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
2209.08010	Causes of Catastrophic Forgetting in Class-Incremental Semantic Segmentation	Class-incremental learning for semantic segmentation (CiSS) is presently a highly researched field which aims at updating a semantic segmentation model by sequentially learning new semantic classes. A major challenge in CiSS is overcoming the effects of catastrophic forgetting, which describes the sudden drop of accura...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	317,972
2405.14007	A Practice in Enrollment Prediction with Markov Chain Models	Enrollment projection is a critical aspect of university management, guiding decisions related to resource allocation and revenue forecasting. However, despite its importance, there remains a lack of transparency regarding the methodologies utilized by many institutions. This paper presents an innovative approach to en...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	456,195
2407.10614	Investigating shocking events in the Ethereum stablecoin ecosystem through temporal multilayer graph structure	In the dynamic landscape of the Web, we are witnessing the emergence of the Web3 paradigm, which dictates that platforms should rely on blockchain technology and cryptocurrencies to sustain themselves and their profitability. Cryptocurrencies are characterised by high market volatility and susceptibility to substantial...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	473,050
1802.08331	Diverse Exploration for Fast and Safe Policy Improvement	We study an important yet under-addressed problem of quickly and safely improving policies in online reinforcement learning domains. As its solution, we propose a novel exploration strategy - diverse exploration (DE), which learns and deploys a diverse set of safe policies to explore the environment. We provide DE theo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	91,080
2007.04448	Emergence of Hierarchy in Networked Endorsement Dynamics	Many social and biological systems are characterized by enduring hierarchies, including those organized around prestige in academia, dominance in animal groups, and desirability in online dating. Despite their ubiquity, the general mechanisms that explain the creation and endurance of such hierarchies are not well unde...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	186,351
1908.01479	Imaging with highly incomplete and corrupted data	We consider the problem of imaging sparse scenes from a few noisy data using an $l_1$-minimization approach. This problem can be cast as a linear system of the form $A \, \rho =b$, where $A$ is an $N\times K$ measurement matrix. We assume that the dimension of the unknown sparse vector $\rho \in {\mathbb{C}}^K$ is much...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	140,777
2112.10325	Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis	Due to the constraints of the imaging device and high cost in operation time, computer tomography (CT) scans are usually acquired with low intra-slice resolution. Improving the intra-slice resolution is beneficial to the disease diagnosis for both human experts and computer-aided systems. To this end, this paper builds...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	272,409
1808.08402	How do Convolutional Neural Networks Learn Design?	In this paper, we aim to understand the design principles in book cover images which are carefully crafted by experts. Book covers are designed in a unique way, specific to genres which convey important information to their readers. By using Convolutional Neural Networks (CNN) to predict book genres from cover images, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	105,940
2208.03692	Multi-Stage NMPC for a MAV based Collision Free Navigation under Varying Communication Delays	Time delays in communication networks are one of the main concerns in deploying robots with computation boards on the edge. This article proposes a multi-stage Nonlinear Model Predictive Control (NMPC) that is capable of handling varying network-induced time delays for establishing a control framework being able to gua...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	311,869
2412.10450	Regional Weather Variable Predictions by Machine Learning with Near-Surface Observational and Atmospheric Numerical Data	Accurate and timely regional weather prediction is vital for sectors dependent on weather-related decisions. Traditional prediction methods, based on atmospheric equations, often struggle with coarse temporal resolutions and inaccuracies. This paper presents a novel machine learning (ML) model, called MiMa (short for M...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	516,939
1105.5789	Clustering and Classification in Text Collections Using Graph Modularity	A new fast algorithm for clustering and classification of large collections of text documents is introduced. The new algorithm employs the bipartite graph that realizes the word-document matrix of the collection. Namely, the modularity of the bipartite graph is used as the optimization functional. Experiments performed...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	10,564
1906.04309	Associative Convolutional Layers	Motivated by the necessity for parameter efficiency in distributed machine learning and AI-enabled edge devices, we provide a general and easy to implement method for significantly reducing the number of parameters of Convolutional Neural Networks (CNNs), during both the training and inference phases. We introduce a si...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	134,670
2005.06602	CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations	This paper describes the winning contribution to SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection (Subtask 2) handed in by team UG Student Intern. We present an ensemble model that makes predictions based on context-free and context-dependent word representations. The key findings are that (1) contex...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	177,043
2301.08078	Stable Contact Guaranteeing Motion/Force Control for an Aerial Manipulator on an Arbitrarily Tilted Surface	This study aims to design a motion/force controller for an aerial manipulator which guarantees the tracking of time-varying motion/force trajectories as well as the stability during the transition between free and contact motions. To this end, we model the force exerted on the end-effector as the Kelvin-Voigt linear mo...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	341,090
2406.11577	Mathematical Entities: Corpora and Benchmarks	Mathematics is a highly specialized domain with its own unique set of challenges. Despite this, there has been relatively little research on natural language processing for mathematical texts, and there are few mathematical language resources aimed at NLP. In this paper, we aim to provide annotated corpora that can be ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	464,952
1302.3597	A Framework for Decision-Theoretic Planning I: Combining the Situation Calculus, Conditional Plans, Probability and Utility	This paper shows how we can combine logical representations of actions and decision theory in such a manner that seems natural for both. In particular we assume an axiomatization of the domain in terms of situation calculus, using what is essentially Reiter's solution to the frame problem, in terms of the completion of...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	22,063
2302.00606	The RW3D: A multi-modal panel dataset to understand the psychological impact of the pandemic	Besides far-reaching public health consequences, the COVID-19 pandemic had a significant psychological impact on people around the world. To gain further insight into this matter, we introduce the Real World Worry Waves Dataset (RW3D). The dataset combines rich open-ended free-text responses with survey data on emotion...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	343,275
2106.06908	Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation	Medical imaging datasets usually exhibit domain shift due to the variations of scanner vendors, imaging protocols, etc. This raises the concern about the generalization capacity of machine learning models. Domain generalization (DG), which aims to learn a model from multiple source domains such that it can be directly ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	240,678
2006.06404	A model for the spread of an epidemic from local to global: A case study of COVID-19 in India	In this paper we propose an epidemiological model for the spread of COVID-19. The dynamics of the spread is based on four fundamental categories of people in a population: Tested and infected, Non-Tested but infected, Tested but not infected, and non-Tested and not infected. The model is based on two levels of dynamics...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	181,417
1612.03239	When multiplicative noise stymies control	We consider the stabilization of an unstable discrete-time linear system that is observed over a channel corrupted by continuous multiplicative noise. Our main result shows that if the system growth is large enough, then the system cannot be stabilized in a second-moment sense. This is done by showing that the probabil...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	65,350
2204.14133	Network Topology Optimization via Deep Reinforcement Learning	Topology impacts important network performance metrics, including link utilization, throughput and latency, and is of central importance to network operators. However, due to the combinatorial nature of network topology, it is extremely difficult to obtain an optimal solution, especially since topology planning in netw...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	294,065
2006.04700	Multimodal Future Localization and Emergence Prediction for Objects in Egocentric View with a Reachability Prior	In this paper, we investigate the problem of anticipating future dynamics, particularly the future location of other vehicles and pedestrians, in the view of a moving vehicle. We approach two fundamental challenges: (1) the partial visibility due to the egocentric view with a single RGB camera and considerable field-of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	180,786
2212.09849	Dataless Knowledge Fusion by Merging Weights of Language Models	Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models. Oftentimes fine-tuned models are readily available but their training data is not, due to data privacy or intellectual property concerns. This creates a barrier to fusing knowledge across individual models to y...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	337,232
2310.01415	GPT-Driver: Learning to Drive with GPT	We present a simple yet effective approach that can transform the OpenAI GPT-3.5 model into a reliable motion planner for autonomous vehicles. Motion planning is a core challenge in autonomous driving, aiming to plan a driving trajectory that is safe and comfortable. Existing motion planners predominantly leverage heur...	false	false	false	false	true	false	false	true	true	false	false	true	false	false	false	false	false	false	396,412
2105.06251	Learning Weakly Convex Sets in Metric Spaces	One of the central problems studied in the theory of machine learning is the question of whether, for a given class of hypotheses, it is possible to efficiently find a {consistent} hypothesis, i.e., which has zero training error. While problems involving {\em convex} hypotheses have been extensively studied, the questi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	235,067
2404.03208	HiMAL: A Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting and explaining Alzheimer disease progression	Objective: We aimed to develop and validate a novel multimodal framework HiMAL (Hierarchical, Multi-task Auxiliary Learning) framework, for predicting cognitive composite functions as auxiliary tasks that estimate the longitudinal risk of transition from Mild Cognitive Impairment (MCI) to Alzheimer Disease (AD). Meth...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	444,165
2008.04357	Directional Laplacian Centrality for Cyber Situational Awareness	Cyber operations is drowning in diverse, high-volume, multi-source data. In order to get a full picture of current operations and identify malicious events and actors analysts must see through data generated by a mix of human activity and benign automated processes. Although many monitoring and alert systems exist, the...	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	true	191,196
2302.14705	AccelTran: A Sparsity-Aware Accelerator for Dynamic Inference with Transformers	Self-attention-based transformer models have achieved tremendous success in the domain of natural language processing. Despite their efficacy, accelerating the transformer is challenging due to its quadratic computational complexity and large activation sizes. Existing transformer accelerators attempt to prune its toke...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	348,405
2111.03418	Meta-Forecasting by combining Global Deep Representations with Local Adaptation	While classical time series forecasting considers individual time series in isolation, recent advances based on deep learning showed that jointly learning from a large pool of related time series can boost the forecasting accuracy. However, the accuracy of these methods suffers greatly when modeling out-of-sample time ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	265,161
2411.05809	Is it me, or is A larger than B: Uncovering the determinants of relational cognitive dissonance resolution	This study explores the computational mechanisms underlying the resolution of cognitive dissonances. We focus on scenarios in which an observation violates the expected relationship between objects. For instance, an agent expects object A to be smaller than B in some feature space but observes the opposite. One solutio...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	506,805
2302.05826	Asymptotically Optimal Coded Distributed Computing via Combinatorial Designs	Coded distributed computing (CDC) introduced by Li \emph{et al.} can greatly reduce the communication load for MapReduce computing systems. In the general cascaded CDC with $K$ workers, $N$ input files and $Q$ Reduce functions, each input file will be mapped by $r$ workers and each Reduce function will be computed by $...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	345,182
2106.02811	Full-Dimensional Rate Enhancement for UAV-Enabled Communications via Intelligent Omni-Surface	This paper investigates the achievable rate maximization problem of a downlink unmanned aerial vehicle (UAV)-enabled communication system aided by an intelligent omni-surface (IOS). Different from the state-of-the-art reconfigurable intelligent surface (RIS) that only reflects incident signals, the IOS can simultaneous...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	239,040
1907.02211	Optimal Decision Trees for the Algorithm Selection Problem: Integer Programming Based Approaches	Even though it is well known that for most relevant computational problems different algorithms may perform better on different classes of problem instances, most researchers still focus on determining a single best algorithmic configuration based on aggregate results such as the average. In this paper, we propose Inte...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	137,559
2301.06428	Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization	We consider the optimization problem of the form $\min_{x \in \mathbb{R}^d} f(x) \triangleq \mathbb{E}_{\xi} [F(x; \xi)]$, where the component $F(x;\xi)$ is $L$-mean-squared Lipschitz but possibly nonconvex and nonsmooth. The recently proposed gradient-free method requires at most $\mathcal{O}( L^4 d^{3/2} \epsilon^{-4...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	340,641
2005.03857	Efficient Computation Reduction in Bayesian Neural Networks Through Feature Decomposition and Memorization	Bayesian method is capable of capturing real world uncertainties/incompleteness and properly addressing the over-fitting issue faced by deep neural networks. In recent years, Bayesian Neural Networks (BNNs) have drawn tremendous attentions of AI researchers and proved to be successful in many applications. However, the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	176,282
2009.04007	Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function	In this paper, we study bidirectional LSTM network for the task of text classification using both supervised and semi-supervised approaches. Several prior works have suggested that either complex pretraining schemes using unsupervised methods such as language modeling (Dai and Le 2015; Miyato, Dai, and Goodfellow 2016)...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	194,941
2403.07003	Evacuation Management Framework towards Smart City-wide Intelligent Emergency Interactive Response System	A smart city solution toward future 6G network deployment allows small and medium sized enterprises (SMEs), industry, and government entities to connect with the infrastructures and play a crucial role in enhancing emergency preparedness with advanced sensors. The objective of this work is to propose a set of coordinat...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	true	436,700
1902.06740	Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning	A common technique to improve learning performance in deep reinforcement learning (DRL) and many other machine learning algorithms is to run multiple learning agents in parallel. A neglected component in the development of these algorithms has been how best to arrange the learning agents involved to improve distributed...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	121,831
0809.4834	Relevance Feedback in Conceptual Image Retrieval: A User Evaluation	The Visual Object Information Retrieval (VOIR) system described in this paper implements an image retrieval approach that combines two layers, the conceptual and the visual layer. It uses terms from a textual thesaurus to represent the conceptual information and also works with image regions, the visual information. Th...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	2,419
2202.12316	AutoIP: A United Framework to Integrate Physics into Gaussian Processes	Physical modeling is critical for many modern science and engineering applications. From a data science or machine learning perspective, where more domain-agnostic, data-driven models are pervasive, physical knowledge -- often expressed as differential equations -- is valuable in that it is complementary to data, and i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	282,187
2412.14771	ALKAFI-LLAMA3: Fine-Tuning LLMs for Precise Legal Understanding in Palestine	Large Language Models (LLMs) have demonstrated remarkable potential in diverse domains, yet their application in the legal sector, particularly in low-resource contexts, remains limited. This study addresses the challenges of adapting LLMs to the Palestinian legal domain, where political instability, fragmented legal f...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	518,855
1704.02853	SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications	We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the tas...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	71,524
2311.14838	OpusCleaner and OpusTrainer, open source toolkits for training Machine Translation and Large language models	Developing high quality machine translation systems is a labour intensive, challenging and confusing process for newcomers to the field. We present a pair of tools OpusCleaner and OpusTrainer that aim to simplify the process, reduce the amount of work and lower the entry barrier for newcomers. OpusCleaner is a data d...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	410,275
1301.6725	Loopy Belief Propagation for Approximate Inference: An Empirical Study	Recently, researchers have demonstrated that loopy belief propagation - the use of Pearls polytree algorithm IN a Bayesian network WITH loops OF error- correcting codes.The most dramatic instance OF this IS the near Shannon - limit performance OF Turbo Codes codes whose decoding algorithm IS equivalent TO loopy belief ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	21,518
2302.02676	Chain of Hindsight Aligns Language Models with Feedback	Learning from human preferences is important for language models to match human needs and to align with human and social values. Prior works have achieved remarkable successes by learning from human feedback to understand and follow instructions. Nonetheless, these methods are either founded on hand-picked model genera...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	344,087
2107.03774	Optimizing Data Processing in Space for Object Detection in Satellite Imagery	There is a proliferation in the number of satellites launched each year, resulting in downlinking of terabytes of data each day. The data received by ground stations is often unprocessed, making this an expensive process considering the large data sizes and that not all of the data is useful. This, coupled with the inc...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	245,256
2108.00768	Cross-cultural Mood Perception in Pop Songs and its Alignment with Mood Detection Algorithms	Do people from different cultural backgrounds perceive the mood in music the same way? How closely do human ratings across different cultures approximate automatic mood detection algorithms that are often trained on corpora of predominantly Western popular music? Analyzing 166 participants responses from Brazil, South ...	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	248,827
1608.07202	Capacity-achieving and Flicker-free FEC coding scheme for Dimmable Visible Light Communication Based on Polar Codes	Visible light communication (VLC) could provide short-range optical wireless communication together with illumination using LED lighting. However, conventional forward error correction (FEC) codes for reliable communication do not have the features for dimming support and flicker mitigation which are required in VLC fo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	60,204
2309.07064	A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Response	In the dynamic landscape of digital forensics, the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a transformative technology, poised to amplify the efficiency and precision of digital forensics investigations. However, the use of ML and AI in digital forensics is still in its nascent s...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	391,637
2311.14530	Machine Translation for Ge'ez Language	Machine translation (MT) for low-resource languages such as Ge'ez, an ancient language that is no longer the native language of any community, faces challenges such as out-of-vocabulary words, domain mismatches, and lack of sufficient labeled training data. In this work, we explore various methods to improve Ge'ez MT, ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	410,140
2309.16179	BEVHeight++: Toward Robust Visual Centric 3D Object Detection	While most recent autonomous driving system focuses on developing perception methods on ego-vehicle sensors, people tend to overlook an alternative approach to leverage intelligent roadside cameras to extend the perception ability beyond the visual range. We discover that the state-of-the-art vision-centric bird's eye ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	395,252
2408.07297	Estimate collective cooperativeness of driving agents in mixed traffic flow	Cooperation is a ubiquitous phenomenon in many natural, social, and engineered systems that contain multiple agents. Characterizing and quantifying cooperativeness of driving agents is of interest and significance for two reasons. Theoretically, it will enhance the understanding of micro-macro connections and emergence...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	480,533
2404.13868	TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos	Multi-object tracking (MOT) is a critical and challenging task in computer vision, particularly in situations involving objects with similar appearances but diverse movements, as seen in team sports. Current methods, largely reliant on object detection and appearance, often fail to track targets in such complex scenari...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	448,477
cs/0212018	Real numbers having ultimately periodic representations in abstract numeration systems	Using a genealogically ordered infinite regular language, we know how to represent an interval of R. Numbers having an ultimately periodic representation play a special role in classical numeration systems. The aim of this paper is to characterize the numbers having an ultimately periodic representation in generalized ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	537,748
2203.07978	Control Barrier Functions for Systems with Multiple Control Inputs	Control Barrier Functions (CBFs) are becoming popular tools in guaranteeing safety for nonlinear systems and constraints, and they can reduce a constrained optimal control problem into a sequence of Quadratic Programs (QPs) for affine control systems. The recently proposed High Order Control Barrier Functions (HOCBFs) ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	285,626
2403.00103	On Robustness and Generalization of ML-Based Congestion Predictors to Valid and Imperceptible Perturbations	There is substantial interest in the use of machine learning (ML)-based techniques throughout the electronic computer-aided design (CAD) flow, particularly methods based on deep learning. However, while deep learning methods have achieved state-of-the-art performance in several applications, recent work has demonstrate...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	433,855
2305.14987	Investigating Table-to-Text Generation Capabilities of LLMs in Real-World Information Seeking Scenarios	Tabular data is prevalent across various industries, necessitating significant time and effort for users to understand and manipulate for their information-seeking purposes. The advancements in large language models (LLMs) have shown enormous potential to improve user efficiency. However, the adoption of LLMs in real-w...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	367,375
2307.06797	Fast and Functional Structured Data Generators Rooted in Out-of-Equilibrium Physics	In this study, we address the challenge of using energy-based models to produce high-quality, label-specific data in complex structured datasets, such as population genetics, RNA or protein sequences data. Traditional training methods encounter difficulties due to inefficient Markov chain Monte Carlo mixing, which affe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	379,179
2410.05804	CASA: Class-Agnostic Shared Attributes in Vision-Language Models for Efficient Incremental Object Detection	Incremental object detection (IOD) is challenged by background shift, where background categories in sequential data may include previously learned or future classes. Inspired by the vision-language foundation models such as CLIP, these models capture shared attributes from extensive image-text paired data during pre-t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	495,926
1502.00068	TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries	The proliferation of massive datasets combined with the development of sophisticated analytical techniques have enabled a wide variety of novel applications such as improved product recommendations, automatic image tagging, and improved speech-driven interfaces. These and many other applications can be supported by Pre...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	39,759
1903.04982	A Capsule-unified Framework of Deep Neural Networks for Graphical Programming	Recently, the growth of deep learning has produced a large number of deep neural networks. How to describe these networks unifiedly is becoming an important issue. We first formalize neural networks in a mathematical definition, give their directed graph representations, and prove a generation theorem about the induced...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	124,078
1502.04933	Block-Level Unitary Query: Incorporating Orthogonal-like Space-time Code with Query Diversity for MIMO Backscatter RFID	Because of the emerging field of Internet of Things (IoT), future backscatter RFID is required to be more reliable and data intensive. Motivated by this, orthogonal space-time block code (OSTBC), which is very successful in mobile communications for its low complexity and high performance, has already been investigated...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	40,318
1503.04885	Optimal control of the state statistics for a linear stochastic system	We consider a variant of the classical linear quadratic Gaussian regulator (LQG) in which penalties on the endpoint state are replaced by the specification of the terminal state distribution. The resulting theory considerably differs from LQG as well as from formulations that bound the probability of violating state co...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	41,190
1609.09588	One-Lee weight and two-Lee weight $\mathbb{Z}_2\mathbb{Z}_2[u]$-additive codes	In this paper, we study one-Lee weight and two-Lee weight codes over $\mathbb{Z}_{2}\mathbb{Z}_{2}[u]$, where $u^{2}=0$. Some properties of one-Lee weight $\mathbb{Z}_{2}\mathbb{Z}_{2}[u]$-additive codes are given, and a complete classification of one-Lee weight $\mathbb{Z}_2\mathbb{Z}_2[u]$-additive formally self-dual...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	61,748
2105.00459	Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks	Power control in decentralized wireless networks poses a complex stochastic optimization problem when formulated as the maximization of the average sum rate for arbitrary interference graphs. Recent work has introduced data-driven design methods that leverage graph neural network (GNN) to efficiently parametrize the po...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	233,229
2210.03441	Decentralized Vision-Based Byzantine Agent Detection in Multi-Robot Systems with IOTA Smart Contracts	Multiple opportunities lie at the intersection of multi-robot systems and distributed ledger technologies (DLTs). In this work, we investigate the potential of new DLT solutions such as IOTA, for detecting anomalies and byzantine agents in multi-robot systems in a decentralized manner. Traditional blockchain approaches...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	322,038
2412.02376	Flexible-Antenna Systems: A Pinching-Antenna Perspective	Flexible-antenna systems have recently received significant research interest due to their capability to reconfigure wireless channels intelligently. This paper focuses on a new type of flexible-antenna technology, termed pinching antennas, which can be realized by applying small dielectric particles on a waveguide. An...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	513,520
2402.07152	Explainable Global Wildfire Prediction Models using Graph Neural Networks	Wildfire prediction has become increasingly crucial due to the escalating impacts of climate change. Traditional CNN-based wildfire prediction models struggle with handling missing oceanic data and addressing the long-range dependencies across distant regions in meteorological data. In this paper, we introduce an innov...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	428,583
2411.13056	Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale Benchmark	The dynamic imbalance of the fore-background is a major challenge in video object counting, which is usually caused by the sparsity of foreground objects. This often leads to severe under- and over-prediction problems and has been less studied in existing works. To tackle this issue in video object counting, we propose...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	509,662
2106.08531	Latent Representation in Human-Robot Interaction with Explicit Consideration of Periodic Dynamics	This paper presents a new data-driven framework for analyzing periodic physical human-robot interaction (pHRI) in latent state space. To elaborate human understanding and/or robot control during pHRI, the model representing pHRI is critical. Recent developments of deep learning technologies would enable us to learn suc...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	241,324
1901.06442	The Conditional Information Leakage Given Eavesdropper's Received Signals in Wiretap Channels	Information leakage in Wyner's wiretap channel model is usually defined as the mutual information between the secret message and the eavesdropper's received signal. We define a new quantity called "conditional information leakage given the eavesdropper's received signals," which expresses the amount of information that...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	118,998
1901.03254	Quantum-inspired sublinear algorithm for solving low-rank semidefinite programming	Semidefinite programming (SDP) is a central topic in mathematical optimization with extensive studies on its efficient solvers. In this paper, we present a proof-of-principle sublinear-time algorithm for solving SDPs with low-rank constraints; specifically, given an SDP with $m$ constraint matrices, each of dimension $...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	118,368
2408.04262	CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learning	Self-supervised learning (SSL) has emerged as a promising paradigm for medical image analysis by harnessing unannotated data. Despite their potential, the existing SSL approaches overlook the high anatomical similarity inherent in medical images. This makes it challenging for SSL methods to capture diverse semantic con...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	479,319
2206.07839	Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness	Certifiable robustness is a highly desirable property for adopting deep neural networks (DNNs) in safety-critical scenarios, but often demands tedious computations to establish. The main hurdle lies in the massive amount of non-linearity in large DNNs. To trade off the DNN expressiveness (which calls for more non-linea...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	302,905
1510.03035	Reliability Analysis of Processes with Moving Cracked Material	The reliability of processes with moving elastic and isotropic material containing initial cracks is considered in terms of fracture. The material is modelled as a moving plate which is simply supported from two of its sides and subjected to homogeneous tension acting in the travelling direction. For tension, two model...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	47,800
1504.03509	Regret vs. Communication: Distributed Stochastic Multi-Armed Bandits and Beyond	In this paper, we consider the distributed stochastic multi-armed bandit problem, where a global arm set can be accessed by multiple players independently. The players are allowed to exchange their history of observations with each other at specific points in time. We study the relationship between regret and communica...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	42,039
1402.6243	Globally Optimal Cooperation in Dense Cognitive Radio Networks	The problem of calculating the local and global decision thresholds in hard decisions based cooperative spectrum sensing is well known for its mathematical intractability. Previous work relied on simple suboptimal counting rules for decision fusion in order to avoid the exhaustive numerical search required for obtainin...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	31,159
2005.14716	Prosody leaks into the memories of words	The average predictability (aka informativity) of a word in context has been shown to condition word duration (Seyfarth, 2014). All else being equal, words that tend to occur in more predictable environments are shorter than words that tend to occur in less predictable environments. One account of the informativity eff...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	179,340
1801.08228	Visual-Inertial Odometry-enhanced Geometrically Stable ICP for Mapping Applications using Aerial Robots	This paper presents a visual-inertial odometry-enhanced geometrically stable Iterative Closest Point (ICP) algorithm for accurate mapping using aerial robots. The proposed method employs a visual-inertial odometry framework in order to provide robust priors to the ICP step and calculate the overlap among point clouds d...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	88,919
1804.04267	Energy Efficient Distributed Worst Case Robust Power Allocation in Massive MIMO	This letter proposes an energy efficient distributed worst case robust power allocation in massive multiple input multiple output (MIMO) system. We assume a bounded channel state information (CSI) error and all channels lie in some bounded uncertainty region. The problem is formulated as max-min one with infinite const...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	94,799
1705.09339	Rejection-Cascade of Gaussians: Real-time adaptive background subtraction framework	Background-Foreground classification is a well-studied problem in computer vision. Due to the pixel-wise nature of modeling and processing in the algorithm, it is usually difficult to satisfy real-time constraints. There is a trade-off between the speed (because of model complexity) and accuracy. Inspired by the reject...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	74,176
2008.00727	Deep Bayesian Bandits: Exploring in Online Personalized Recommendations	Recommender systems trained in a continuous learning fashion are plagued by the feedback loop problem, also known as algorithmic bias. This causes a newly trained model to act greedily and favor items that have already been engaged by users. This behavior is particularly harmful in personalised ads recommendations, as ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	190,093
1904.03061	A Literature Study of Embeddings on Source Code	Natural language processing has improved tremendously after the success of word embedding techniques such as word2vec. Recently, the same idea has been applied on source code with encouraging results. In this survey, we aim to collect and discuss the usage of word embedding techniques on programs and source code. The a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	126,599
2311.05707	FMViT: A multiple-frequency mixing Vision Transformer	The transformer model has gained widespread adoption in computer vision tasks in recent times. However, due to the quadratic time and memory complexity of self-attention, which is proportional to the number of input tokens, most existing Vision Transformers (ViTs) encounter challenges in achieving efficient performance...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	406,669
2401.04890	Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies	This work introduces a novel principle for disentanglement we call mechanism sparsity regularization, which applies when the latent factors of interest depend sparsely on observed auxiliary variables and/or past latent factors. We propose a representation learning method that induces disentanglement by simultaneously l...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	420,576
2007.07773	Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-directional LSTM	Fall event detection, as one of the greatest risks to the elderly, has been a hot research issue in the solitary scene in recent years. Nevertheless, there are few researches on the fall event detection in complex background. Different from most conventional background subtraction methods which depend on background mod...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	187,435
2103.16370	Distribution Alignment: A Unified Framework for Long-tail Visual Recognition	Despite the recent success of deep neural networks, it remains challenging to effectively model the long-tail class distribution in visual recognition tasks. To address this problem, we first investigate the performance bottleneck of the two-stage learning framework via ablative study. Motivated by our discovery, we pr...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	227,561
2204.00619	Maze Learning using a Hyperdimensional Predictive Processing Cognitive Architecture	We present the COGnitive Neural GENerative system (CogNGen), a cognitive architecture that combines two neurobiologically-plausible, computational models: predictive processing and hyperdimensional/vector-symbolic models. We draw inspiration from architectures such as ACT-R and Spaun/Nengo. CogNGen is in broad agreemen...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	289,324
2101.02916	Accelerating Training of Batch Normalization: A Manifold Perspective	Batch normalization (BN) has become a critical component across diverse deep neural networks. The network with BN is invariant to positively linear re-scale transformation, which makes there exist infinite functionally equivalent networks with different scales of weights. However, optimizing these equivalent networks w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	214,772
1003.2724	Particle Swarm Optimization Based Diophantine Equation Solver	The paper introduces particle swarm optimization as a viable strategy to find numerical solution of Diophantine equation, for which there exists no general method of finding solutions. The proposed methodology uses a population of integer particles. The candidate solutions in the feasible space are optimized to have be...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	5,914
2003.03473	PoseNet3D: Learning Temporally Consistent 3D Human Pose via Knowledge Distillation	Recovering 3D human pose from 2D joints is a highly unconstrained problem. We propose a novel neural network framework, PoseNet3D, that takes 2D joints as input and outputs 3D skeletons and SMPL body model parameters. By casting our learning approach in a student-teacher framework, we avoid using any 3D data such as pa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	167,229
2001.03340	Temporally Folded Convolutional Neural Networks for Sequence Forecasting	In this work we propose a novel approach to utilize convolutional neural networks for time series forecasting. The time direction of the sequential data with spatial dimensions $D=1,2$ is considered democratically as the input of a spatiotemporal $(D+1)$-dimensional convolutional neural network. Latter then reduces the...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	159,950
2406.06435	Language Models are Alignable Decision-Makers: Dataset and Application to the Medical Triage Domain	In difficult decision-making scenarios, it is common to have conflicting opinions among expert human decision-makers as there may not be a single right answer. Such decisions may be guided by different attributes that can be used to characterize an individual's decision. We introduce a novel dataset for medical triage ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	462,574
2204.04040	Ontology Matching Through Absolute Orientation of Embedding Spaces	Ontology matching is a core task when creating interoperable and linked open datasets. In this paper, we explore a novel structure-based mapping approach which is based on knowledge graph embeddings: The ontologies to be matched are embedded, and an approach known as absolute orientation is used to align the two embedd...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	true	false	290,515
2305.12523	Multi-Static Target Detection and Power Allocation for Integrated Sensing and Communication in Cell-Free Massive MIMO	This paper studies an integrated sensing and communication (ISAC) system within a centralized cell-free massive MIMO (multiple-input multiple-output) network for target detection. ISAC transmit access points serve the user equipments in the downlink and optionally steer a beam toward the target in a multi-static sensin...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	366,042
2210.00120	NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning	Neural Motion Planners (NMPs) have emerged as a promising tool for solving robot navigation tasks in complex environments. However, these methods often require expert data for learning, which limits their application to scenarios where data generation is time-consuming. Recent developments have also led to physics-info...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	320,739
1704.06765	Subspace Tracking Algorithms for Millimeter Wave MIMO Channel Estimation with Hybrid Beamforming	This paper proposes the use of subspace tracking algorithms for performing MIMO channel estimation at millimeter wave (mmWave) frequencies. Using a subspace approach, we develop a protocol enabling the estimation of the right (resp. left) singular vectors at the transmitter (resp. receiver) side; then, we adapt the pro...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	72,219
2209.06423	SCULPTOR: Skeleton-Consistent Face Creation Using a Learned Parametric Generator	Recent years have seen growing interest in 3D human faces modelling due to its wide applications in digital human, character generation and animation. Existing approaches overwhelmingly emphasized on modeling the exterior shapes, textures and skin properties of faces, ignoring the inherent correlation between inner ske...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	317,399
2212.11966	Removing Objects From Neural Radiance Fields	Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Before sharing a NeRF, though, it might be desirable to remove personal information or unsightly objects. Such removal is not easily achieved wi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	337,927
2410.07272	Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration	Decentralized Federated Learning has emerged as an alternative to centralized architectures due to its faster training, privacy preservation, and reduced communication overhead. In decentralized communication, the server aggregation phase in Centralized Federated Learning shifts to the client side, which means that cli...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	496,564

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