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
2204.00942	A-ACT: Action Anticipation through Cycle Transformations	While action anticipation has garnered a lot of research interest recently, most of the works focus on anticipating future action directly through observed visual cues only. In this work, we take a step back to analyze how the human capability to anticipate the future can be transferred to machine learning algorithms. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	289,442
2312.04746	Quilt-LLaVA: Visual Instruction Tuning by Extracting Localized Narratives from Open-Source Histopathology Videos	Diagnosis in histopathology requires a global whole slide images (WSIs) analysis, requiring pathologists to compound evidence from different WSI patches. The gigapixel scale of WSIs poses a challenge for histopathology multi-modal models. Training multi-model models for histopathology requires instruction tuning datase...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	413,808
1801.07145	E-swish: Adjusting Activations to Different Network Depths	Activation functions have a notorious impact on neural networks on both training and testing the models against the desired problem. Currently, the most used activation function is the Rectified Linear Unit (ReLU). This paper introduces a new and novel activation function, closely related with the new activation $Swish...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	88,731
2311.03362	Simulation-based Safety Assurance for an AVP System incorporating Learning-Enabled Components	There have been major developments in Automated Driving (AD) and Driving Assist Systems (ADAS) in recent years. However, their safety assurance, thus methodologies for testing, verification and validation AD/ADAS safety-critical applications remain as one the main challenges. Inevitably AI also penetrates into AD/ADAS ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	405,817
2110.03861	QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks	The advent of noisy intermediate-scale quantum (NISQ) computers raises a crucial challenge to design quantum neural networks for fully quantum learning tasks. To bridge the gap, this work proposes an end-to-end learning framework named QTN-VQC, by introducing a trainable quantum tensor network (QTN) for quantum embeddi...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	true	false	false	259,652
1703.07595	Can you tell where in India I am from? Comparing humans and computers on fine-grained race face classification	Faces form the basis for a rich variety of judgments in humans, yet the underlying features remain poorly understood. Although fine-grained distinctions within a race might more strongly constrain possible facial features used by humans than in case of coarse categories such as race or gender, such fine grained distinc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	70,425
2405.20471	Equivalent External Noise Temperature of Time-Varying Receivers	The equivalent external noise temperature of time-varying antennas is studied using the concept of cross-frequency effective aperture, which quantifies the intermodulation conversion of external noise across the frequency spectrum into a receiver's operational bandwidth. The theoretical tools for this approach are laid...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	459,365
1407.1785	Novel methods for multilinear data completion and de-noising based on tensor-SVD	In this paper we propose novel methods for completion (from limited samples) and de-noising of multilinear (tensor) data and as an application consider 3-D and 4- D (color) video data completion and de-noising. We exploit the recently proposed tensor-Singular Value Decomposition (t-SVD)[11]. Based on t-SVD, the notion ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	34,470
1903.07350	Network Weight Estimation for Binary-Valued Observation Models	This paper studies the estimation of network weights for a class of systems with binary-valued observations. In these systems only quantized observations are available for the network estimation. Furthermore, system states are coupled with observations, and the quantization parts are unknown inherent components, which ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	124,597
1110.2741	An Algebraic Graphical Model for Decision with Uncertainties, Feasibilities, and Utilities	Numerous formalisms and dedicated algorithms have been designed in the last decades to model and solve decision making problems. Some formalisms, such as constraint networks, can express "simple" decision problems, while others are designed to take into account uncertainties, unfeasible decisions, and utilities. Even i...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	12,619
2304.09599	DECN: Evolution Inspired Deep Convolution Network for Black-box Optimization	Evolutionary algorithms (EAs) have emerged as a powerful framework for optimization, especially for black-box optimization. Existing evolutionary algorithms struggle to comprehend and effectively utilize task-specific information for adjusting their optimization strategies, leading to subpar performance on target tasks...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	359,107
2305.05418	Measuring Rule-based LTLf Process Specifications: A Probabilistic Data-driven Approach	Declarative process specifications define the behavior of processes by means of rules based on Linear Temporal Logic on Finite Traces (LTLf). In a mining context, these specifications are inferred from, and checked on, multi-sets of runs recorded by information systems (namely, event logs). To this end, being able to g...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	363,141
1806.09316	Vision-based Pose Estimation for Augmented Reality : A Comparison Study	Augmented reality aims to enrich our real world by inserting 3D virtual objects. In order to accomplish this goal, it is important that virtual elements are rendered and aligned in the real scene in an accurate and visually acceptable way. The solution of this problem can be related to a pose estimation and 3D camera l...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	101,332
2501.00265	Outlier-Robust Training of Machine Learning Models	Robust training of machine learning models in the presence of outliers has garnered attention across various domains. The use of robust losses is a popular approach and is known to mitigate the impact of outliers. We bring to light two literatures that have diverged in their ways of designing robust losses: one using M...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	521,599
2410.18533	LOGO -- Long cOntext aliGnment via efficient preference Optimization	Long-context models(LCMs) have shown great potential in processing long input sequences(even more than 100M tokens) conveniently and effectively. With significant progress, recent research has pointed out that LCMs can accurately locate token-level salient information within the context. Yet, the generation performance...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	501,927
2303.14840	On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks	Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data. The respectively used principle of measuring distances provides advantages and drawbacks. These are typically not compared nor discussed in the literature due to a lack of multi-modal datasets. Texture-less regions are problemat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	354,261
2402.03243	PINN-BO: A Black-box Optimization Algorithm using Physics-Informed Neural Networks	Black-box optimization is a powerful approach for discovering global optima in noisy and expensive black-box functions, a problem widely encountered in real-world scenarios. Recently, there has been a growing interest in leveraging domain knowledge to enhance the efficacy of machine learning methods. Partial Differenti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	426,920
2312.04712	Error Discovery by Clustering Influence Embeddings	We present a method for identifying groups of test examples -- slices -- on which a model under-performs, a task now known as slice discovery. We formalize coherence -- a requirement that erroneous predictions, within a slice, should be wrong for the same reason -- as a key property that any slice discovery method shou...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	413,787
2004.12993	DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference	Large-scale pre-trained language models such as BERT have brought significant improvements to NLP applications. However, they are also notorious for being slow in inference, which makes them difficult to deploy in real-time applications. We propose a simple but effective method, DeeBERT, to accelerate BERT inference. O...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	174,412
1910.10666	Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks	This paper proposes a novel family of primal-dual-based distributed algorithms for smooth, convex, multi-agent optimization over networks that uses only gradient information and gossip communications. The algorithms can also employ acceleration on the computation and communications. We provide a unified analysis of the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	150,548
1601.07065	Intelligent Conversational Bot for Massive Online Open Courses (MOOCs)	Massive Online Open Courses (MOOCs) which were introduced in 2008 has since drawn attention around the world for both its advantages as well as criticism on its drawbacks. One of the issues in MOOCs which is the lack of interactivity with the instructor has brought conversational bot into the picture to fill in this ga...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	51,375
2306.03373	CiT-Net: Convolutional Neural Networks Hand in Hand with Vision Transformers for Medical Image Segmentation	The hybrid architecture of convolutional neural networks (CNNs) and Transformer are very popular for medical image segmentation. However, it suffers from two challenges. First, although a CNNs branch can capture the local image features using vanilla convolution, it cannot achieve adaptive feature learning. Second, alt...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	371,293
2410.19492	TRADE: Transfer of Distributions between External Conditions with Normalizing Flows	Modeling distributions that depend on external control parameters is a common scenario in diverse applications like molecular simulations, where system properties like temperature affect molecular configurations. Despite the relevance of these applications, existing solutions are unsatisfactory as they require severely...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	502,338
2004.05286	Recent advances in opinion propagation dynamics: A 2020 Survey	Opinion dynamics have attracted the interest of researchers from different fields. Local interactions among individuals create interesting dynamics for the system as a whole. Such dynamics are important from a variety of perspectives. Group decision making, successful marketing, and constructing networks (in which cons...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	172,145
2107.07412	Assign Hysteresis Parameter For Ericsson BTS Power Saving Algorithm Using Unsupervised Learning	Gaza Strip suffers from a chronic electricity deficit that affects all industries including the telecommunication field, so there is a need to optimize and reduce power consumption of the telecommunication equipment. In this paper we propose a new model that helps GSM radio frequency engineers to choose the optimal val...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	246,411
2005.03333	Causal Paths in Temporal Networks of Face-to-Face Human Interactions	In a temporal network causal paths are characterized by the fact that links from a source to a target must respect the chronological order. In this article we study the causal paths structure in temporal networks of human face to face interactions in different social contexts. In a static network paths are transitive i...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	176,128
1708.09702	Human and Machine Judgements for Russian Semantic Relatedness	Semantic relatedness of terms represents similarity of meaning by a numerical score. On the one hand, humans easily make judgments about semantic relatedness. On the other hand, this kind of information is useful in language processing systems. While semantic relatedness has been extensively studied for English using n...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	79,823
2409.13626	Improved Unet brain tumor image segmentation based on GSConv module and ECA attention mechanism	An improved model of medical image segmentation for brain tumor is discussed, which is a deep learning algorithm based on U-Net architecture. Based on the traditional U-Net, we introduce GSConv module and ECA attention mechanism to improve the performance of the model in medical image segmentation tasks. With these imp...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	490,074
2004.07177	Analysis of Stochastic Gradient Descent in Continuous Time	Stochastic gradient descent is an optimisation method that combines classical gradient descent with random subsampling within the target functional. In this work, we introduce the stochastic gradient process as a continuous-time representation of stochastic gradient descent. The stochastic gradient process is a dynamic...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	172,714
2410.15875	Enabling Asymmetric Knowledge Transfer in Multi-Task Learning with Self-Auxiliaries	Knowledge transfer in multi-task learning is typically viewed as a dichotomy; positive transfer, which improves the performance of all tasks, or negative transfer, which hinders the performance of all tasks. In this paper, we investigate the understudied problem of asymmetric task relationships, where knowledge transfe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	500,770
2110.08847	Provable RL with Exogenous Distractors via Multistep Inverse Dynamics	Many real-world applications of reinforcement learning (RL) require the agent to deal with high-dimensional observations such as those generated from a megapixel camera. Prior work has addressed such problems with representation learning, through which the agent can provably extract endogenous, latent state information...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	261,576
2406.12048	MEDeA: Multi-view Efficient Depth Adjustment	The majority of modern single-view depth estimation methods predict relative depth and thus cannot be directly applied in many real-world scenarios, despite impressive performance in the benchmarks. Moreover, single-view approaches cannot guarantee consistency across a sequence of frames. Consistency is typically addre...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	465,198
2408.01168	Misinforming LLMs: vulnerabilities, challenges and opportunities	Large Language Models (LLMs) have made significant advances in natural language processing, but their underlying mechanisms are often misunderstood. Despite exhibiting coherent answers and apparent reasoning behaviors, LLMs rely on statistical patterns in word embeddings rather than true cognitive processes. This leads...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	478,133
1910.08129	Marpa, A practical general parser: the recognizer	The Marpa recognizer is described. Marpa is a practical and fully implemented algorithm for the recognition, parsing and evaluation of context-free grammars. The Marpa recognizer is the first to unite the improvements to Earley's algorithm found in Joop Leo's 1991 paper to those in Aycock and Horspool's 2002 paper. Mar...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	149,780
2203.09179	Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed	Gaussian process regression underpins countless academic and industrial applications of machine learning and statistics, with maximum likelihood estimation routinely used to select appropriate parameters for the covariance kernel. However, it remains an open problem to establish the circumstances in which maximum likel...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	286,064
2409.00912	Merging Multiple Datasets for Improved Appearance-Based Gaze Estimation	Multiple datasets have been created for training and testing appearance-based gaze estimators. Intuitively, more data should lead to better performance. However, combining datasets to train a single esti-mator rarely improves gaze estimation performance. One reason may be differences in the experimental protocols used ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	485,129
2409.19806	PALM: Few-Shot Prompt Learning for Audio Language Models	Audio-Language Models (ALMs) have recently achieved remarkable success in zero-shot audio recognition tasks, which match features of audio waveforms with class-specific text prompt features, inspired by advancements in Vision-Language Models (VLMs). Given the sensitivity of zero-shot performance to the choice of hand-c...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	492,859
1804.09943	System Description of CITlab's Recognition & Retrieval Engine for ICDAR2017 Competition on Information Extraction in Historical Handwritten Records	We present a recognition and retrieval system for the ICDAR2017 Competition on Information Extraction in Historical Handwritten Records which successfully infers person names and other data from marriage records. The system extracts information from the line images with a high accuracy and outperforms the baseline. The...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	96,068
2210.06417	BiaScope: Visual Unfairness Diagnosis for Graph Embeddings	The issue of bias (i.e., systematic unfairness) in machine learning models has recently attracted the attention of both researchers and practitioners. For the graph mining community in particular, an important goal toward algorithmic fairness is to detect and mitigate bias incorporated into graph embeddings since they ...	true	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	true	323,280
2303.04440	HyT-NAS: Hybrid Transformers Neural Architecture Search for Edge Devices	Vision Transformers have enabled recent attention-based Deep Learning (DL) architectures to achieve remarkable results in Computer Vision (CV) tasks. However, due to the extensive computational resources required, these architectures are rarely implemented on resource-constrained platforms. Current research investigate...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	350,093
1906.05466	Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection	Personal health mention detection deals with predicting whether or not a given sentence is a report of a health condition. Past work mentions errors in this prediction when symptom words, i.e. names of symptoms of interest, are used in a figurative sense. Therefore, we combine a state-of-the-art figurative usage detect...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	135,033
1807.00147	Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria	Though quite challenging, leveraging large-scale unlabeled or partially labeled data in learning systems (e.g., model/classifier training) has attracted increasing attentions due to its fundamental importance. To address this problem, many active learning (AL) methods have been proposed that employ up-to-date detectors...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	101,776
2411.00894	Multiscale texture separation	In this paper, we investigate theoretically the behavior of Meyer's image cartoon + texture decomposition model. Our main results is a new theorem which shows that, by combining the decomposition model and a well chosen Littlewood-Paley filter, it is possible to extract almost perfectly a certain class of textures. Thi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	504,823
2005.11623	RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images	Recent methods for people detection in overhead, fisheye images either use radially-aligned bounding boxes to represent people, assuming people always appear along image radius or require significant pre-/post-processing which radically increases computational complexity. In this work, we develop an end-to-end rotation...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	178,515
1702.02111	Rayleigh Quotient Iteration with a Multigrid in Energy Preconditioner for Massively Parallel Neutron Transport	Three complementary methods have been implemented in the code Denovo that accelerate neutral particle transport calculations with methods that use leadership-class computers fully and effectively: a multigroup block (MG) Krylov solver, a Rayleigh quotient iteration (RQI) eigenvalue solver, and a multigrid in energy pre...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	67,927
2303.02722	Performance of OTFS-NOMA Scheme for Coordinated Direct and Relay Transmission Networks in High-Mobility Scenarios	In this letter, an orthogonal time frequency space (OTFS) based non-orthogonal multiple access (NOMA) scheme is investigated for the coordinated direct and relay transmission system, where a source directly communicates with a near user with high mobile speed, and it needs the relaying assistance to serve the far user ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	349,463
2402.09476	AI-Enabled Lung Cancer Prognosis	Lung cancer is the primary cause of cancer-related mortality, claiming approximately 1.79 million lives globally in 2020, with an estimated 2.21 million new cases diagnosed within the same period. Among these, Non-Small Cell Lung Cancer (NSCLC) is the predominant subtype, characterized by a notably bleak prognosis and ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	429,544
2103.15368	Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton	We propose a deep learning system for attention-guided dual-layer image compression (AGDL). In the AGDL compression system, an image is encoded into two layers, a base layer and an attention-guided refinement layer. Unlike the existing ROI image compression methods that spend an extra bit budget equally on all pixels i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	227,180
1809.09735	Bayesian Persuasive Driving	In the autonomous driving area, interaction between vehicles is still a piece of puzzle which has not been fully resolved. The ability to intelligently and safely interact with other vehicles can not only improve self driving quality but also be beneficial to the global driving environment. In this paper, a Bayesian pe...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	108,759
2104.09940	Active and sparse methods in smoothed model checking	Smoothed model checking based on Gaussian process classification provides a powerful approach for statistical model checking of parametric continuous time Markov chain models. The method constructs a model for the functional dependence of satisfaction probability on the Markov chain parameters. This is done via Gaussia...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	231,409
1906.11385	A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree	Decision Tree is a classic formulation of active learning: given $n$ hypotheses with nonnegative weights summing to 1 and a set of tests that each partition the hypotheses, output a decision tree using the provided tests that uniquely identifies each hypothesis and has minimum (weighted) average depth. Previous works s...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	136,652
2006.13198	Spectral Bias and Task-Model Alignment Explain Generalization in Kernel Regression and Infinitely Wide Neural Networks	Generalization beyond a training dataset is a main goal of machine learning, but theoretical understanding of generalization remains an open problem for many models. The need for a new theory is exacerbated by recent observations in deep neural networks where overparameterization leads to better performance, contradict...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,826
2106.09700	Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study	Biomedical knowledge graphs (KGs) hold rich information on entities such as diseases, drugs, and genes. Predicting missing links in these graphs can boost many important applications, such as drug design and repurposing. Recent work has shown that general-domain language models (LMs) can serve as "soft" KGs, and that t...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	241,760
2105.02504	Minimizing costs of communication with random constant weight codes	We present a framework for minimizing costs in constant weight codes while maintaining a certain amount of differentiable codewords. Our calculations are based on a combinatorial view of constant weight codes and relay on simple approximations.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	233,840
2004.07941	Continual Learning for Anomaly Detection in Surveillance Videos	Anomaly detection in surveillance videos has been recently gaining attention. A challenging aspect of high-dimensional applications such as video surveillance is continual learning. While current state-of-the-art deep learning approaches perform well on existing public datasets, they fail to work in a continual learnin...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	172,911
1712.01641	Fully Convolutional Measurement Network for Compressive Sensing Image Reconstruction	Recently, deep learning methods have made a significant improvement in compressive sensing image reconstruction task. In the existing methods, the scene is measured block by block due to the high computational complexity. This results in block-effect of the recovered images. In this paper, we propose a fully convolutio...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	86,135
1405.0616	Automated Attribution and Intertextual Analysis	In this work, we employ quantitative methods from the realm of statistics and machine learning to develop novel methodologies for author attribution and textual analysis. In particular, we develop techniques and software suitable for applications to Classical study, and we illustrate the efficacy of our approach in sev...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	32,785
1306.3517	Different Approaches to Community Evolution Prediction in Blogosphere	Predicting the future direction of community evolution is a problem with high theoretical and practical significance. It allows to determine which characteristics describing communities have importance from the point of view of their future behaviour. Knowledge about the probable future career of the community aids in ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	25,211
2112.02459	SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction	Pedestrian trajectory prediction is an important technique of autonomous driving, which has become a research hot-spot in recent years. Previous methods mainly rely on the position relationship of pedestrians to model social interaction, which is obviously not enough to represent the complex cases in real situations. I...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	269,849
1611.02683	Unsupervised Pretraining for Sequence to Sequence Learning	This work presents a general unsupervised learning method to improve the accuracy of sequence to sequence (seq2seq) models. In our method, the weights of the encoder and decoder of a seq2seq model are initialized with the pretrained weights of two language models and then fine-tuned with labeled data. We apply this met...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	63,595
1507.08559	CRISNER: A Practically Efficient Reasoner for Qualitative Preferences	We present CRISNER (Conditional & Relative Importance Statement Network PrEference Reasoner), a tool that provides practically efficient as well as exact reasoning about qualitative preferences in popular ceteris paribus preference languages such as CP-nets, TCP-nets, CP-theories, etc. The tool uses a model checking en...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	45,579
2410.05262	TurtleBench: Evaluating Top Language Models via Real-World Yes/No Puzzles	As the application of Large Language Models (LLMs) expands, the demand for reliable evaluations increases. Existing LLM evaluation benchmarks primarily rely on static datasets, making it challenging to assess model performance in dynamic interactions with users. Moreover, these benchmarks often depend on specific backg...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	495,639
2312.07062	ThinkBot: Embodied Instruction Following with Thought Chain Reasoning	Embodied Instruction Following (EIF) requires agents to complete human instruction by interacting objects in complicated surrounding environments. Conventional methods directly consider the sparse human instruction to generate action plans for agents, which usually fail to achieve human goals because of the instruction...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	414,784
1902.03471	Depth-Map Generation using Pixel Matching in Stereoscopic Pair of Images	Modern day multimedia content generation and dissemination is moving towards the presentation of more and more `realistic' scenarios. The switch from 2-dimensional (2D) to 3-dimensional (3D) has been a major driving force in that direction. Over the recent past, a large number of approaches have been proposed for creat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	121,115
2103.02866	IACN: Influence-aware and Attention-based Co-evolutionary Network for Recommendation	Recommending relevant items to users is a crucial task on online communities such as Reddit and Twitter. For recommendation system, representation learning presents a powerful technique that learns embeddings to represent user behaviors and capture item properties. However, learning embeddings on online communities is ...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	223,091
2203.05201	Online Deep Metric Learning via Mutual Distillation	Deep metric learning aims to transform input data into an embedding space, where similar samples are close while dissimilar samples are far apart from each other. In practice, samples of new categories arrive incrementally, which requires the periodical augmentation of the learned model. The fine-tuning on the new cate...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	284,744
1911.11312	Spatial-Aware GAN for Unsupervised Person Re-identification	The recent person re-identification research has achieved great success by learning from a large number of labeled person images. On the other hand, the learned models often experience significant performance drops when applied to images collected in a different environment. Unsupervised domain adaptation (UDA) has bee...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	155,078
2304.06991	WYTIWYR: A User Intent-Aware Framework with Multi-modal Inputs for Visualization Retrieval	Retrieving charts from a large corpus is a fundamental task that can benefit numerous applications such as visualization recommendations.The retrieved results are expected to conform to both explicit visual attributes (e.g., chart type, colormap) and implicit user intents (e.g., design style, context information) that ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	358,188
1412.2309	Visual Causal Feature Learning	We provide a rigorous definition of the visual cause of a behavior that is broadly applicable to the visually driven behavior in humans, animals, neurons, robots and other perceiving systems. Our framework generalizes standard accounts of causal learning to settings in which the causal variables need to be constructed ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	38,190
2408.06804	Deep Learning for Speaker Identification: Architectural Insights from AB-1 Corpus Analysis and Performance Evaluation	In the fields of security systems, forensic investigations, and personalized services, the importance of speech as a fundamental human input outweighs text-based interactions. This research delves deeply into the complex field of Speaker Identification (SID), examining its essential components and emphasising Mel Spect...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	480,352
1912.04251	Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from Multi-Vendor X-ray Scans	In the last two decades, luggage scanning has globally become one of the prime aviation security concerns. Manual screening of the baggage items is a cumbersome, subjective and inefficient process. Hence, many researchers have developed Xray imagery-based autonomous systems to address these shortcomings. However, to th...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	156,800
2310.02367	Linear Recurrent Units for Sequential Recommendation	State-of-the-art sequential recommendation relies heavily on self-attention-based recommender models. Yet such models are computationally expensive and often too slow for real-time recommendation. Furthermore, the self-attention operation is performed at a sequence-level, thereby making low-cost incremental inference c...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	396,816
2211.06761	Few-Shot Learning for Biometric Verification	In machine learning applications, it is common practice to feed as much information as possible. In most cases, the model can handle large data sets that allow to predict more accurately. In the presence of data scarcity, a Few-Shot learning (FSL) approach aims to build more accurate algorithms with limited training da...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	330,013
0905.2423	Bounds on sets with few distances	We derive a new estimate of the size of finite sets of points in metric spaces with few distances. The following applications are considered: (1) we improve the Ray-Chaudhuri--Wilson bound of the size of uniform intersecting families of subsets; (2) we refine the bound of Delsarte-Goethals-Seidel on the maximum siz...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,692
1708.09204	Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching	Leveraging on the recent developments in convolutional neural networks (CNNs), matching dense correspondence from a stereo pair has been cast as a learning problem, with performance exceeding traditional approaches. However, it remains challenging to generate high-quality disparities for the inherently ill-posed region...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	79,757
2410.23978	GAMap: Zero-Shot Object Goal Navigation with Multi-Scale Geometric-Affordance Guidance	Zero-Shot Object Goal Navigation (ZS-OGN) enables robots or agents to navigate toward objects of unseen categories without object-specific training. Traditional approaches often leverage categorical semantic information for navigation guidance, which struggles when only objects are partially observed or detailed and fu...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	504,275
2002.02418	The utility of tactile force to autonomous learning of in-hand manipulation is task-dependent	Tactile sensors provide information that can be used to learn and execute manipulation tasks. Different tasks, however, might require different levels of sensory information; which in turn likely affect learning rates and performance. This paper evaluates the role of tactile information on autonomous learning of manipu...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	162,916
2002.08729	Bimodal Distribution Removal and Genetic Algorithm in Neural Network for Breast Cancer Diagnosis	Diagnosis of breast cancer has been well studied in the past. Multiple linear programming models have been devised to approximate the relationship between cell features and tumour malignancy. However, these models are less capable in handling non-linear correlations. Neural networks instead are powerful in processing c...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	164,849
2305.01492	An Adaptive Behaviour-Based Strategy for SARs interacting with Older Adults with MCI during a Serious Game Scenario	The monotonous nature of repetitive cognitive training may cause losing interest in it and dropping out by older adults. This study introduces an adaptive technique that enables a Socially Assistive Robot (SAR) to select the most appropriate actions to maintain the engagement level of older adults while they play the s...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	361,688
1506.07615	Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis	Subspace recovery from corrupted and missing data is crucial for various applications in signal processing and information theory. To complete missing values and detect column corruptions, existing robust Matrix Completion (MC) methods mostly concentrate on recovering a low-rank matrix from few corrupted coefficients w...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	44,541
2103.02521	On the role of depth predictions for 3D human pose estimation	Following the successful application of deep convolutional neural networks to 2d human pose estimation, the next logical problem to solve is 3d human pose estimation from monocular images. While previous solutions have shown some success, they do not fully utilize the depth information from the 2d inputs. With the goal...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	222,988
2501.11903	Finding the nearest bounded-real port-Hamiltonian system	In this paper, we consider linear time-invariant continuous control systems which are bounded real, also known as scattering passive. Our main theoretical contribution is to show the equivalence between such systems and port-Hamiltonian (PH) systems whose factors satisfy certain linear matrix inequalities. Based on thi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	526,088
2412.07797	Mogo: RQ Hierarchical Causal Transformer for High-Quality 3D Human Motion Generation	In the field of text-to-motion generation, Bert-type Masked Models (MoMask, MMM) currently produce higher-quality outputs compared to GPT-type autoregressive models (T2M-GPT). However, these Bert-type models often lack the streaming output capability required for applications in video game and multimedia environments, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	515,814
1311.2272	From average case complexity to improper learning complexity	The basic problem in the PAC model of computational learning theory is to determine which hypothesis classes are efficiently learnable. There is presently a dearth of results showing hardness of learning problems. Moreover, the existing lower bounds fall short of the best known algorithms. The biggest challenge in pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	28,303
2010.02557	PolicyQA: A Reading Comprehension Dataset for Privacy Policies	Privacy policy documents are long and verbose. A question answering (QA) system can assist users in finding the information that is relevant and important to them. Prior studies in this domain frame the QA task as retrieving the most relevant text segment or a list of sentences from the policy document given a question...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	199,077
1804.08330	Energy Efficiency of Rate-Splitting Multiple Access, and Performance Benefits over SDMA and NOMA	Rate-Splitting Multiple Access (RSMA) is a general and powerful multiple access framework for downlink multi-antenna systems, and contains Space-Division Multiple Access (SDMA) and Non-Orthogonal Multiple Access (NOMA) as special cases. RSMA relies on linearly precoded rate-splitting with Successive Interference Cancel...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	95,746
2302.06873	Lero: A Learning-to-Rank Query Optimizer	A recent line of works apply machine learning techniques to assist or rebuild cost-based query optimizers in DBMS. While exhibiting superiority in some benchmarks, their deficiencies, e.g., unstable performance, high training cost, and slow model updating, stem from the inherent hardness of predicting the cost or laten...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	345,561
1804.09466	Zigzag Learning for Weakly Supervised Object Detection	This paper addresses weakly supervised object detection with only image-level supervision at training stage. Previous approaches train detection models with entire images all at once, making the models prone to being trapped in sub-optimums due to the introduced false positive examples. Unlike them, we propose a zigzag...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	95,978
2412.09043	DrivingRecon: Large 4D Gaussian Reconstruction Model For Autonomous Driving	Photorealistic 4D reconstruction of street scenes is essential for developing real-world simulators in autonomous driving. However, most existing methods perform this task offline and rely on time-consuming iterative processes, limiting their practical applications. To this end, we introduce the Large 4D Gaussian Recon...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	516,336
2502.01232	Efficient rule induction by ignoring pointless rules	The goal of inductive logic programming (ILP) is to find a set of logical rules that generalises training examples and background knowledge. We introduce an ILP approach that identifies pointless rules. A rule is pointless if it contains a redundant literal or cannot discriminate against negative examples. We show that...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	529,763
2405.06880	EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation	An efficient and effective decoding mechanism is crucial in medical image segmentation, especially in scenarios with limited computational resources. However, these decoding mechanisms usually come with high computational costs. To address this concern, we introduce EMCAD, a new efficient multi-scale convolutional atte...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	453,485
1904.00388	Multi-vision Attention Networks for On-line Red Jujube Grading	To solve the red jujube classification problem, this paper designs a convolutional neural network model with low computational cost and high classification accuracy. The architecture of the model is inspired by the multi-visual mechanism of the organism and DenseNet. To further improve our model, we add the attention m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	125,869
2406.08455	AToM-Bot: Embodied Fulfillment of Unspoken Human Needs with Affective Theory of Mind	We propose AToM-Bot, a novel task generation and execution framework for proactive robot-human interaction, which leverages the human mental and physical state inference capabilities of the Vision Language Model (VLM) prompted by the Affective Theory of Mind (AToM). Without requiring explicit commands by humans, AToM-B...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	463,495
1401.3459	Generic Preferences over Subsets of Structured Objects	Various tasks in decision making and decision support systems require selecting a preferred subset of a given set of items. Here we focus on problems where the individual items are described using a set of characterizing attributes, and a generic preference specification is required, that is, a specification that can w...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	29,865
2411.01014	Mixed Reality Teleoperation Assistance for Direct Control of Humanoids	Teleoperation plays a crucial role in enabling robot operations in challenging environments, yet existing limitations in effectiveness and accuracy necessitate the development of innovative strategies for improving teleoperated tasks. This article introduces a novel approach that utilizes mixed reality and assistive au...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	504,874
2006.06051	Learning to Incentivize Other Learning Agents	The challenge of developing powerful and general Reinforcement Learning (RL) agents has received increasing attention in recent years. Much of this effort has focused on the single-agent setting, in which an agent maximizes a predefined extrinsic reward function. However, a long-term question inevitably arises: how wil...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	true	181,290
2101.01571	Exact solution to the random sequential dynamics of a message passing algorithm	We analyze the random sequential dynamics of a message passing algorithm for Ising models with random interactions in the large system limit. We derive exact results for the two-time correlation functions and the speed of convergence. The {\em de Almedia-Thouless} stability criterion of the static problem is found to b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	214,398
2411.16679	Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts?	We evaluate how well Large Language Models (LLMs) latently recall and compose facts to answer multi-hop queries like "In the year Scarlett Johansson was born, the Summer Olympics were hosted in the country of". One major challenge in evaluating this ability is that LLMs may have developed shortcuts by encounters of the...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	511,112
2405.08277	AI-driven, Model-Free Current Control: A Deep Symbolic Approach for Optimal Induction Machine Performance	This paper proposed a straightforward and efficient current control solution for induction machines employing deep symbolic regression (DSR). The proposed DSR-based control design offers a simple yet highly effective approach by creating an optimal control model through training and fitting, resulting in an analytical ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	454,041
1911.12650	Multiple quadrotors carrying a flexible hose: dynamics, differential flatness and control	Using quadrotors UAVs for cooperative payload transportation using cables has been actively gaining interest in recent years. Understanding the dynamics of these complex multi-agent systems would help towards designing safe and reliable systems. In this work, we study one such multi-agent system comprising of multiple ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	155,460

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