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
1902.01048	Average cost optimal control under weak ergodicity hypotheses: Relative value iterations	We study Markov decision processes with Polish state and action spaces. The action space is state dependent and is not necessarily compact. We first establish the existence of an optimal ergodic occupation measure using only a near-monotone hypothesis on the running cost. Then we study the well-posedness of Bellman equ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	120,577
2203.05956	Label-efficient Hybrid-supervised Learning for Medical Image Segmentation	Due to the lack of expertise for medical image annotation, the investigation of label-efficient methodology for medical image segmentation becomes a heated topic. Recent progresses focus on the efficient utilization of weak annotations together with few strongly-annotated labels so as to achieve comparable segmentation...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	284,969
1709.01696	User Assignment with Distributed Large Intelligent Surface (LIS) Systems	In this paper, we consider a wireless communication system where a large intelligent surface (LIS) is deployed comprising a number of small and distributed LIS-Units. Each LIS-Unit has a separate signal process unit (SPU) and is connected to a central process unit (CPU) that coordinates the behaviors of all the LIS-Uni...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	80,133
2105.03190	Joint Subcarrier and Power Allocation in MU OFDM DCSK Systems with Noise Reduction	This paper investigates the joint number of subcarriers and power optimization in Multi user Orthogonal Frequency Division Multiplexing based Differential Chaotic Shift Keying systems with noise reduction. We first find a closed-form expression for the optimal number of references in MU OFDM DCSK systems, while existin...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	234,072
2305.13019	Robots in the Garden: Artificial Intelligence and Adaptive Landscapes	This paper introduces ELUA, the Ecological Laboratory for Urban Agriculture, a collaboration among landscape architects, architects and computer scientists who specialize in artificial intelligence, robotics and computer vision. ELUA has two gantry robots, one indoors and the other outside on the rooftop of a 6-story c...	false	false	false	false	true	false	false	true	false	false	false	true	false	true	false	false	false	false	366,301
2306.03018	Quantification of Uncertainties in Deep Learning-based Environment Perception	In this work, we introduce a novel Deep Learning-based method to perceive the environment of a vehicle based on radar scans while accounting for uncertainties in its predictions. The environment of the host vehicle is segmented into equally sized grid cells which are classified individually. Complementary to the segmen...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	371,156
2409.16499	Learning Linear Dynamics from Bilinear Observations	We consider the problem of learning a realization of a partially observed dynamical system with linear state transitions and bilinear observations. Under very mild assumptions on the process and measurement noises, we provide a finite time analysis for learning the unknown dynamics matrices (up to a similarity transfor...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	491,371
2412.02889	Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows	The diffusion learning method, DiffDock, for docking small-molecule ligands into protein binding sites was recently introduced. Results included comparisons to more conventional docking approaches, with DiffDock showing superior performance. Here, we employ a fully automatic workflow using the Surflex-Dock methods to g...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	513,724
2308.14985	Stochastic Motion Planning as Gaussian Variational Inference: Theory and Algorithms	We present a novel formulation for motion planning under uncertainties based on variational inference where the optimal motion plan is modeled as a posterior distribution. We propose a Gaussian variational inference-based framework, termed Gaussian Variational Inference Motion Planning (GVI-MP), to approximate this pos...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	388,520
2011.03149	Affinity LCFCN: Learning to Segment Fish with Weak Supervision	Aquaculture industries rely on the availability of accurate fish body measurements, e.g., length, width and mass. Manual methods that rely on physical tools like rulers are time and labour intensive. Leading automatic approaches rely on fully-supervised segmentation models to acquire these measurements but these requir...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	205,149
2502.07562	LoRP-TTS: Low-Rank Personalized Text-To-Speech	Speech synthesis models convert written text into natural-sounding audio. While earlier models were limited to a single speaker, recent advancements have led to the development of zero-shot systems that generate realistic speech from a wide range of speakers using their voices as additional prompts. However, they still...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	532,663
2206.08725	Galois LCD Codes Over Fq + uFq + vFq + uvFq	In \cite{anote}, Wu and Shi studied $ l $-Galois LCD codes over finite chain ring $\mathcal{R}=\mathbb{F}_q+u\mathbb{F}_q$, where $u^2=0$ and $ q=p^e$ for some prime $p$ and positive integer $e$. In this work, we extend the results to the finite non chain ring $ \mathcal{R} =\mathbb{F}_q+u\mathbb{F}_q+v\mathbb{F}_q+uv\...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	303,267
1709.07403	Inducing Distant Supervision in Suggestion Mining through Part-of-Speech Embeddings	Mining suggestion expressing sentences from a given text is a less investigated sentence classification task, and therefore lacks hand labeled benchmark datasets. In this work, we propose and evaluate two approaches for distant supervision in suggestion mining. The distant supervision is obtained through a large silver...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	81,271
2501.07086	Boosting Text-To-Image Generation via Multilingual Prompting in Large Multimodal Models	Previous work on augmenting large multimodal models (LMMs) for text-to-image (T2I) generation has focused on enriching the input space of in-context learning (ICL). This includes providing a few demonstrations and optimizing image descriptions to be more detailed and logical. However, as demand for more complex and fle...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	524,259
2109.01765	Effective user intent mining with unsupervised word representation models and topic modelling	Understanding the intent behind chat between customers and customer service agents has become a crucial problem nowadays due to an exponential increase in the use of the Internet by people from different cultures and educational backgrounds. More importantly, the explosion of e-commerce has led to a significant increas...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	253,527
2206.03113	Wavelet Prior Attention Learning in Axial Inpainting Network	Image inpainting is the task of filling masked or unknown regions of an image with visually realistic contents, which has been remarkably improved by Deep Neural Networks (DNNs) recently. Essentially, as an inverse problem, the inpainting has the underlying challenges of reconstructing semantically coherent results wit...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	301,153
2301.05635	Time-Myopic Go-Explore: Learning A State Representation for the Go-Explore Paradigm	Very large state spaces with a sparse reward signal are difficult to explore. The lack of a sophisticated guidance results in a poor performance for numerous reinforcement learning algorithms. In these cases, the commonly used random exploration is often not helpful. The literature shows that this kind of environments ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	340,408
1709.03656	Joint Adaptive Neighbours and Metric Learning for Multi-view Subspace Clustering	Due to the existence of various views or representations in many real-world data, multi-view learning has drawn much attention recently. Multi-view spectral clustering methods based on similarity matrixes or graphs are pretty popular. Generally, these algorithms learn informative graphs by directly utilizing original d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	80,499
2311.15230	GAIA: Zero-shot Talking Avatar Generation	Zero-shot talking avatar generation aims at synthesizing natural talking videos from speech and a single portrait image. Previous methods have relied on domain-specific heuristics such as warping-based motion representation and 3D Morphable Models, which limit the naturalness and diversity of the generated avatars. In ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	410,435
2502.07355	Performance Bounds and Degree-Distribution Optimization of Finite-Length BATS Codes	Batched sparse (BATS) codes were proposed as a reliable communication solution for networks with packet loss. In the finite-length regime, the error probability of BATS codes under belief propagation (BP) decoding has been studied in the literature and can be analyzed by recursive formulae. However, all existing analys...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	532,562
1201.6615	Feature Selection for Value Function Approximation Using Bayesian Model Selection	Feature selection in reinforcement learning (RL), i.e. choosing basis functions such that useful approximations of the unkown value function can be obtained, is one of the main challenges in scaling RL to real-world applications. Here we consider the Gaussian process based framework GPTD for approximate policy evaluati...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	14,029
2502.04260	Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention	Machine Unlearning allows participants to remove their data from a trained machine learning model in order to preserve their privacy, and security. However, the machine unlearning literature for generative models is rather limited. The literature for image-to-image generative model (I2I model) considers minimizing the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	531,042
2109.02748	Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIP	In an out-of-distribution (OOD) detection problem, samples of known classes(also called in-distribution classes) are used to train a special classifier. In testing, the classifier can (1) classify the test samples of known classes to their respective classes and also (2) detect samples that do not belong to any of the ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	253,838
2111.14799	UBoCo : Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection	Generic Event Boundary Detection (GEBD) is a newly suggested video understanding task that aims to find one level deeper semantic boundaries of events. Bridging the gap between natural human perception and video understanding, it has various potential applications, including interpretable and semantically valid video p...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	268,709
2207.13543	Abstracting Sketches through Simple Primitives	Humans show high-level of abstraction capabilities in games that require quickly communicating object information. They decompose the message content into multiple parts and communicate them in an interpretable protocol. Toward equipping machines with such capabilities, we propose the Primitive-based Sketch Abstraction...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	310,338
1809.10934	Strong Coordination over Noisy Channels with Strictly Causal Encoding	We consider a network of two nodes separated by a noisy channel, in which the input and output signals have to be coordinated with the source and its reconstruction. In the case of strictly causal encoding and non-causal decoding, we prove inner and outer bounds for the strong coordination region and show that the inne...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	109,018
1702.08396	Learning Hierarchical Features from Generative Models	Deep neural networks have been shown to be very successful at learning feature hierarchies in supervised learning tasks. Generative models, on the other hand, have benefited less from hierarchical models with multiple layers of latent variables. In this paper, we prove that hierarchical latent variable models do not ta...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	68,975
1810.10363	G-SMOTE: A GMM-based synthetic minority oversampling technique for imbalanced learning	Imbalanced Learning is an important learning algorithm for the classification models, which have enjoyed much popularity on many applications. Typically, imbalanced learning algorithms can be partitioned into two types, i.e., data level approaches and algorithm level approaches. In this paper, the focus is to develop a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	111,278
2203.12023	Generative Modeling Helps Weak Supervision (and Vice Versa)	Many promising applications of supervised machine learning face hurdles in the acquisition of labeled data in sufficient quantity and quality, creating an expensive bottleneck. To overcome such limitations, techniques that do not depend on ground truth labels have been studied, including weak supervision and generative...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	287,111
2409.06013	Improved Visually Prompted Keyword Localisation in Real Low-Resource Settings	Given an image query, visually prompted keyword localisation (VPKL) aims to find occurrences of the depicted word in a speech collection. This can be useful when transcriptions are not available for a low-resource language (e.g. if it is unwritten). Previous work showed that VPKL can be performed with a visually ground...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	486,970
2501.13551	Minimizing Queue Length Regret for Arbitrarily Varying Channels	We consider an online channel scheduling problem for a single transmitter-receiver pair equipped with $N$ arbitrarily varying wireless channels. The transmission rates of the channels might be non-stationary and could be controlled by an oblivious adversary. At every slot, incoming data arrives at an infinite-capacity ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	526,732
1605.00303	A Self-Taught Artificial Agent for Multi-Physics Computational Model Personalization	Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence c...	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	55,323
1604.04677	Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction	We demonstrate that an attention-based encoder-decoder model can be used for sentence-level grammatical error identification for the Automated Evaluation of Scientific Writing (AESW) Shared Task 2016. The attention-based encoder-decoder models can be used for the generation of corrections, in addition to error identifi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	54,685
2004.12587	Binary MIMO Detection via Homotopy Optimization and Its Deep Adaptation	In this paper we consider maximum-likelihood (ML) MIMO detection under one-bit quantized observations and binary symbol constellations. This problem is motivated by the recent interest in adopting coarse quantization in massive MIMO systems--as an effective way to scale down the hardware complexity and energy consumpti...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	174,294
2102.09900	Identifying and Mapping the Global Research Output on Coronavirus Disease: A Scientometric Study	The paper explores and analyses the trend of world literature on "Coronavirus Disease" in terms of the output of research publications as indexed in the Science Citation Index Expanded (SCI-E) of Web of Science during the period from 2011 to 2020. The study found that 6071 research records have been published on Corona...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	220,922
1902.00993	Improving Question Answering with External Knowledge	We focus on multiple-choice question answering (QA) tasks in subject areas such as science, where we require both broad background knowledge and the facts from the given subject-area reference corpus. In this work, we explore simple yet effective methods for exploiting two sources of external knowledge for subject-area...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	120,559
2402.04087	A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation	Contrastive Language-Image Pretraining (CLIP) has gained popularity for its remarkable zero-shot capacity. Recent research has focused on developing efficient fine-tuning methods, such as prompt learning and adapter, to enhance CLIP's performance in downstream tasks. However, these methods still require additional trai...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	427,327
2404.15354	Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach	Spectral graph neural networks are proposed to harness spectral information inherent in graph-structured data through the application of polynomial-defined graph filters, recently achieving notable success in graph-based web applications. Existing studies reveal that various polynomial choices greatly impact spectral G...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	449,064
1702.01847	Low Rank Matrix Recovery with Simultaneous Presence of Outliers and Sparse Corruption	We study a data model in which the data matrix D can be expressed as D = L + S + C, where L is a low rank matrix, S an element-wise sparse matrix and C a matrix whose non-zero columns are outlying data points. To date, robust PCA algorithms have solely considered models with either S or C, but not both. As such, existi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	67,884
2409.08106	Hypergraph Change Point Detection using Adapted Cardinality-Based Gadgets: Applications in Dynamic Legal Structures	Hypergraphs provide a robust framework for modeling complex systems with higher-order interactions. However, analyzing them in dynamic settings presents significant computational challenges. To address this, we introduce a novel method that adapts the cardinality-based gadget to convert hypergraphs into strongly connec...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	487,774
2208.11122	Distance-Aware Occlusion Detection with Focused Attention	For humans, understanding the relationships between objects using visual signals is intuitive. For artificial intelligence, however, this task remains challenging. Researchers have made significant progress studying semantic relationship detection, such as human-object interaction detection and visual relationship dete...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	314,318
2209.06899	Out of One, Many: Using Language Models to Simulate Human Samples	We propose and explore the possibility that language models can be studied as effective proxies for specific human sub-populations in social science research. Practical and research applications of artificial intelligence tools have sometimes been limited by problematic biases (such as racism or sexism), which are ofte...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	317,541
2006.07409	How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds	Text-based games are long puzzles or quests, characterized by a sequence of sparse and potentially deceptive rewards. They provide an ideal platform to develop agents that perceive and act upon the world using a combinatorially sized natural language state-action space. Standard Reinforcement Learning agents are poorly...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	181,784
1005.0907	Multistage Hybrid Arabic/Indian Numeral OCR System	The use of OCR in postal services is not yet universal and there are still many countries that process mail sorting manually. Automated Arabic/Indian numeral Optical Character Recognition (OCR) systems for Postal services are being used in some countries, but still there are errors during the mail sorting process, thus...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	6,419
1904.06492	Properties of Inconsistency Measures for Databases	How should we quantify the inconsistency of a database that violates integrity constraints? Proper measures are important for various tasks, such as progress indication and action prioritization in cleaning systems, and reliability estimation for new datasets. To choose an appropriate inconsistency measure, it is impor...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	127,564
1203.5638	On MMSE Properties and I-MMSE Implications in Parallel MIMO Gaussian Channels	The scalar additive Gaussian noise channel has the "single crossing point" property between the minimum-mean square error (MMSE) in the estimation of the input given the channel output, assuming a Gaussian input to the channel, and the MMSE assuming an arbitrary input. This paper extends the result to the parallel MIMO...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	15,123
1904.04866	Characterizing the impact of geometric properties of word embeddings on task performance	Analysis of word embedding properties to inform their use in downstream NLP tasks has largely been studied by assessing nearest neighbors. However, geometric properties of the continuous feature space contribute directly to the use of embedding features in downstream models, and are largely unexplored. We consider four...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	127,144
2308.01519	Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperation	For Industry 4.0 Revolution, cooperative autonomous mobility systems are widely used based on multi-agent reinforcement learning (MARL). However, the MARL-based algorithms suffer from huge parameter utilization and convergence difficulties with many agents. To tackle these problems, a quantum MARL (QMARL) algorithm bas...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	383,273
1906.04772	A Systematic Comparison of English Noun Compound Representations	Building meaningful representations of noun compounds is not trivial since many of them scarcely appear in the corpus. To that end, composition functions approximate the distributional representation of a noun compound by combining its constituent distributional vectors. In the more general case, phrase embeddings have...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	134,834
2112.14853	Effects of Plasticity Functions on Neural Assemblies	We explore the effects of various plasticity functions on assemblies of neurons. To bridge the gap between experimental and computational theories we make use of a conceptual framework, the Assembly Calculus, which is a formal system for the description of brain function based on assemblies of neurons. The Assembly Cal...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	273,631
2110.07918	Estimating the Level and Direction of Phonetic Dialect Change in the Northern Netherlands	This article reports ongoing investigations into phonetic change of dialect groups in the northern Netherlandic language area, particularly the Frisian and Low Saxon dialect groups, which are known to differ in vitality. To achieve this, we combine existing phonetically transcribed corpora with dialectometric approache...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	261,182
2309.13375	Generative Retrieval with Semantic Tree-Structured Item Identifiers via Contrastive Learning	The retrieval phase is a vital component in recommendation systems, requiring the model to be effective and efficient. Recently, generative retrieval has become an emerging paradigm for document retrieval, showing notable performance. These methods enjoy merits like being end-to-end differentiable, suggesting their via...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	394,180
1409.6041	Domain Adaptive Neural Networks for Object Recognition	We propose a simple neural network model to deal with the domain adaptation problem in object recognition. Our model incorporates the Maximum Mean Discrepancy (MMD) measure as a regularization in the supervised learning to reduce the distribution mismatch between the source and target domains in the latent space. From ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	true	false	false	36,215
2207.14403	Interactive Evaluation of Dialog Track at DSTC9	The ultimate goal of dialog research is to develop systems that can be effectively used in interactive settings by real users. To this end, we introduced the Interactive Evaluation of Dialog Track at the 9th Dialog System Technology Challenge. This track consisted of two sub-tasks. The first sub-task involved building ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	310,570
2405.15019	Agentic Skill Discovery	Language-conditioned robotic skills make it possible to apply the high-level reasoning of Large Language Models (LLMs) to low-level robotic control. A remaining challenge is to acquire a diverse set of fundamental skills. Existing approaches either manually decompose a complex task into atomic robotic actions in a top-...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	456,702
2409.14775	Like a Martial Arts Dodge: Safe Expeditious Whole-Body Control of Mobile Manipulators for Collision Avoidance	In the control task of mobile manipulators(MM), achieving efficient and agile obstacle avoidance in dynamic environments is challenging. In this letter, we present a safe expeditious whole-body(SEWB) control for MMs that ensures both external and internal collision-free. SEWB is constructed by a two-layer optimization ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	490,626
2011.00912	Facial UV Map Completion for Pose-invariant Face Recognition: A Novel Adversarial Approach based on Coupled Attention Residual UNets	Pose-invariant face recognition refers to the problem of identifying or verifying a person by analyzing face images captured from different poses. This problem is challenging due to the large variation of pose, illumination and facial expression. A promising approach to deal with pose variation is to fulfill incomplete...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	204,421
2407.19326	Accounting for plasticity: An extension of inelastic Constitutive Artificial Neural Networks	The class of Constitutive Artificial Neural Networks (CANNs) represents a new approach of neural networks in the field of constitutive modeling. So far, CANNs have proven to be a powerful tool in predicting elastic and inelastic material behavior. However, the specification of inelastic constitutive artificial neural n...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	476,740
2311.14405	OneFormer3D: One Transformer for Unified Point Cloud Segmentation	Semantic, instance, and panoptic segmentation of 3D point clouds have been addressed using task-specific models of distinct design. Thereby, the similarity of all segmentation tasks and the implicit relationship between them have not been utilized effectively. This paper presents a unified, simple, and effective model ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	410,098
2404.01094	HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach	Our paper addresses the complex task of transferring a hairstyle from a reference image to an input photo for virtual hair try-on. This task is challenging due to the need to adapt to various photo poses, the sensitivity of hairstyles, and the lack of objective metrics. The current state of the art hairstyle transfer m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	443,246
2403.02738	Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment	Despite the notable advancements of existing prompting methods, such as In-Context Learning and Chain-of-Thought for Large Language Models (LLMs), they still face challenges related to various biases. Traditional debiasing methods primarily focus on the model training stage, including approaches based on data augmentat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	434,924
2105.15022	Reinforcement Learning-based Dynamic Service Placement in Vehicular Networks	The emergence of technologies such as 5G and mobile edge computing has enabled provisioning of different types of services with different resource and service requirements to the vehicles in a vehicular network.The growing complexity of traffic mobility patterns and dynamics in the requests for different types of servi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	237,888
2401.11524	Controlling the Misinformation Diffusion in Social Media by the Effect of Different Classes of Agents	The rapid and widespread dissemination of misinformation through social networks is a growing concern in today's digital age. This study focused on modeling fake news diffusion, discovering the spreading dynamics, and designing control strategies. A common approach for modeling the misinformation dynamics is SIR-based ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	423,039
2302.09277	Promoting Cooperation in Multi-Agent Reinforcement Learning via Mutual Help	Multi-agent reinforcement learning (MARL) has achieved great progress in cooperative tasks in recent years. However, in the local reward scheme, where only local rewards for each agent are given without global rewards shared by all the agents, traditional MARL algorithms lack sufficient consideration of agents' mutual ...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	true	false	false	false	346,353
2206.05446	A Decomposition-Based Approach for Evaluating Inter-Annotator Disagreement in Narrative Analysis	In this work, we explore sources of inter-annotator disagreement in narrative analysis, in light of the question of whether or not a narrative plot exists in the text. For this purpose, we present a method for a conceptual decomposition of an existing annotation into two separate levels: (1) \textbf{whether} or not a n...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	302,008
2002.08599	On Learning Sets of Symmetric Elements	Learning from unordered sets is a fundamental learning setup, recently attracting increasing attention. Research in this area has focused on the case where elements of the set are represented by feature vectors, and far less emphasis has been given to the common case where set elements themselves adhere to their own sy...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	164,810
2412.17250	SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval	The performance of Dense retrieval (DR) is significantly influenced by the quality of negative sampling. Traditional DR methods primarily depend on naive negative sampling techniques or on mining hard negatives through external retriever and meticulously crafted strategies. However, naive negative sampling often fails ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	519,889
1908.01357	Exact BER Performance Analysis for Downlink NOMA Systems Over Nakagami-m Fading Channels	In this paper, the performance of a promising technology for the next generation wireless communications, non-orthogonal multiple access (NOMA), is investigated. In particular, the bit error rate (BER) performance of downlink NOMA systems over Nakagami-m flat fading channels, is presented. Under various conditions and ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	140,744
1708.06975	Generating Visual Representations for Zero-Shot Classification	This paper addresses the task of learning an image clas-sifier when some categories are defined by semantic descriptions only (e.g. visual attributes) while the others are defined by exemplar images as well. This task is often referred to as the Zero-Shot classification task (ZSC). Most of the previous methods rely on ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	79,412
2208.09747	Near-Optimal $\Phi$-Regret Learning in Extensive-Form Games	In this paper, we establish efficient and uncoupled learning dynamics so that, when employed by all players in multiplayer perfect-recall imperfect-information extensive-form games, the trigger regret of each player grows as $O(\log T)$ after $T$ repetitions of play. This improves exponentially over the prior best know...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	313,820
2302.03183	Capturing Topic Framing via Masked Language Modeling	Differential framing of issues can lead to divergent world views on important issues. This is especially true in domains where the information presented can reach a large audience, such as traditional and social media. Scalable and reliable measurement of such differential framing is an important first step in addressi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	344,247
2411.04677	Lightning IR: Straightforward Fine-tuning and Inference of Transformer-based Language Models for Information Retrieval	A wide range of transformer-based language models have been proposed for information retrieval tasks. However, including transformer-based models in retrieval pipelines is often complex and requires substantial engineering effort. In this paper, we introduce Lightning IR, an easy-to-use PyTorch Lightning-based framewor...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	506,364
2305.16748	A Decentralized Spike-based Learning Framework for Sequential Capture in Discrete Perimeter Defense Problem	This paper proposes a novel Decentralized Spike-based Learning (DSL) framework for the discrete Perimeter Defense Problem (d-PDP). A team of defenders is operating on the perimeter to protect the circular territory from radially incoming intruders. At first, the d-PDP is formulated as a spatio-temporal multi-task assig...	false	false	false	false	true	false	true	true	false	false	true	false	false	false	true	true	false	false	368,237
2502.02998	Conformal Uncertainty Indicator for Continual Test-Time Adaptation	Continual Test-Time Adaptation (CTTA) aims to adapt models to sequentially changing domains during testing, relying on pseudo-labels for self-adaptation. However, incorrect pseudo-labels can accumulate, leading to performance degradation. To address this, we propose a Conformal Uncertainty Indicator (CUI) for CTTA, lev...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	530,558
2206.03465	On entropic and almost multilinear representability of matroids	This article studies two notions of generalized matroid representations motivated by algorithmic information theory and cryptographic secret sharing. The first (entropic representability) involves discrete random variables, while the second (almost-multilinear representability) deals with approximate subspace arrangeme...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	301,294
1311.4833	Domain Adaptation of Majority Votes via Perturbed Variation-based Label Transfer	We tackle the PAC-Bayesian Domain Adaptation (DA) problem. This arrives when one desires to learn, from a source distribution, a good weighted majority vote (over a set of classifiers) on a different target distribution. In this context, the disagreement between classifiers is known crucial to control. In non-DA superv...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	28,527
2010.01500	Affine Linear Parameter-Varying Embedding of Nonlinear Models with Improved Accuracy and Minimal Overbounding	In this paper, automated generation of linear parameter-varying (LPV) state-space models to embed the dynamical behavior of nonlinear systems is considered, focusing on the trade-off between scheduling complexity and model accuracy and on the minimization of the conservativeness of the resulting embedding. The LPV stat...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	198,680
2211.15118	A Faster $k$-means++ Algorithm	$k$-means++ is an important algorithm for choosing initial cluster centers for the $k$-means clustering algorithm. In this work, we present a new algorithm that can solve the $k$-means++ problem with nearly optimal running time. Given $n$ data points in $\mathbb{R}^d$, the current state-of-the-art algorithm runs in $\w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	333,127
1605.08878	Computational Estimate Visualisation and Evaluation of Agent Classified Rules Learning System	Student modelling and agent classified rules learning as applied in the development of the intelligent Preassessment System has been presented in [10],[11]. In this paper, we now demystify the theory behind the development of the pre-assessment system followed by some computational experimentation and graph visualisati...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	56,491
cs/0505045	A T Step Ahead Optimal Target Detection Algorithm for a Multi Sensor Surveillance System	This paper presents a methodology for optimal target detection in a multi sensor surveillance system. The system consists of mobile sensors that guard a rectangular surveillance zone crisscrossed by moving targets. Targets percolate the surveillance zone in a poisson fashion with uniform velocities. Under these statist...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	538,719
2010.01247	Interpreting Robust Optimization via Adversarial Influence Functions	Robust optimization has been widely used in nowadays data science, especially in adversarial training. However, little research has been done to quantify how robust optimization changes the optimizers and the prediction losses comparing to standard training. In this paper, inspired by the influence function in robust s...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	198,577
2411.16403	Adapter-based Approaches to Knowledge-enhanced Language Models -- A Survey	Knowledge-enhanced language models (KELMs) have emerged as promising tools to bridge the gap between large-scale language models and domain-specific knowledge. KELMs can achieve higher factual accuracy and mitigate hallucinations by leveraging knowledge graphs (KGs). They are frequently combined with adapter modules to...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	511,011
1810.04513	ET-Lasso: A New Efficient Tuning of Lasso-type Regularization for High-Dimensional Data	The L1 regularization (Lasso) has proven to be a versatile tool to select relevant features and estimate the model coefficients simultaneously and has been widely used in many research areas such as genomes studies, finance, and biomedical imaging. Despite its popularity, it is very challenging to guarantee the feature...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	110,060
2012.10544	Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses	As machine learning systems grow in scale, so do their training data requirements, forcing practitioners to automate and outsource the curation of training data in order to achieve state-of-the-art performance. The absence of trustworthy human supervision over the data collection process exposes organizations to securi...	false	false	false	false	true	false	true	false	false	false	false	true	true	false	false	false	false	false	212,368
2105.06073	Good and Bad Optimization Models: Insights from Rockafellians	A basic requirement for a mathematical model is often that its solution (output) shouldn't change much if the model's parameters (input) are perturbed. This is important because the exact values of parameters may not be known and one would like to avoid being mislead by an output obtained using incorrect values. Thus, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	235,014
2305.07882	Dual Use Concerns of Generative AI and Large Language Models	We suggest the implementation of the Dual Use Research of Concern (DURC) framework, originally designed for life sciences, to the domain of generative AI, with a specific focus on Large Language Models (LLMs). With its demonstrated advantages and drawbacks in biological research, we believe the DURC criteria can be eff...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	364,068
2005.13289	Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection	The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems. The two sophisticated heuristic solvers LKH and EAX and respective (restart) variants manage to calculate close-to optimal or even optimal solutions, also for large instances with several thousand node...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	178,973
2303.15097	Control-oriented modeling of a LiBr/H2O absorption heat pumping device and experimental validation	Absorption heat pumping devices (AHPDs, comprising absorption heat pumps and chillers) are devices that use thermal energy instead of electricity to generate heating and cooling, thereby facilitating the use of waste heat and renewable energy sources such as solar or geothermal energy. Despite this benefit, widespread ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	354,358
2106.13314	Promises and Pitfalls of Black-Box Concept Learning Models	Machine learning models that incorporate concept learning as an intermediate step in their decision making process can match the performance of black-box predictive models while retaining the ability to explain outcomes in human understandable terms. However, we demonstrate that the concept representations learned by t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	243,029
1812.03163	Transfer learning for vision-based tactile sensing	Due to the complexity of modeling the elastic properties of materials, the use of machine learning algorithms is continuously increasing for tactile sensing applications. Recent advances in deep neural networks applied to computer vision make vision-based tactile sensors very appealing for their high-resolution and low...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	115,940
1609.05115	Dense Wide-Baseline Scene Flow From Two Handheld Video Cameras	We propose a new technique for computing dense scene flow from two handheld videos with wide camera baselines and different photometric properties due to different sensors or camera settings like exposure and white balance. Our technique innovates in two ways over existing methods: (1) it supports independently moving ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	61,076
1805.03196	Suburban Residential Building Penetration Loss at 28 GHz for Fixed Wireless Access	Fixed wireless access at mm/cm bands has been proposed for high-speed broadband access to suburban residential customers and building penetration loss is a key parameter. We report a measurement campaign at 28 GHz in a New Jersey suburban residential neighborhood for three representative single-family homes. A base ant...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	97,004
2406.07114	Unlocking the Potential of Metaverse in Innovative and Immersive Digital Health	The concept of Metaverse has attracted a lot of attention in various fields and one of its important applications is health and treatment. The Metaverse has enormous potential to transform healthcare by changing patient care, medical education, and the way teaching/learning and research are done. The purpose of this re...	false	false	false	false	true	true	false	false	false	false	false	false	false	true	false	false	false	false	462,912
2206.05753	Concurrent Learning Based Adaptive Control of Euler Lagrange Systems with Guaranteed Parameter Convergence	This work presents a solution to the adaptive tracking control of Euler Lagrange systems with guaranteed tracking and parameter estimation error convergence. Specifically a concurrent learning based update rule fused by the filtered version of the desired system dynamics in conjunction with a desired state based regres...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	302,124
2012.01918	Multi-mode Core Tensor Factorization based Low-Rankness and Its Applications to Tensor Completion	Low-rank tensor completion has been widely used in computer vision and machine learning. This paper develops a novel multi-modal core tensor factorization (MCTF) method combined with a tensor low-rankness measure and a better nonconvex relaxation form of this measure (NC-MCTF). The proposed models encode low-rank insig...	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	209,570
2111.14956	Third-Party Hardware IP Assurance against Trojans through Supervised Learning and Post-processing	System-on-chip (SoC) developers increasingly rely on pre-verified hardware intellectual property (IP) blocks acquired from untrusted third-party vendors. These IPs might contain hidden malicious functionalities or hardware Trojans to compromise the security of the fabricated SoCs. Recently, supervised machine learning ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	268,757
0710.1626	Throughput Scaling in Random Wireless Networks: A Non-Hierarchical Multipath Routing Strategy	Franceschetti et al. have recently shown that per-node throughput in an extended, ad hoc wireless network with $\Theta(n)$ randomly distributed nodes and multihop routing can be increased from the $\Omega({1 \over \sqrt{n} \log n})$ scaling demonstrated in the seminal paper of Gupta and Kumar to $\Omega({1 \over \sqrt{...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	759
2409.15525	Speech2rtMRI: Speech-Guided Diffusion Model for Real-time MRI Video of the Vocal Tract during Speech	Understanding speech production both visually and kinematically can inform second language learning system designs, as well as the creation of speaking characters in video games and animations. In this work, we introduce a data-driven method to visually represent articulator motion in Magnetic Resonance Imaging (MRI) v...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	490,947
2108.00406	Discovering Distinctive "Semantics" in Super-Resolution Networks	Image super-resolution (SR) is a representative low-level vision problem. Although deep SR networks have achieved extraordinary success, we are still unaware of their working mechanisms. Specifically, whether SR networks can learn semantic information, or just perform complex mapping function? What hinders SR networks ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	248,712
2011.11378	Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights	The quality grading of mangoes is a crucial task for mango growers as it vastly affects their profit. However, until today, this process still relies on laborious efforts of humans, who are prone to fatigue and errors. To remedy this, the paper approaches the grading task with various convolutional neural networks (CNN...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	207,811

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