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
2101.03285	Detecting, Localising and Classifying Polyps from Colonoscopy Videos using Deep Learning	In this paper, we propose and analyse a system that can automatically detect, localise and classify polyps from colonoscopy videos. The detection of frames with polyps is formulated as a few-shot anomaly classification problem, where the training set is highly imbalanced with the large majority of frames consisting of ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	214,879
2109.12269	Integrating Recurrent Neural Networks with Data Assimilation for Scalable Data-Driven State Estimation	Data assimilation (DA) is integrated with machine learning in order to perform entirely data-driven online state estimation. To achieve this, recurrent neural networks (RNNs) are implemented as surrogate models to replace key components of the DA cycle in numerical weather prediction (NWP), including the conventional n...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	257,218
2004.05790	Towards Transferable Adversarial Attack against Deep Face Recognition	Face recognition has achieved great success in the last five years due to the development of deep learning methods. However, deep convolutional neural networks (DCNNs) have been found to be vulnerable to adversarial examples. In particular, the existence of transferable adversarial examples can severely hinder the robu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	172,312
1304.5299	Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget	Can we make Bayesian posterior MCMC sampling more efficient when faced with very large datasets? We argue that computing the likelihood for N datapoints in the Metropolis-Hastings (MH) test to reach a single binary decision is computationally inefficient. We introduce an approximate MH rule based on a sequential hypoth...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	24,071
2010.12778	New compound control algorithm in sliding mode control to reduce the chattering phenomenon: experimental validation	In this work, a new SMS is proposed to achieve high tracking and suitable robustness. However, the chattering phenomenon should be regarded as the main drawback of the SMC. Therefore, a new compound control algorithm is used for reducing the chattering phenomenon. The applied compound control law constantly evaluates t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	202,846
2012.08559	Intrusion detection in computer systems by using artificial neural networks with Deep Learning approaches	Intrusion detection into computer networks has become one of the most important issues in cybersecurity. Attackers keep on researching and coding to discover new vulnerabilities to penetrate information security system. In consequence computer systems must be daily upgraded using up-to-date techniques to keep hackers a...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	211,791
2410.12153	Layer-of-Thoughts Prompting (LoT): Leveraging LLM-Based Retrieval with Constraint Hierarchies	This paper presents a novel approach termed Layer-of-Thoughts Prompting (LoT), which utilizes constraint hierarchies to filter and refine candidate responses to a given query. By integrating these constraints, our method enables a structured retrieval process that enhances explainability and automation. Existing method...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	498,870
1507.02592	Fast rates in statistical and online learning	The speed with which a learning algorithm converges as it is presented with more data is a central problem in machine learning --- a fast rate of convergence means less data is needed for the same level of performance. The pursuit of fast rates in online and statistical learning has led to the discovery of many conditi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	45,002
2109.14850	Matching Markets	Matching markets are of particular interest in computer science and economics literature as they are often used to model real-world phenomena where we aim to equitably distribute a limited amount of resources to multiple agents and determine these distributions efficiently. Although it has been shown that finding marke...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	258,090
2402.10076	QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference	We introduce QUICK, a group of novel optimized CUDA kernels for the efficient inference of quantized Large Language Models (LLMs). QUICK addresses the shared memory bank-conflict problem of state-of-the-art mixed precision matrix multiplication kernels. Our method interleaves the quantized weight matrices of LLMs offli...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	429,802
1002.0110	On Unbiased Estimation of Sparse Vectors Corrupted by Gaussian Noise	We consider the estimation of a sparse parameter vector from measurements corrupted by white Gaussian noise. Our focus is on unbiased estimation as a setting under which the difficulty of the problem can be quantified analytically. We show that there are infinitely many unbiased estimators but none of them has uniforml...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	5,569
2103.04831	Semantic Models for the First-stage Retrieval: A Comprehensive Review	Multi-stage ranking pipelines have been a practical solution in modern search systems, where the first-stage retrieval is to return a subset of candidate documents, and latter stages attempt to re-rank those candidates. Unlike re-ranking stages going through quick technique shifts during past decades, the first-stage r...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	223,777
2209.06013	Virtual Underwater Datasets for Autonomous Inspections	Underwater Vehicles have become more sophisticated, driven by the off-shore sector and the scientific community's rapid advancements in underwater operations. Notably, many underwater tasks, including the assessment of subsea infrastructure, are performed with the assistance of Autonomous Underwater Vehicles (AUVs). Th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	317,267
2411.15715	Task Scheduling for Efficient Inference of Large Language Models on Single Moderate GPU Systems	Large language models~(LLMs) are known for their high demand on computing resources and memory due to their substantial model size, which leads to inefficient inference on moderate GPU systems. Techniques like quantization or pruning can shrink model sizes but often impair accuracy, making them unsuitable for practical...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	510,736
2101.08980	Nonstationary Stochastic Multiarmed Bandits: UCB Policies and Minimax Regret	We study the nonstationary stochastic Multi-Armed Bandit (MAB) problem in which the distribution of rewards associated with each arm are assumed to be time-varying and the total variation in the expected rewards is subject to a variation budget. The regret of a policy is defined by the difference in the expected cumula...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	216,464
2209.07081	DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks	Solutions to differential equations are of significant scientific and engineering relevance. Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving differential equations, but they lack a theoretical justification for the use of any particular loss function. This work presents Different...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	317,623
2411.08875	Causal Explanations for Image Classifiers	Existing algorithms for explaining the output of image classifiers use different definitions of explanations and a variety of techniques to extract them. However, none of the existing tools use a principled approach based on formal definitions of causes and explanations for the explanation extraction. In this paper we ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	508,041
2402.17510	Demonstrating and Reducing Shortcuts in Vision-Language Representation Learning	Vision-language models (VLMs) mainly rely on contrastive training to learn general-purpose representations of images and captions. We focus on the situation when one image is associated with several captions, each caption containing both information shared among all captions and unique information per caption about the...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	433,027
2305.11017	Deep Metric Tensor Regularized Policy Gradient	Policy gradient algorithms are an important family of deep reinforcement learning techniques. Many past research endeavors focused on using the first-order policy gradient information to train policy networks. Different from these works, we conduct research in this paper driven by the believe that properly utilizing an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	365,340
2107.12588	A Novel Interactive Two-stage Joint Retail Electricity Market for Multiple Microgrids	To accommodate the advent of microgrids (MG) managing distributed energy resources (DER) in distribution systems, an interactive two-stage joint retail electricity market mechanism is proposed to provide an effective platform for these prosumers to proactively join in retail transactions. Day-ahead stochastic energy tr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	247,940
2109.01652	Finetuned Language Models Are Zero-Shot Learners	This paper explores a simple method for improving the zero-shot learning abilities of language models. We show that instruction tuning -- finetuning language models on a collection of tasks described via instructions -- substantially improves zero-shot performance on unseen tasks. We take a 137B parameter pretrained ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	253,490
1703.07418	Cognitive Hierarchy Theory for Distributed Resource Allocation in the Internet of Things	In this paper, the problem of distributed resource allocation is studied for an Internet of Things (IoT) system, composed of a heterogeneous group of nodes compromising both machine-type devices (MTDs) and human-type devices (HTDs). The problem is formulated as a noncooperative game between the heterogeneous IoT device...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	70,390
2406.08207	Transformer-based Model for ASR N-Best Rescoring and Rewriting	Voice assistants increasingly use on-device Automatic Speech Recognition (ASR) to ensure speed and privacy. However, due to resource constraints on the device, queries pertaining to complex information domains often require further processing by a search engine. For such applications, we propose a novel Transformer bas...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	463,389
2108.07307	Synthesizing Pareto-Optimal Interpretations for Black-Box Models	We present a new multi-objective optimization approach for synthesizing interpretations that "explain" the behavior of black-box machine learning models. Constructing human-understandable interpretations for black-box models often requires balancing conflicting objectives. A simple interpretation may be easier to under...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	250,875
2206.12052	Learning the policy for mixed electric platoon control of automated and human-driven vehicles at signalized intersection: a random search approach	The upgrading and updating of vehicles have accelerated in the past decades. Out of the need for environmental friendliness and intelligence, electric vehicles (EVs) and connected and automated vehicles (CAVs) have become new components of transportation systems. This paper develops a reinforcement learning framework t...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	304,462
2209.00349	FLAME: Free-form Language-based Motion Synthesis & Editing	Text-based motion generation models are drawing a surge of interest for their potential for automating the motion-making process in the game, animation, or robot industries. In this paper, we propose a diffusion-based motion synthesis and editing model named FLAME. Inspired by the recent successes in diffusion models, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	315,557
2304.02901	SpanRE: Entities and Overlapping Relations Extraction Based on Spans and Entity Attention	Extracting entities and relations is an essential task of information extraction. Triplets extracted from a sentence might overlap with each other. Previous methods either did not address the overlapping issues or solved overlapping issues partially. To tackle triplet overlapping problems completely, firstly we extract...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	356,600
2407.18646	Decoding Knowledge Claims: The Evaluation of Scientific Publication Contributions through Semantic Analysis	The surge in scientific publications challenges the use of publication counts as a measure of scientific progress, requiring alternative metrics that emphasize the quality and novelty of scientific contributions rather than sheer quantity. This paper proposes the use of Relaxed Word Mover's Distance (RWMD), a semantic ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	476,467
2212.11790	Normalized Contrastive Learning for Text-Video Retrieval	Cross-modal contrastive learning has led the recent advances in multimodal retrieval with its simplicity and effectiveness. In this work, however, we reveal that cross-modal contrastive learning suffers from incorrect normalization of the sum retrieval probabilities of each text or video instance. Specifically, we show...	false	false	false	false	false	true	true	false	false	false	false	true	false	false	false	false	false	false	337,889
2405.11346	Decision support system for Forest fire management using Ontology with Big Data and LLMs	Forests are crucial for ecological balance, but wildfires, a major cause of forest loss, pose significant risks. Fire weather indices, which assess wildfire risk and predict resource demands, are vital. With the rise of sensor networks in fields like healthcare and environmental monitoring, semantic sensor networks are...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	455,102
2209.06888	ADAMANT: A Pipeline for Adaptable Manipulation Tasks	This paper presents ADAMANT, a set of software modules that provides grasp planning capabilities to an existing robot planning and control software framework. Our presented work allows a user to adapt a manipulation task to be used under widely different scenarios with minimal user input, thus reducing the operator's c...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	317,537
1007.0296	A Bayesian View of the Poisson-Dirichlet Process	The two parameter Poisson-Dirichlet Process (PDP), a generalisation of the Dirichlet Process, is increasingly being used for probabilistic modelling in discrete areas such as language technology, bioinformatics, and image analysis. There is a rich literature about the PDP and its derivative distributions such as the Ch...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	6,949
2210.16508	Clenshaw Graph Neural Networks	Graph Convolutional Networks (GCNs), which use a message-passing paradigm with stacked convolution layers, are foundational methods for learning graph representations. Recent GCN models use various residual connection techniques to alleviate the model degradation problem such as over-smoothing and gradient vanishing. E...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	327,359
2210.02407	The Influence of Explainable Artificial Intelligence: Nudging Behaviour or Boosting Capability?	This article aims to provide a theoretical account and corresponding paradigm for analysing how explainable artificial intelligence (XAI) influences people's behaviour and cognition. It uses insights from research on behaviour change. Two notable frameworks for thinking about behaviour change techniques are nudges - ai...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	321,638
2209.01288	Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning	In Multi-Agent Reinforcement Learning, communication is critical to encourage cooperation among agents. Communication in realistic wireless networks can be highly unreliable due to network conditions varying with agents' mobility, and stochasticity in the transmission process. We propose a framework to learn practical ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	315,826
2409.01726	Mahalanobis Distance-based Multi-view Optimal Transport for Multi-view Crowd Localization	Multi-view crowd localization predicts the ground locations of all people in the scene. Typical methods usually estimate the crowd density maps on the ground plane first, and then obtain the crowd locations. However, the performance of existing methods is limited by the ambiguity of the density maps in crowded areas, w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	485,450
0802.0554	Message-Passing Decoding of Lattices Using Gaussian Mixtures	A lattice decoder which represents messages explicitly as a mixture of Gaussians functions is given. In order to prevent the number of functions in a mixture from growing as the decoder iterations progress, a method for replacing N Gaussian functions with M Gaussian functions, with M < N, is given. A squared distance m...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,252
1506.04929	ASPMT(QS): Non-Monotonic Spatial Reasoning with Answer Set Programming Modulo Theories	The systematic modelling of \emph{dynamic spatial systems} [9] is a key requirement in a wide range of application areas such as comonsense cognitive robotics, computer-aided architecture design, dynamic geographic information systems. We present ASPMT(QS), a novel approach and fully-implemented prototype for non-monot...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	44,238
cs/0606106	Self-orthogonality of $q$-ary Images of $q^m$-ary Codes and Quantum Code Construction	A code over GF$(q^m)$ can be imaged or expanded into a code over GF$(q)$ using a basis for the extension field over the base field. The properties of such an image depend on the original code and the basis chosen for imaging. Problems relating the properties of a code and its image with respect to a basis have been of ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	539,542
2202.09856	Non-Deterministic Face Mask Removal Based On 3D Priors	This paper presents a novel image inpainting framework for face mask removal. Although current methods have demonstrated their impressive ability in recovering damaged face images, they suffer from two main problems: the dependence on manually labeled missing regions and the deterministic result corresponding to each i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	281,342
2408.10003	Towards a Knowledge Graph for Models and Algorithms in Applied Mathematics	Mathematical models and algorithms are an essential part of mathematical research data, as they are epistemically grounding numerical data. In order to represent models and algorithms as well as their relationship semantically to make this research data FAIR, two previously distinct ontologies were merged and extended,...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	481,680
2205.15547	Discovery of Keys for Graphs [Extended Version]	Keys for graphs uses the topology and value constraints needed to uniquely identify entities in a graph database. They have been studied to support object identification, knowledge fusion, data deduplication, and social network reconciliation. In this paper, we present our algorithm to mine keys over graphs. Our algori...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	299,770
1904.00625	Med3D: Transfer Learning for 3D Medical Image Analysis	The performance on deep learning is significantly affected by volume of training data. Models pre-trained from massive dataset such as ImageNet become a powerful weapon for speeding up training convergence and improving accuracy. Similarly, models based on large dataset are important for the development of deep learnin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	125,917
2208.04312	NRBdMF: A recommendation algorithm for predicting drug effects considering directionality	Predicting the novel effects of drugs based on information about approved drugs can be regarded as a recommendation system. Matrix factorization is one of the most used recommendation systems and various algorithms have been devised for it. A literature survey and summary of existing algorithms for predicting drug effe...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	312,066
2403.06194	On depth prediction for autonomous driving using self-supervised learning	Perception of the environment is a critical component for enabling autonomous driving. It provides the vehicle with the ability to comprehend its surroundings and make informed decisions. Depth prediction plays a pivotal role in this process, as it helps the understanding of the geometry and motion of the environment. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	436,341
2007.13305	Controlling the Outbreak of COVID-19: A Noncooperative Game Perspective	COVID-19 is a global epidemic. Till now, there is no remedy for this epidemic. However, isolation and social distancing are seemed to be effective preventive measures to control this pandemic. Therefore, in this paper, an optimization problem is formulated that accommodates both isolation and social distancing features...	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	189,089
2411.09169	Artificial Theory of Mind and Self-Guided Social Organisation	One of the challenges artificial intelligence (AI) faces is how a collection of agents coordinate their behaviour to achieve goals that are not reachable by any single agent. In a recent article by Ozmen et al this was framed as one of six grand challenges: That AI needs to respect human cognitive processes at the huma...	true	false	false	false	true	false	false	false	false	false	false	false	false	true	true	false	false	false	508,153
1907.05272	Introduction to Camera Pose Estimation with Deep Learning	Over the last two decades, deep learning has transformed the field of computer vision. Deep convolutional networks were successfully applied to learn different vision tasks such as image classification, image segmentation, object detection and many more. By transferring the knowledge learned by deep models on large gen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	138,311
2411.03569	Towards Personalized Federated Learning via Comprehensive Knowledge Distillation	Federated learning is a distributed machine learning paradigm designed to protect data privacy. However, data heterogeneity across various clients results in catastrophic forgetting, where the model rapidly forgets previous knowledge while acquiring new knowledge. To address this challenge, personalized federated learn...	false	false	false	false	true	false	true	false	false	false	false	true	true	false	false	false	false	false	505,950
1409.5719	Local Optimal Sets and Bounded Archiving on Multi-objective NK-Landscapes with Correlated Objectives	The properties of local optimal solutions in multi-objective combinatorial optimization problems are crucial for the effectiveness of local search algorithms, particularly when these algorithms are based on Pareto dominance. Such local search algorithms typically return a set of mutually nondominated Pareto local optim...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	36,187
1803.03114	Concise Fuzzy Planar Embedding of Graphs: a Dimensionality Reduction Approach	The enormous amount of data to be represented using large graphs exceeds in some cases the resources of a conventional computer. Edges in particular can take up a considerable amount of memory as compared to the number of nodes. However, rigorous edge storage might not always be essential to be able to draw the needed ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	92,193
2205.15848	Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction	Recently, neural implicit surfaces learning by volume rendering has become popular for multi-view reconstruction. However, one key challenge remains: existing approaches lack explicit multi-view geometry constraints, hence usually fail to generate geometry consistent surface reconstruction. To address this challenge, w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	299,888
2010.08321	Learning Accurate Entropy Model with Global Reference for Image Compression	In recent deep image compression neural networks, the entropy model plays a critical role in estimating the prior distribution of deep image encodings. Existing methods combine hyperprior with local context in the entropy estimation function. This greatly limits their performance due to the absence of a global vision. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	201,152
2209.02010	On the Origins of Self-Modeling	Self-Modeling is the process by which an agent, such as an animal or machine, learns to create a predictive model of its own dynamics. Once captured, this self-model can then allow the agent to plan and evaluate various potential behaviors internally using the self-model, rather than using costly physical experimentati...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	316,076
1412.7110	Learning linearly separable features for speech recognition using convolutional neural networks	Automatic speech recognition systems usually rely on spectral-based features, such as MFCC of PLP. These features are extracted based on prior knowledge such as, speech perception or/and speech production. Recently, convolutional neural networks have been shown to be able to estimate phoneme conditional probabilities i...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	38,765
2404.16773	ConKeD++ -- Improving descriptor learning for retinal image registration: A comprehensive study of contrastive losses	Self-supervised contrastive learning has emerged as one of the most successful deep learning paradigms. In this regard, it has seen extensive use in image registration and, more recently, in the particular field of medical image registration. In this work, we propose to test and extend and improve a state-of-the-art fr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	449,613
2411.06097	A Multimodal Adaptive Graph-based Intelligent Classification Model for Fake News	Numerous studies have been proposed to detect fake news focusing on multi-modalities based on machine and/or deep learning. However, studies focusing on graph-based structures using geometric deep learning are lacking. To address this challenge, we introduce the Multimodal Adaptive Graph-based Intelligent Classificatio...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	506,964
1909.03565	Computation of the Distance-based Bound on Strong Structural Controllability in Networks	In this paper, we study the problem of computing a tight lower bound on the dimension of the strong structurally controllable subspace (SSCS) in networks with Laplacian dynamics. The bound is based on a sequence of vectors containing the distances between leaders (nodes with external inputs) and followers (remaining no...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	144,528
2002.03478	Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions	Off-policy evaluation in reinforcement learning offers the chance of using observational data to improve future outcomes in domains such as healthcare and education, but safe deployment in high stakes settings requires ways of assessing its validity. Traditional measures such as confidence intervals may be insufficient...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	163,273
0910.5260	A Gradient Descent Algorithm on the Grassman Manifold for Matrix Completion	We consider the problem of reconstructing a low-rank matrix from a small subset of its entries. In this paper, we describe the implementation of an efficient algorithm called OptSpace, based on singular value decomposition followed by local manifold optimization, for solving the low-rank matrix completion problem. It h...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	4,809
2010.05752	An Adaptive Multivariable Smooth Second-Order Sliding Mode Approach	This paper presents a novel adaptive multivariable smooth second-order sliding mode approach with the features of fast finite-time convergence, adaptation to disturbances and smooth. This approach can be directly applied to the controller design of multi-input and multi-output (MIMO) systems. In addition, a novel adapt...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	200,244
1601.00172	On Quantitatively Measuring Controllability of Complex Networks	This letter deals with the controllability issue of complex networks. An index is chosen to quantitatively measure the extent of controllability of given network. The effect of this index is analyzed based on empirical studies on various classes of network topologies, such as random network, small-world network, and sc...	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	50,612
2301.13112	Benchmarking optimality of time series classification methods in distinguishing diffusions	Statistical optimality benchmarking is crucial for analyzing and designing time series classification (TSC) algorithms. This study proposes to benchmark the optimality of TSC algorithms in distinguishing diffusion processes by the likelihood ratio test (LRT). The LRT is an optimal classifier by the Neyman-Pearson lemma...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	342,786
2105.07222	On the Distributional Properties of Adaptive Gradients	Adaptive gradient methods have achieved remarkable success in training deep neural networks on a wide variety of tasks. However, not much is known about the mathematical and statistical properties of this family of methods. This work aims at providing a series of theoretical analyses of its statistical properties justi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	235,365
2201.03681	FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing	Graph Neural Networks (GNNs) have proven to excel in predictive modeling tasks where the underlying data is a graph. However, as GNNs are extensively used in human-centered applications, the issue of fairness has arisen. While edge deletion is a common method used to promote fairness in GNNs, it fails to consider when ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	274,907
0707.1064	The Effect of Noise Correlation in AF Relay Networks	In wireless relay networks, noise at the relays can be correlated possibly due to common interference or noise propagation from preceding hops. In this work we consider a parallel relay network with noise correlation. For the relay strategy of amplify-and-forward (AF), we determine the optimal rate maximizing relay gai...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	404
2112.05445	Beyond Parallel Pancakes: Quasi-Polynomial Time Guarantees for Non-Spherical Gaussian Mixtures	We consider mixtures of $k\geq 2$ Gaussian components with unknown means and unknown covariance (identical for all components) that are well-separated, i.e., distinct components have statistical overlap at most $k^{-C}$ for a large enough constant $C\ge 1$. Previous statistical-query [DKS17] and lattice-based [BRST21, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	270,846
1808.06603	Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer	We present the Network-based Biased Tree Ensembles (NetBiTE) method for drug sensitivity prediction and drug sensitivity biomarker identification in cancer using a combination of prior knowledge and gene expression data. Our devised method consists of a biased tree ensemble that is built according to a probabilistic bi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	105,570
1910.13688	Dual Illumination Estimation for Robust Exposure Correction	Exposure correction is one of the fundamental tasks in image processing and computational photography. While various methods have been proposed, they either fail to produce visually pleasing results, or only work well for limited types of image (e.g., underexposed images). In this paper, we present a novel automatic ex...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	151,456
2212.01927	Label Encoding for Regression Networks	Deep neural networks are used for a wide range of regression problems. However, there exists a significant gap in accuracy between specialized approaches and generic direct regression in which a network is trained by minimizing the squared or absolute error of output labels. Prior work has shown that solving a regressi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	334,618
2112.00351	Energy Management of a Multi-Battery System for Renewable-Based High Power EV Charging	Hybrid fast-charging stations with battery storage and local renewable generation can facilitate low-carbon electric vehicle (EV) charging, while reducing the stress on the distribution grid. This paper proposes energy management strategies for a novel multi-battery design that directly connects its strings to other DC...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	269,114
1906.06187	NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language	Rule-based models are attractive for various tasks because they inherently lead to interpretable and explainable decisions and can easily incorporate prior knowledge. However, such systems are difficult to apply to problems involving natural language, due to its linguistic variability. In contrast, neural models can co...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	135,230
2203.08975	A Survey of Multi-Agent Deep Reinforcement Learning with Communication	Communication is an effective mechanism for coordinating the behaviors of multiple agents, broadening their views of the environment, and to support their collaborations. In the field of multi-agent deep reinforcement learning (MADRL), agents can improve the overall learning performance and achieve their objectives by ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	285,977
1904.11102	Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation	Fast and efficient path generation is critical for robots operating in complex environments. This motion planning problem is often performed in a robot's actuation or configuration space, where popular pathfinding methods such as A, RRT, get exponentially more computationally expensive to execute as the dimensionalit...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	128,778
1811.03341	Modelling Opinion Dynamics in the Age of Algorithmic Personalisation	Modern technology has drastically changed the way we interact and consume information. For example, online social platforms allow for seamless communication exchanges at an unprecedented scale. However, we are still bounded by cognitive and temporal constraints. Our attention is limited and extremely valuable. Algorith...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	112,816
1310.2954	Improved Spectrum Mobility using Virtual Reservation in Collaborative Cognitive Radio Networks	Cognitive radio technology would enable a set of secondary users (SU) to opportunistically use the spectrum licensed to a primary user (PU). On the appearance of this PU on a specific frequency band, any SU occupying this band should free it for PUs. Typically, SUs may collaborate to reduce the impact of cognitive user...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	27,712
1610.00279	Deep Learning Algorithms for Signal Recognition in Long Perimeter Monitoring Distributed Fiber Optic Sensors	In this paper, we show an approach to build deep learning algorithms for recognizing signals in distributed fiber optic monitoring and security systems for long perimeters. Synthesizing such detection algorithms poses a non-trivial research and development challenge, because these systems face stringent error (type I a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	61,813
2501.09878	ASTRA: A Scene-aware TRAnsformer-based model for trajectory prediction	We present ASTRA (A} Scene-aware TRAnsformer-based model for trajectory prediction), a light-weight pedestrian trajectory forecasting model that integrates the scene context, spatial dynamics, social inter-agent interactions and temporal progressions for precise forecasting. We utilised a U-Net-based feature extractor,...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	525,303
2003.09124	Learning the Loss Functions in a Discriminative Space for Video Restoration	With more advanced deep network architectures and learning schemes such as GANs, the performance of video restoration algorithms has greatly improved recently. Meanwhile, the loss functions for optimizing deep neural networks remain relatively unchanged. To this end, we propose a new framework for building effective lo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	168,964
2103.12376	Generalizing Face Forgery Detection with High-frequency Features	Current face forgery detection methods achieve high accuracy under the within-database scenario where training and testing forgeries are synthesized by the same algorithm. However, few of them gain satisfying performance under the cross-database scenario where training and testing forgeries are synthesized by different...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	226,156
2209.10032	Robust Dynamic State Estimation of Multi-Machine Power Networks with Solar Farms and Dynamics Loads	Conventional state estimation routines of electrical grids are mainly reliant on dynamic models of fossil fuel-based resources. These models commonly contain differential equations describing synchronous generator models and algebraic equations modeling power flow/balance equations. Fuel-free power systems that are dri...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	318,714
2312.12577	An integrated EOS, pore-crush, strength and damage model framework for near-field ground-shock	An integrated Equation of State (EOS) and strength/pore-crush/damage model framework is provided for modeling near to source (near-field) ground-shock response, where large deformations and pressures necessitate coupling EOS with pressure-dependent plastic yield and damage. Nonlinear pressure-dependence of strength up ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	417,009
2408.00863	UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation	The remarkable success of Large Language Models (LLMs) across diverse tasks has driven the research community to extend their capabilities to molecular applications. However, most molecular LLMs employ adapter-based architectures that do not treat molecule and text modalities equally and lack a supervision signal for t...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	478,007
1905.00462	Full-stack Optimization for Accelerating CNNs with FPGA Validation	We present a full-stack optimization framework for accelerating inference of CNNs (Convolutional Neural Networks) and validate the approach with field-programmable gate arrays (FPGA) implementations. By jointly optimizing CNN models, computing architectures, and hardware implementations, our full-stack approach achieve...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	129,473
2406.08627	Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis	Time series data are ubiquitous across a wide range of real-world domains. While real-world time series analysis (TSA) requires human experts to integrate numerical series data with multimodal domain-specific knowledge, most existing TSA models rely solely on numerical data, overlooking the significance of information ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	463,556
2310.07324	Guided Attention for Interpretable Motion Captioning	Diverse and extensive work has recently been conducted on text-conditioned human motion generation. However, progress in the reverse direction, motion captioning, has seen less comparable advancement. In this paper, we introduce a novel architecture design that enhances text generation quality by emphasizing interpreta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	398,929
2403.11367	3DGS-ReLoc: 3D Gaussian Splatting for Map Representation and Visual ReLocalization	This paper presents a novel system designed for 3D mapping and visual relocalization using 3D Gaussian Splatting. Our proposed method uses LiDAR and camera data to create accurate and visually plausible representations of the environment. By leveraging LiDAR data to initiate the training of the 3D Gaussian Splatting ma...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	true	438,661
1901.01588	PyOD: A Python Toolbox for Scalable Outlier Detection	PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data. Uniquely, it provides access to a wide range of outlier detection algorithms, including established outlier ensembles and more recent neural network-based approaches, under a single, well-documented API designed for us...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	118,012
1902.09368	Dual Attention Networks for Visual Reference Resolution in Visual Dialog	Visual dialog (VisDial) is a task which requires an AI agent to answer a series of questions grounded in an image. Unlike in visual question answering (VQA), the series of questions should be able to capture a temporal context from a dialog history and exploit visually-grounded information. A problem called visual refe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	122,402
2002.10524	Efficient exploration of zero-sum stochastic games	We investigate the increasingly important and common game-solving setting where we do not have an explicit description of the game but only oracle access to it through gameplay, such as in financial or military simulations and computer games. During a limited-duration learning phase, the algorithm can control the actio...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	true	false	false	true	165,421
1710.06908	Generalized Bounds on the Capacity of the Binary-Input Channels	For the class of the memoryless binary-input channels which are not necessarily symmetric, we derive tight bounds on the capacity in terms of the Bhattacharyya parameter. As it turns out, the bounds derived under the symmetric channel assumption in [1] are valid for the general case as well.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	82,844
2209.11448	Rethinking Performance Gains in Image Dehazing Networks	Image dehazing is an active topic in low-level vision, and many image dehazing networks have been proposed with the rapid development of deep learning. Although these networks' pipelines work fine, the key mechanism to improving image dehazing performance remains unclear. For this reason, we do not target to propose a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	319,187
2209.06521	Preregistered protocol for: Articulatory changes in speech following treatment for oral or oropharyngeal cancer: a systematic review	This document outlines a PROSPERO pre-registered protocol for a systematic review regarding articulatory changes in speech following oral or orophayrngeal cancer treatment. Treatment of tumours in the oral cavity may result in physiological changes that could lead to articulatory difficulties. The tongue becomes less m...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	317,428
2308.03203	Microvasculature Segmentation in Human BioMolecular Atlas Program (HuBMAP)	Image segmentation serves as a critical tool across a range of applications, encompassing autonomous driving's pedestrian detection and pre-operative tumor delineation in the medical sector. Among these applications, we focus on the National Institutes of Health's (NIH) Human BioMolecular Atlas Program (HuBMAP), a sign...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	383,934
2410.09368	Towards a Domain-Specific Modelling Environment for Reinforcement Learning	In recent years, machine learning technologies have gained immense popularity and are being used in a wide range of domains. However, due to the complexity associated with machine learning algorithms, it is a challenge to make it user-friendly, easy to understand and apply. Machine learning applications are especially ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	497,562
1904.04374	Collision-aware Task Assignment for Multi-Robot Systems	We propose a novel formulation of the collision-aware task assignment (CATA) problem and a decentralized auction-based algorithm to solve the problem with optimality bound. Using a collision cone, we predict potential collisions and introduce a binary decision variable into the local reward function for task bidding. W...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	127,016
2401.04874	Feature Network Methods in Machine Learning and Applications	A machine learning (ML) feature network is a graph that connects ML features in learning tasks based on their similarity. This network representation allows us to view feature vectors as functions on the network. By leveraging function operations from Fourier analysis and from functional analysis, one can easily genera...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	420,571
2205.03871	Adversarial Learning of Hard Positives for Place Recognition	Image retrieval methods for place recognition learn global image descriptors that are used for fetching geo-tagged images at inference time. Recent works have suggested employing weak and self-supervision for mining hard positives and hard negatives in order to improve localization accuracy and robustness to visibility...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	295,451
2302.10434	Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes	In recent years, large amounts of electronic health records (EHRs) concerning chronic diseases have been collected to facilitate medical diagnosis. Modeling the dynamic properties of EHRs related to chronic diseases can be efficiently done using dynamic treatment regimes (DTRs). While reinforcement learning (RL) is a w...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	346,809
2305.08104	Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling	Federated learning (FL) has recently gained much attention due to its effectiveness in speeding up supervised learning tasks under communication and privacy constraints. However, whether similar speedups can be established for reinforcement learning remains much less understood theoretically. Towards this direction, we...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	true	false	false	false	364,167

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