategy called think twice before recognizing to improve fine-grained traffic sign recognition (TSR). Fine-grained TSR in the wild is difficult due to the complex road conditions, and existing approaches particularly struggle with cross-country TSR when data is lacking. Our strategy achieves effective...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	485,369
2110.11572	Reinforcement Learning for Process Control with Application in Semiconductor Manufacturing	Process control is widely discussed in the manufacturing process, especially for semiconductor manufacturing. Due to unavoidable disturbances in manufacturing, different process controllers are proposed to realize variation reduction. Since reinforcement learning (RL) has shown great advantages in learning actions from...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	262,530
2002.09182	Quantum secret sharing using GHZ state qubit positioning and selective qubits strategy for secret reconstruction	The work presents a novel quantum secret sharing strategy based on GHZ product state sharing between three parties. The dealer, based on the classical information to be shared, toggles his qubit and shares the product state. The other parties make their Bell measurements and collude to reconstruct the secret. Unlike th...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	164,999
1807.06233	Robust Deep Multi-modal Learning Based on Gated Information Fusion Network	The goal of multi-modal learning is to use complimentary information on the relevant task provided by the multiple modalities to achieve reliable and robust performance. Recently, deep learning has led significant improvement in multi-modal learning by allowing for the information fusion in the intermediate feature lev...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	103,088
2209.11034	SEER: Safe Efficient Exploration for Aerial Robots using Learning to Predict Information Gain	We address the problem of efficient 3-D exploration in indoor environments for micro aerial vehicles with limited sensing capabilities and payload/power constraints. We develop an indoor exploration framework that uses learning to predict the occupancy of unseen areas, extracts semantic features, samples viewpoints to ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	319,055
2109.04405	An Accelerated Proximal Gradient-based Model Predictive Control Algorithm	In this letter, an accelerated quadratic programming (QP) algorithm is proposed based on the proximal gradient method. The algorithm can achieve convergence rate $O(1/p^{\alpha})$, where $p$ is the iteration number and $\alpha$ is the given positive integer. The proposed algorithm improves the convergence rate of exist...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	254,394
2501.03085	Personalized Fashion Recommendation with Image Attributes and Aesthetics Assessment	Personalized fashion recommendation is a difficult task because 1) the decisions are highly correlated with users' aesthetic appetite, which previous work frequently overlooks, and 2) many new items are constantly rolling out that cause strict cold-start problems in the popular identity (ID)-based recommendation method...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	522,757
1703.04096	Improving Interpretability of Deep Neural Networks with Semantic Information	Interpretability of deep neural networks (DNNs) is essential since it enables users to understand the overall strengths and weaknesses of the models, conveys an understanding of how the models will behave in the future, and how to diagnose and correct potential problems. However, it is challenging to reason about what ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	69,835
2302.14063	How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?	Automatic recommendation systems based on deep neural networks have become extremely popular during the last decade. Some of these systems can however be used for applications which are ranked as High Risk by the European Commission in the A.I. act, as for instance for online job candidate recommendation. When used in ...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	348,152
2310.07807	FedSym: Unleashing the Power of Entropy for Benchmarking the Algorithms for Federated Learning	Federated learning (FL) is a decentralized machine learning approach where independent learners process data privately. Its goal is to create a robust and accurate model by aggregating and retraining local models over multiple rounds. However, FL faces challenges regarding data heterogeneity and model aggregation effec...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	399,130
2006.04391	AutoMat -- Automatic Differentiation for Generalized Standard Materials on GPUs	We propose a universal method for the evaluation of generalized standard materials that greatly simplifies the material law implementation process. By means of automatic differentiation and a numerical integration scheme, AutoMat reduces the implementation effort to two potential functions. By moving AutoMat to the GPU...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	180,680
2112.15011	Radiology Report Generation with a Learned Knowledge Base and Multi-modal Alignment	In clinics, a radiology report is crucial for guiding a patient's treatment. However, writing radiology reports is a heavy burden for radiologists. To this end, we present an automatic, multi-modal approach for report generation from a chest x-ray. Our approach, motivated by the observation that the descriptions in rad...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	273,672
2104.12005	Wireless Federated Learning (WFL) for 6G Networks -- Part II: The Compute-then-Transmit NOMA Paradigm	As it has been discussed in the first part of this work, the utilization of advanced multiple access protocols and the joint optimization of the communication and computing resources can facilitate the reduction of delay for wireless federated learning (WFL), which is of paramount importance for the efficient integrati...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	232,084
1603.02416	Correlation Measure Equivalence in Dynamic Causal Structures of Quantum Gravity	We prove an equivalence transformation between the correlation measure functions of the causally-unbiased quantum gravity space and the causally-biased standard space. The theory of quantum gravity fuses the dynamic (nonfixed) causal structure of general relativity and the quantum uncertainty of quantum mechanics. In a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	53,010
2211.08900	Convergence analysis of unsupervised Legendre-Galerkin neural networks for linear second-order elliptic PDEs	In this paper, we perform the convergence analysis of unsupervised Legendre--Galerkin neural networks (ULGNet), a deep-learning-based numerical method for solving partial differential equations (PDEs). Unlike existing deep learning-based numerical methods for PDEs, the ULGNet expresses the solution as a spectral expans...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	330,810
2204.00266	Multifaceted Improvements for Conversational Open-Domain Question Answering	Open-domain question answering (OpenQA) is an important branch of textual QA which discovers answers for the given questions based on a large number of unstructured documents. Effectively mining correct answers from the open-domain sources still has a fair way to go. Existing OpenQA systems might suffer from the issues...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	289,194
1907.03395	Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks	Predicting the future trajectories of multiple interacting agents in a scene has become an increasingly important problem for many different applications ranging from control of autonomous vehicles and social robots to security and surveillance. This problem is compounded by the presence of social interactions between ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	137,852
2104.10535	Exploiting Learned Policies in Focal Search	Recent machine-learning approaches to deterministic search and domain-independent planning employ policy learning to speed up search. Unfortunately, when attempting to solve a search problem by successively applying a policy, no guarantees can be given on solution quality. The problem of how to effectively use a learne...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	231,615
1102.5549	Instant Replay: Investigating statistical Analysis in Sports	Technology has had an unquestionable impact on the way people watch sports. Along with this technological evolution has come a higher standard to ensure a good viewing experience for the casual sports fan. It can be argued that the pervasion of statistical analysis in sports serves to satiate the fan's desire for detai...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	9,399
2112.13809	Improving Deep Image Matting via Local Smoothness Assumption	Natural image matting is a fundamental and challenging computer vision task. Conventionally, the problem is formulated as an underconstrained problem. Since the problem is ill-posed, further assumptions on the data distribution are required to make the problem well-posed. For classical matting methods, a commonly adopt...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	273,363
2202.12003	IBIA: Bayesian Inference via Incremental Build-Infer-Approximate operations on Clique Trees	Exact inference in Bayesian networks is intractable and has an exponential dependence on the size of the largest clique in the corresponding clique tree (CT), necessitating approximations. Factor based methods to bound clique sizes are more accurate than structure based methods, but expensive since they involve inferen...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	282,081
2402.06640	Modeling and Optimization of Epidemiological Control Policies Through Reinforcement Learning	Pandemics involve the high transmission of a disease that impacts global and local health and economic patterns. The impact of a pandemic can be minimized by enforcing certain restrictions on a community. However, while minimizing infection and death rates, these restrictions can also lead to economic crises. Epidemiol...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	428,373
2305.09625	Conditional variational autoencoder with Gaussian process regression recognition for parametric models	In this article, we present a data-driven method for parametric models with noisy observation data. Gaussian process regression based reduced order modeling (GPR-based ROM) can realize fast online predictions without using equations in the offline stage. However, GPR-based ROM does not perform well for complex systems ...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	364,709
2106.13081	Artifact Detection and Correction in EEG data: A Review	Electroencephalography (EEG) has countless applications across many of fields. However, EEG applications are limited by low signal-to-noise ratios. Multiple types of artifacts contribute to the noisiness of EEG, and many techniques have been proposed to detect and correct these artifacts. These techniques range from si...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	242,955
1503.06391	Predictive model of the human muscle fatigue: application to repetitive push-pull tasks with light external load	Repetitive tasks in industrial works may contribute to health problems among operators, such as musculo-skeletal disorders, in part due to insufficient control of muscle fatigue. In this paper, a predictive model of fatigue is proposed for repetitive push/pull operations. Assumptions generally accepted in the literatur...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	41,340
2110.10220	Patch Based Transformation for Minimum Variance Beamformer Image Approximation Using Delay and Sum Pipeline	In the recent past, there have been several efforts in accelerating computationally heavy beamforming algorithms such as minimum variance distortionless response (MVDR) beamforming to achieve real-time performance comparable to the popular delay and sum (DAS) beamforming. This has been achieved using a variety of neura...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	262,070
2405.10027	The Real Price of Bandit Information in Multiclass Classification	We revisit the classical problem of multiclass classification with bandit feedback (Kakade, Shalev-Shwartz and Tewari, 2008), where each input classifies to one of $K$ possible labels and feedback is restricted to whether the predicted label is correct or not. Our primary inquiry is with regard to the dependency on the...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	454,623
1310.3595	Stabilizing discrete-time switched linear systems	This article deals with stabilizing discrete-time switched linear systems. Our contributions are threefold: Firstly, given a family of linear systems possibly containing unstable dynamics, we propose a large class of switching signals that stabilize a switched system generated by the switching signal and the given fami...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	27,757
cs/0408064	Proportional Conflict Redistribution Rules for Information Fusion	In this paper we propose five versions of a Proportional Conflict Redistribution rule (PCR) for information fusion together with several examples. From PCR1 to PCR2, PCR3, PCR4, PCR5 one increases the complexity of the rules and also the exactitude of the redistribution of conflicting masses. PCR1 restricted from the h...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	538,323
2107.02086	One-Cycle Pruning: Pruning ConvNets Under a Tight Training Budget	Introducing sparsity in a neural network has been an efficient way to reduce its complexity while keeping its performance almost intact. Most of the time, sparsity is introduced using a three-stage pipeline: 1) train the model to convergence, 2) prune the model according to some criterion, 3) fine-tune the pruned model...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	244,697
1711.06663	Multiresolution and Hierarchical Analysis of Astronomical Spectroscopic Cubes using 3D Discrete Wavelet Transform	The intrinsically hierarchical and blended structure of interstellar molecular clouds, plus the always increasing resolution of astronomical instruments, demand advanced and automated pattern recognition techniques for identifying and connecting source components in spectroscopic cubes. We extend the work done in multi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	84,821
2305.09645	StructGPT: A General Framework for Large Language Model to Reason over Structured Data	In this paper, we study how to improve the zero-shot reasoning ability of large language models~(LLMs) over structured data in a unified way. Inspired by the study on tool augmentation for LLMs, we develop an \emph{Iterative Reading-then-Reasoning~(IRR)} approach for solving question answering tasks based on structured...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	364,716
2311.09027	Assessing the Robustness of Intelligence-Driven Reinforcement Learning	Robustness to noise is of utmost importance in reinforcement learning systems, particularly in military contexts where high stakes and uncertain environments prevail. Noise and uncertainty are inherent features of military operations, arising from factors such as incomplete information, adversarial actions, or unpredic...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	407,962
2405.20513	Deep Modeling of Non-Gaussian Aleatoric Uncertainty	Deep learning offers promising new ways to accurately model aleatoric uncertainty in robotic estimation systems, particularly when the uncertainty distributions do not conform to traditional assumptions of being fixed and Gaussian. In this study, we formulate and evaluate three fundamental deep learning approaches for ...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	459,384
2405.05722	TraceGrad: a Framework Learning Expressive SO(3)-equivariant Non-linear Representations for Electronic-Structure Hamiltonian Prediction	We propose a framework to combine strong non-linear expressiveness with strict SO(3)-equivariance in prediction of the electronic-structure Hamiltonian, by exploring the mathematical relationships between SO(3)-invariant and SO(3)-equivariant quantities and their representations. The proposed framework, called TraceGra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	453,028
1509.08392	Properties of Eventually Positive Linear Input-Output Systems	In this paper, we consider the systems with trajectories originating in the nonnegative orthant becoming nonnegative after some finite time transient. First we consider dynamical systems (i.e., fully observable systems with no inputs), which we call eventually positive. We compute forward-invariant cones and Lyapunov f...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	47,367
2412.08291	Code LLMs: A Taxonomy-based Survey	Large language models (LLMs) have demonstrated remarkable capabilities across various NLP tasks and have recently expanded their impact to coding tasks, bridging the gap between natural languages (NL) and programming languages (PL). This taxonomy-based survey provides a comprehensive analysis of LLMs in the NL-PL domai...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	516,031
2404.15814	Fast Ensembling with Diffusion Schr\"odinger Bridge	Deep Ensemble (DE) approach is a straightforward technique used to enhance the performance of deep neural networks by training them from different initial points, converging towards various local optima. However, a limitation of this methodology lies in its high computational overhead for inference, arising from the ne...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	449,251
2310.04664	Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition	This paper focuses on the research of micro-expression recognition (MER) and proposes a flexible and reliable deep learning method called learning to rank onset-occurring-offset representations (LTR3O). The LTR3O method introduces a dynamic and reduced-size sequence structure known as 3O, which consists of onset, occur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	397,751
2410.18875	Exploring the Universe with SNAD: Anomaly Detection in Astronomy	SNAD is an international project with a primary focus on detecting astronomical anomalies within large-scale surveys, using active learning and other machine learning algorithms. The work carried out by SNAD not only contributes to the discovery and classification of various astronomical phenomena but also enhances our...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	502,066
2004.12207	Internet-human infrastructures: Lessons from Havana's StreetNet	We propose a mixed-methods approach to understanding the human infrastructure underlying StreetNet (SNET), a distributed, community-run intranet that serves as the primary 'Internet' in Havana, Cuba. We bridge ethnographic studies and the study of social networks and organizations to understand the way that power is em...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	174,166
2301.05466	A Nearly-Linear Time Algorithm for Minimizing Risk of Conflict in Social Networks	Concomitant with the tremendous prevalence of online social media platforms, the interactions among individuals are unprecedentedly enhanced. People are free to interact with acquaintances, express and exchange their own opinions through commenting, liking, retweeting on online social media, leading to resistance, cont...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	340,354
2104.10970	Time series analysis with dynamic law exploration	In this paper we examine, how the dynamic laws governing the time evolution of a time series can be identified. We give a finite difference equation as well as a differential equation representation for that. We also study, how the required symmetries, like time reversal can be imposed on the laws. We study the compres...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	231,773
2104.10879	Efficient LiDAR Odometry for Autonomous Driving	LiDAR odometry plays an important role in self-localization and mapping for autonomous navigation, which is usually treated as a scan registration problem. Although having achieved promising performance on KITTI odometry benchmark, the conventional searching tree-based approach still has the difficulty in dealing with ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	231,749
2303.06114	Optimal Design of Validation Experiments for the Prediction of Quantities of Interest	Numerical predictions of quantities of interest measured within physical systems rely on the use of mathematical models that should be validated, or at best, not invalidated. Model validation usually involves the comparison of experimental data (outputs from the system of interest) and model predictions, both obtained ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	350,697
2410.04542	Generative Flows on Synthetic Pathway for Drug Design	Generative models in drug discovery have recently gained attention as efficient alternatives to brute-force virtual screening. However, most existing models do not account for synthesizability, limiting their practical use in real-world scenarios. In this paper, we propose RxnFlow, which sequentially assembles molecule...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	495,336
2311.17655	Vulnerability of Automatic Identity Recognition to Audio-Visual Deepfakes	The task of deepfakes detection is far from being solved by speech or vision researchers. Several publicly available databases of fake synthetic video and speech were built to aid the development of detection methods. However, existing databases typically focus on visual or voice modalities and provide no proof that th...	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	411,373
2105.02184	PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond	Reducing the complexity of the pipeline of instance segmentation is crucial for real-world applications. This work addresses this issue by introducing an anchor-box free and single-shot instance segmentation framework, termed PolarMask, which reformulates the instance segmentation problem as predicting the contours of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	233,746
2408.15954	InstanSeg: an embedding-based instance segmentation algorithm optimized for accurate, efficient and portable cell segmentation	Cell and nucleus segmentation are fundamental tasks for quantitative bioimage analysis. Despite progress in recent years, biologists and other domain experts still require novel algorithms to handle increasingly large and complex real-world datasets. These algorithms must not only achieve state-of-the-art accuracy, but...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	484,137
2303.12711	Geometry-Aware Latent Representation Learning for Modeling Disease Progression of Barrett's Esophagus	Barrett's Esophagus (BE) is the only precursor known to Esophageal Adenocarcinoma (EAC), a type of esophageal cancer with poor prognosis upon diagnosis. Therefore, diagnosing BE is crucial in preventing and treating esophageal cancer. While supervised machine learning supports BE diagnosis, high interobserver variabili...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	353,351
1410.5884	Mean-Field Networks	The mean field algorithm is a widely used approximate inference algorithm for graphical models whose exact inference is intractable. In each iteration of mean field, the approximate marginals for each variable are updated by getting information from the neighbors. This process can be equivalently converted into a feedf...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	36,948

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