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
2109.04865	Emerging AI Security Threats for Autonomous Cars -- Case Studies	Artificial Intelligence has made a significant contribution to autonomous vehicles, from object detection to path planning. However, AI models require a large amount of sensitive training data and are usually computationally intensive to build. The commercial value of such models motivates attackers to mount various at...	false	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false	false	false	254,568
2312.08019	AdapEdit: Spatio-Temporal Guided Adaptive Editing Algorithm for Text-Based Continuity-Sensitive Image Editing	With the great success of text-conditioned diffusion models in creative text-to-image generation, various text-driven image editing approaches have attracted the attentions of many researchers. However, previous works mainly focus on discreteness-sensitive instructions such as adding, removing or replacing specific obj...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	415,159
1905.13168	Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes	In the analysis of sequential data, the detection of abrupt changes is important in predicting future changes. In this paper, we propose statistical hypothesis tests for detecting covariance structure changes in locally smooth time series modeled by Gaussian Processes (GPs). We provide theoretically justified threshold...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,018
1803.04276	Angle-based Shape Determination Theory of Planar Graphs with Application to Formation Stabilization	This paper presents an angle-based approach for distributed formation shape stabilization of multi-agent systems in the plane. We develop an angle rigidity theory to study whether a planar framework can be determined by angles between segments uniquely up to translations, rotations, scalings and reflections. The propos...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	92,424
1901.09566	Physical-Layer Supervised Learning Assisted by an Entangled Sensor Network	Many existing quantum supervised learning (SL) schemes consider data given a priori in a classical description. With only noisy intermediate-scale quantum (NISQ) devices available in the near future, their quantum speedup awaits the development of quantum random access memories (qRAMs) and fault-tolerant quantum comput...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	119,786
2305.05524	A Lower and Upper Bound on the Epsilon-Uniform Common Randomness Capacity	We consider a standard two-source model for uniform common randomness (UCR) generation, in which Alice and Bob observe independent and identically distributed (i.i.d.) samples of a correlated finite source and where Alice is allowed to send information to Bob over an arbitrary single-user channel. We study the \(\bolds...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	363,184
2204.08917	Global-and-Local Collaborative Learning for Co-Salient Object Detection	The goal of co-salient object detection (CoSOD) is to discover salient objects that commonly appear in a query group containing two or more relevant images. Therefore, how to effectively extract inter-image correspondence is crucial for the CoSOD task. In this paper, we propose a global-and-local collaborative learning...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	292,254
1602.03742	HMM and DTW for evaluation of therapeutical gestures using kinect	Automatic recognition of the quality of movement in human beings is a challenging task, given the difficulty both in defining the constraints that make a movement correct, and the difficulty in using noisy data to determine if these constraints were satisfied. This paper presents a method for the detection of deviation...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	52,047
2008.00539	An Investigation in Optimal Encoding of Protein Primary Sequence for Structure Prediction by Artificial Neural Networks	Machine learning and the use of neural networks has increased precipitously over the past few years primarily due to the ever-increasing accessibility to data and the growth of computation power. It has become increasingly easy to harness the power of machine learning for predictive tasks. Protein structure prediction ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	190,039
2003.06555	Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation	Adversarial training is promising for improving robustness of deep neural networks towards adversarial perturbations, especially on the classification task. The effect of this type of training on semantic segmentation, contrarily, just commences. We make the initial attempt to explore the defense strategy on semantic s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	168,151
2206.11140	Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries	Subgraph GNNs are a recent class of expressive Graph Neural Networks (GNNs) which model graphs as collections of subgraphs. So far, the design space of possible Subgraph GNN architectures as well as their basic theoretical properties are still largely unexplored. In this paper, we study the most prominent form of subgr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	304,172
2401.08165	Near-Far Field Codebook Design for IOS-Aided Multi-User Communications	Recently, the rapid development of metasurface facilitates the growth of extremely large-scale antenna arrays, making the ultra-massive MIMO possible. In this paper, we study the codebook design and beam training for an intelligent omni-surface (IOS) aided multi-user system, where the IOS is a novel metasurface enablin...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	421,788
2203.13534	Generalization bounds for learning under graph-dependence: A survey	Traditional statistical learning theory relies on the assumption that data are identically and independently distributed (i.i.d.). However, this assumption often does not hold in many real-life applications. In this survey, we explore learning scenarios where examples are dependent and their dependence relationship is ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	287,669
2309.14653	Joint Design of Source-Channel Codes with Linear Source Encoding Complexity and Good Channel Thresholds Based on Double-Protograph LDPC Codes	We propose the use of a lower or upper triangular sub-base matrix to replace the identity matrix in the source-check-channel-variable linking protomatrix of a double-protograph low-density parity-check joint-source-channel code (DP-LDPC JSCC). The elements along the diagonal of the proposed lower or upper triangular su...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	394,689
1909.06904	Using an AI creativity system to explore how aesthetic experiences are processed along the brains perceptual neural pathways	With the increased sophistication of AI techniques, the application of these systems has been expanding to ever newer fields. Increasingly, these systems are being used in modeling of human aesthetics and creativity, e.g. how humans create artworks and design products. Our lab has developed one such AI creativity deep ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	145,522
1811.11607	On the relation between topological entropy and restoration entropy	In the context of state estimation under communication constraints, several notions of dynamical entropy play a fundamental role, among them: topological entropy and restoration entropy. In this paper, we present a theorem which demonstrates that for most dynamical systems restoration entropy strictly exceeds topologic...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	114,817
2301.07861	Improving Food Detection For Images From a Wearable Egocentric Camera	Diet is an important aspect of our health. Good dietary habits can contribute to the prevention of many diseases and improve the overall quality of life. To better understand the relationship between diet and health, image-based dietary assessment systems have been developed to collect dietary information. We introduce...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	341,030
2401.16719	OptiState: State Estimation of Legged Robots using Gated Networks with Transformer-based Vision and Kalman Filtering	State estimation for legged robots is challenging due to their highly dynamic motion and limitations imposed by sensor accuracy. By integrating Kalman filtering, optimization, and learning-based modalities, we propose a hybrid solution that combines proprioception and exteroceptive information for estimating the state ...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	424,950
1806.07441	Wall Stress Estimation of Cerebral Aneurysm based on Zernike Convolutional Neural Networks	Convolutional neural networks (ConvNets) have demonstrated an exceptional capacity to discern visual patterns from digital images and signals. Unfortunately, such powerful ConvNets do not generalize well to arbitrary-shaped manifolds, where data representation does not fit into a tensor-like grid. Hence, many fields of...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	100,927
2401.02086	View-based Explanations for Graph Neural Networks	Generating explanations for graph neural networks (GNNs) has been studied to understand their behavior in analytical tasks such as graph classification. Existing approaches aim to understand the overall results of GNNs rather than providing explanations for specific class labels of interest, and may return explanation ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	419,594
cs/0702025	Algebraic Signal Processing Theory: Cooley-Tukey Type Algorithms for DCTs and DSTs	This paper presents a systematic methodology based on the algebraic theory of signal processing to classify and derive fast algorithms for linear transforms. Instead of manipulating the entries of transform matrices, our approach derives the algorithms by stepwise decomposition of the associated signal models, or polyn...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	540,135
2110.10441	Feedback Linearization of Car Dynamics for Racing via Reinforcement Learning	Through the method of Learning Feedback Linearization, we seek to learn a linearizing controller to simplify the process of controlling a car to race autonomously. A soft actor-critic approach is used to learn a decoupling matrix and drift vector that effectively correct for errors in a hand-designed linearizing contro...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	262,160
2403.11679	NEDS-SLAM: A Neural Explicit Dense Semantic SLAM Framework using 3D Gaussian Splatting	We propose NEDS-SLAM, a dense semantic SLAM system based on 3D Gaussian representation, that enables robust 3D semantic mapping, accurate camera tracking, and high-quality rendering in real-time. In the system, we propose a Spatially Consistent Feature Fusion model to reduce the effect of erroneous estimates from pre-t...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	438,810
2502.05842	A Grid-Forming HVDC Series Tapping Converter Using Extended Techniques of Flex-LCC	This paper discusses an extension technology for the previously proposed Flexible Line-Commutated Converter (Flex LCC) [1]. The proposed extension involves modifying the arm internal-electromotive-force control, redesigning the main-circuit parameters, and integrating a low-power coordination strategy. As a result, the...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	531,801
1911.08135	Investigating the relationship between graph eigenvector ordering and the signal processing dual	Graph signal processing uses the graph eigenvector basis to analyze signals. However, these graph eigenvectors are typically linearly ordered (by total variation), which may not be reasonable for many graph structures. There have been structure based similarity metrics proposed in the literature that better capture the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	154,103
2006.06755	Conditional Sampling with Monotone GANs: from Generative Models to Likelihood-Free Inference	We present a novel framework for conditional sampling of probability measures, using block triangular transport maps. We develop the theoretical foundations of block triangular transport in a Banach space setting, establishing general conditions under which conditional sampling can be achieved and drawing connections b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	181,541
2112.06999	Designing weighted and multiplex networks for deep learning user geolocation in Twitter	Predicting the geographical location of users of social media like Twitter has found several applications in health surveillance, emergency monitoring, content personalization, and social studies in general. In this work we contribute to the research in this area by designing and evaluating new methods based on the lit...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	271,344
2404.12347	AniClipart: Clipart Animation with Text-to-Video Priors	Clipart, a pre-made art form, offers a convenient and efficient way of creating visual content. However, traditional workflows for animating static clipart are laborious and time-consuming, involving steps like rigging, keyframing, and inbetweening. Recent advancements in text-to-video generation hold great potential i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	447,842
2210.02672	A Novel Maximum-Entropy-Driven Technique for Low-Rank Orthogonal Nonnegative Matrix Factorization with $\ell_0$-Norm sparsity Constraint	In data-driven control and machine learning, a common requirement involves breaking down large matrices into smaller, low-rank factors that possess specific levels of sparsity. This paper introduces an innovative solution to the orthogonal nonnegative matrix factorization (ONMF) problem. The objective is to approximate...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	321,739
2408.07419	Unsupervised Stereo Matching Network For VHR Remote Sensing Images Based On Error Prediction	Stereo matching in remote sensing has recently garnered increased attention, primarily focusing on supervised learning. However, datasets with ground truth generated by expensive airbone Lidar exhibit limited quantity and diversity, constraining the effectiveness of supervised networks. In contrast, unsupervised learni...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	480,575
2009.00867	Tree Automata for Extracting Consensus from Partial Replicas of a Structured Document	In an asynchronous cooperative editing workflow of a structured document, each of the co-authors receives in the different phases of the editing process, a copy of the document to insert its contribution. For confidentiality reasons, this copy may be only a partial replica containing only parts of the (global) document...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	194,164
1506.05870	To Know Where We Are: Vision-Based Positioning in Outdoor Environments	Augmented reality (AR) displays become more and more popular recently, because of its high intuitiveness for humans and high-quality head-mounted display have rapidly developed. To achieve such displays with augmented information, highly accurate image registration or ego-positioning are required, but little attention ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	44,344
1906.00735	Achieving Generalizable Robustness of Deep Neural Networks by Stability Training	We study the recently introduced stability training as a general-purpose method to increase the robustness of deep neural networks against input perturbations. In particular, we explore its use as an alternative to data augmentation and validate its performance against a number of distortion types and transformations i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,496
2010.01677	Local Additivity Based Data Augmentation for Semi-supervised NER	Named Entity Recognition (NER) is one of the first stages in deep language understanding yet current NER models heavily rely on human-annotated data. In this work, to alleviate the dependence on labeled data, we propose a Local Additivity based Data Augmentation (LADA) method for semi-supervised NER, in which we create...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	198,739
2410.21987	Node Regression on Latent Position Random Graphs via Local Averaging	Node regression consists in predicting the value of a graph label at a node, given observations at the other nodes. To gain some insight into the performance of various estimators for this task, we perform a theoretical study in a context where the graph is random. Specifically, we assume that the graph is generated by...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	503,459
1910.13018	Predicting Louisiana Public High School Dropout through Imbalanced Learning Techniques	This study is motivated by the magnitude of the problem of Louisiana high school dropout and its negative impacts on individual and public well-being. Our goal is to predict students who are at risk of high school dropout, by examining Louisiana administrative dataset. Due to the imbalanced nature of the dataset, imbal...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	151,265
2112.07974	Detail-aware Deep Clothing Animations Infused with Multi-source Attributes	This paper presents a novel learning-based clothing deformation method to generate rich and reasonable detailed deformations for garments worn by bodies of various shapes in various animations. In contrast to existing learning-based methods, which require numerous trained models for different garment topologies or pose...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	271,659
2107.11643	An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products	Defects are unavoidable in casting production owing to the complexity of the casting process. While conventional human-visual inspection of casting products is slow and unproductive in mass productions, an automatic and reliable defect detection not just enhances the quality control process but positively improves prod...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	247,647
2411.05508	An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking	Recent advances have demonstrated that large language models (LLMs) excel as listwise rerankers, but their high computational demands remain a barrier to widespread adoption. Further, the traditional language modeling (LM) objective is not ideally suited for reranking tasks. FIRST is a novel approach that addresses the...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	506,693
2108.02892	Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications	In this paper, we propose a deep reinforcement learning (DRL) approach for solving the optimisation problem of the network's sum-rate in device-to-device (D2D) communications supported by an intelligent reflecting surface (IRS). The IRS is deployed to mitigate the interference and enhance the signal between the D2D tra...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	249,486
2409.11128	Genetic Information Analysis of Age-Related Macular Degeneration Fellow Eye Using Multi-Modal Selective ViT	In recent years, there has been significant development in the analysis of medical data using machine learning. It is believed that the onset of Age-related Macular Degeneration (AMD) is associated with genetic polymorphisms. However, genetic analysis is costly, and artificial intelligence may offer assistance. This pa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	489,018
2112.12021	Community Detection in Medical Image Datasets: Using Wavelets and Spectral Methods	Medical image datasets can have large number of images representing patients with different health conditions and various disease severity. When dealing with raw unlabeled image datasets, the large number of samples often makes it hard for experts and non-experts to understand the variety of images present in a dataset...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	272,855
1509.01186	Model Based Reinforcement Learning with Final Time Horizon Optimization	We present one of the first algorithms on model based reinforcement learning and trajectory optimization with free final time horizon. Grounded on the optimal control theory and Dynamic Programming, we derive a set of backward differential equations that propagate the value function and provide the optimal control poli...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	46,576
2210.04157	The Role of Coverage in Online Reinforcement Learning	Coverage conditions -- which assert that the data logging distribution adequately covers the state space -- play a fundamental role in determining the sample complexity of offline reinforcement learning. While such conditions might seem irrelevant to online reinforcement learning at first glance, we establish a new con...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	322,332
1804.08000	Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds	Fine-grained entity typing is the task of assigning fine-grained semantic types to entity mentions. We propose a neural architecture which learns a distributional semantic representation that leverages a greater amount of semantic context -- both document and sentence level information -- than prior work. We find that ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	95,660
1909.09534	Creative GANs for generating poems, lyrics, and metaphors	Generative models for text have substantially contributed to tasks like machine translation and language modeling, using maximum likelihood optimization (MLE). However, for creative text generation, where multiple outputs are possible and originality and uniqueness are encouraged, MLE falls short. Methods optimized for...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	146,284
2401.05425	An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection	Epilepsy is one of the most common neurological diseases globally (around 50 million people worldwide). Fortunately, up to 70% of people with epilepsy could live seizure-free if properly diagnosed and treated, and a reliable technique to monitor the onset of seizures could improve the quality of life of patients who ar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	420,779
2501.10107	BBPOS: BERT-based Part-of-Speech Tagging for Uzbek	This paper advances NLP research for the low-resource Uzbek language by evaluating two previously untested monolingual Uzbek BERT models on the part-of-speech (POS) tagging task and introducing the first publicly available UPOS-tagged benchmark dataset for Uzbek. Our fine-tuned models achieve 91% average accuracy, outp...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	525,395
2112.00499	Structure-Aware Label Smoothing for Graph Neural Networks	Representing a label distribution as a one-hot vector is a common practice in training node classification models. However, the one-hot representation may not adequately reflect the semantic characteristics of a node in different classes, as some nodes may be semantically close to their neighbors in other classes. It w...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	269,156
2108.08730	Accurate 3D frequency-domain seismic wave modeling with the wavelength-adaptive 27-point finite-difference stencil: a tool for full waveform inversion	Efficient frequency-domain Full Waveform Inversion (FWI) of long-offset/wide-azimuth node data can be designed with a few discrete frequencies. However, 3D frequency-domain seismic modeling remains challenging since it requires solving a large and sparse linear indefinite system per frequency. When such systems are sol...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	251,362
2406.01028	LLEMamba: Low-Light Enhancement via Relighting-Guided Mamba with Deep Unfolding Network	Transformer-based low-light enhancement methods have yielded promising performance by effectively capturing long-range dependencies in a global context. However, their elevated computational demand limits the scalability of multiple iterations in deep unfolding networks, and hence they have difficulty in flexibly balan...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	460,133
1903.11257	How Can We Be So Dense? The Benefits of Using Highly Sparse Representations	Most artificial networks today rely on dense representations, whereas biological networks rely on sparse representations. In this paper we show how sparse representations can be more robust to noise and interference, as long as the underlying dimensionality is sufficiently high. A key intuition that we develop is that ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	125,468
2407.12474	Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detection	Unsupervised Anomaly Detection (UAD) methods rely on healthy data distributions to identify anomalies as outliers. In brain MRI, a common approach is reconstruction-based UAD, where generative models reconstruct healthy brain MRIs, and anomalies are detected as deviations between input and reconstruction. However, this...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	473,945
2404.13646	Physics-informed Discretization-independent Deep Compositional Operator Network	Solving parametric Partial Differential Equations (PDEs) for a broad range of parameters is a critical challenge in scientific computing. To this end, neural operators, which \textcolor{black}{predicts the PDE solution with variable PDE parameter inputs}, have been successfully used. However, the training of neural ope...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	448,378
2403.14584	Dynamical importance and network perturbations	The leading eigenvalue $\lambda$ of the adjacency matrix of a graph exerts much influence on the behavior of dynamical processes on that graph. It is thus relevant to relate notions of the importance (specifically, centrality measures) of network structures to $\lambda$ and its associated eigenvector. We study a previo...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	440,139
2206.08264	Towards the Generation of Musical Explanations with GPT-3	Open AI's language model, GPT-3, has shown great potential for many NLP tasks, with applications in many different domains. In this work we carry out a first study on GPT-3's capability to communicate musical decisions through textual explanations when prompted with a textual representation of a piece of music. Enablin...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	303,060
2005.12129	Factor Analysis of Mixed Data for Anomaly Detection	Anomaly detection aims to identify observations that deviate from the typical pattern of data. Anomalous observations may correspond to financial fraud, health risks, or incorrectly measured data in practice. We show detecting anomalies in high-dimensional mixed data is enhanced through first embedding the data then as...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	178,648
1908.00801	Space-adaptive anisotropic bivariate Laplacian regularization for image restoration	In this paper we present a new regularization term for variational image restoration which can be regarded as a space-variant anisotropic extension of the classical isotropic Total Variation (TV) regularizer. The proposed regularizer comes from the statistical assumption that the gradients of the target image distribut...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	140,605
1101.5463	Walking on a Graph with a Magnifying Glass: Stratified Sampling via Weighted Random Walks	Our objective is to sample the node set of a large unknown graph via crawling, to accurately estimate a given metric of interest. We design a random walk on an appropriately defined weighted graph that achieves high efficiency by preferentially crawling those nodes and edges that convey greater information regarding th...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	8,947
2211.14858	"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanations	Counterfactual explanations are a popular type of explanation for making the outcomes of a decision making system transparent to the user. Counterfactual explanations tell the user what to do in order to change the outcome of the system in a desirable way. However, it was recently discovered that the recommendations of...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	333,024
2309.12251	Planning Optimal Trajectories for Mobile Manipulators under End-effector Trajectory Continuity Constraint	Mobile manipulators have been employed in many applications that are traditionally performed by either multiple fixed-base robots or a large robotic system. This capability is enabled by the mobility of the mobile base. However, the mobile base also brings redundancy to the system, which makes mobile manipulator motion...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	393,719
1301.6689	A Hybrid Anytime Algorithm for the Constructiion of Causal Models From Sparse Data	We present a hybrid constraint-based/Bayesian algorithm for learning causal networks in the presence of sparse data. The algorithm searches the space of equivalence classes of models (essential graphs) using a heuristic based on conventional constraint-based techniques. Each essential graph is then converted into a dir...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	21,483
1910.11005	Wasserstein distances for evaluating cross-lingual embeddings	Word embeddings are high dimensional vector representations of words that capture their semantic similarity in the vector space. There exist several algorithms for learning such embeddings both for a single language as well as for several languages jointly. In this work we propose to evaluate collections of embeddings ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	150,651
2411.11016	Time Step Generating: A Universal Synthesized Deepfake Image Detector	Currently, high-fidelity text-to-image models are developed in an accelerating pace. Among them, Diffusion Models have led to a remarkable improvement in the quality of image generation, making it vary challenging to distinguish between real and synthesized images. It simultaneously raises serious concerns regarding pr...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	508,891
2206.06714	Interpretable Gait Recognition by Granger Causality	Which joint interactions in the human gait cycle can be used as biometric characteristics? Most current methods on gait recognition suffer from the lack of interpretability. We propose an interpretable feature representation of gait sequences by the graphical Granger causal inference. Gait sequence of a person in the s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	302,471
2501.15140	Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language Models	Multi-modal large language models (MLLMs) have shown remarkable abilities in various visual understanding tasks. However, MLLMs still struggle with fine-grained visual recognition (FGVR), which aims to identify subordinate-level categories from images. This can negatively impact more advanced capabilities of MLLMs, suc...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	527,424
1705.05619	Research on Bi-mode Biometrics Based on Deep Learning	In view of the fact that biological characteristics have excellent independent distinguishing characteristics,biometric identification technology involves almost all the relevant areas of human distinction. Fingerprints, iris, face, voice-print and other biological features have been widely used in the public security ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	73,524
1708.01179	Patch-based adaptive weighting with segmentation and scale (PAWSS) for visual tracking	Tracking-by-detection algorithms are widely used for visual tracking, where the problem is treated as a classification task where an object model is updated over time using online learning techniques. In challenging conditions where an object undergoes deformation or scale variations, the update step is prone to includ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	78,346
2205.05507	TextMatcher: Cross-Attentional Neural Network to Compare Image and Text	We study a novel multimodal-learning problem, which we call text matching: given an image containing a single-line text and a candidate text transcription, the goal is to assess whether the text represented in the image corresponds to the candidate text. We devise the first machine-learning model specifically designed ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	295,952
1508.04422	Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural Networks	Several popular graph embedding techniques for representation learning and dimensionality reduction rely on performing computationally expensive eigendecompositions to derive a nonlinear transformation of the input data space. The resulting eigenvectors encode the embedding coordinates for the training samples only, an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	46,129
2502.09775	CellFlow: Simulating Cellular Morphology Changes via Flow Matching	Building a virtual cell capable of accurately simulating cellular behaviors in silico has long been a dream in computational biology. We introduce CellFlow, an image-generative model that simulates cellular morphology changes induced by chemical and genetic perturbations using flow matching. Unlike prior methods, CellF...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	533,590
2311.10104	A Framework of Defining, Modeling, and Analyzing Cognition Mechanisms	Cognition is a core part of and a common topic among philosophy of mind, psychology, neuroscience, AI, and cognitive science. Through a mechanistic lens, I propose a framework of defining, modeling, and analyzing cognition mechanisms. Firstly, appropriate terms are introduced and used in explanations related to the fra...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	408,417
1811.05844	A Learning-Based Framework for Line-Spectra Super-resolution	We propose a learning-based approach for estimating the spectrum of a multisinusoidal signal from a finite number of samples. A neural-network is trained to approximate the spectra of such signals on simulated data. The proposed methodology is very flexible: adapting to different signal and noise models only requires m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	113,398
2308.00773	High-Fidelity Eye Animatable Neural Radiance Fields for Human Face	Face rendering using neural radiance fields (NeRF) is a rapidly developing research area in computer vision. While recent methods primarily focus on controlling facial attributes such as identity and expression, they often overlook the crucial aspect of modeling eyeball rotation, which holds importance for various down...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	383,043
2410.14195	Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysis	Histopathology Whole Slide Image (WSI) analysis serves as the gold standard for clinical cancer diagnosis in the daily routines of doctors. To develop computer-aided diagnosis model for WSIs, previous methods typically employ Multi-Instance Learning to enable slide-level prediction given only slide-level labels. Among ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	499,925
2312.01682	ResEnsemble-DDPM: Residual Denoising Diffusion Probabilistic Models for Ensemble Learning	Nowadays, denoising diffusion probabilistic models have been adapted for many image segmentation tasks. However, existing end-to-end models have already demonstrated remarkable capabilities. Rather than using denoising diffusion probabilistic models alone, integrating the abilities of both denoising diffusion probabili...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	412,548
2108.09649	The Exploitation of Distance Distributions for Clustering	Although distance measures are used in many machine learning algorithms, the literature on the context-independent selection and evaluation of distance measures is limited in the sense that prior knowledge is used. In cluster analysis, current studies evaluate the choice of distance measure after applying unsupervised ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	251,673
1703.04082	Sequential Local Learning for Latent Graphical Models	Learning parameters of latent graphical models (GM) is inherently much harder than that of no-latent ones since the latent variables make the corresponding log-likelihood non-concave. Nevertheless, expectation-maximization schemes are popularly used in practice, but they are typically stuck in local optima. In the rece...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	69,833
2006.11627	Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble	Despite neural networks have achieved prominent performance on many natural language processing (NLP) tasks, they are vulnerable to adversarial examples. In this paper, we propose Dirichlet Neighborhood Ensemble (DNE), a randomized smoothing method for training a robust model to defense substitution-based attacks. Duri...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	183,305
2203.11068	Learning Enriched Illuminants for Cross and Single Sensor Color Constancy	Color constancy aims to restore the constant colors of a scene under different illuminants. However, due to the existence of camera spectral sensitivity, the network trained on a certain sensor, cannot work well on others. Also, since the training datasets are collected in certain environments, the diversity of illumin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	286,785
1911.02851	Dependency and Span, Cross-Style Semantic Role Labeling on PropBank and NomBank	The latest developments in neural semantic role labeling (SRL) have shown great performance improvements with both the dependency and span formalisms/styles. Although the two styles share many similarities in linguistic meaning and computation, most previous studies focus on a single style. In this paper, we define a n...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	152,472
2008.06606	Quantification of BERT Diagnosis Generalizability Across Medical Specialties Using Semantic Dataset Distance	Deep learning models in healthcare may fail to generalize on data from unseen corpora. Additionally, no quantitative metric exists to tell how existing models will perform on new data. Previous studies demonstrated that NLP models of medical notes generalize variably between institutions, but ignored other levels of he...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	191,838
1705.07425	Learning Semantic Relatedness From Human Feedback Using Metric Learning	Assessing the degree of semantic relatedness between words is an important task with a variety of semantic applications, such as ontology learning for the Semantic Web, semantic search or query expansion. To accomplish this in an automated fashion, many relatedness measures have been proposed. However, most of these me...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	73,836
2210.04561	A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning	Visual reinforcement learning (RL), which makes decisions directly from high-dimensional visual inputs, has demonstrated significant potential in various domains. However, deploying visual RL techniques in the real world remains challenging due to their low sample efficiency and large generalization gaps. To tackle the...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	322,505
2307.07409	KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization	In this paper, we introduce CheXOFA, a new pre-trained vision-language model (VLM) for the chest X-ray domain. Our model is initially pre-trained on various multimodal datasets within the general domain before being transferred to the chest X-ray domain. Following a prominent VLM, we unify various domain-specific tasks...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	379,397
2211.14843	Learning Object-Language Alignments for Open-Vocabulary Object Detection	Existing object detection methods are bounded in a fixed-set vocabulary by costly labeled data. When dealing with novel categories, the model has to be retrained with more bounding box annotations. Natural language supervision is an attractive alternative for its annotation-free attributes and broader object concepts. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	333,018
2212.00471	Implicit Mixture of Interpretable Experts for Global and Local Interpretability	We investigate the feasibility of using mixtures of interpretable experts (MoIE) to build interpretable image classifiers on MNIST10. MoIE uses a black-box router to assign each input to one of many inherently interpretable experts, thereby providing insight into why a particular classification decision was made. We fi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	334,066
2010.01590	Deep kernel processes	We define deep kernel processes in which positive definite Gram matrices are progressively transformed by nonlinear kernel functions and by sampling from (inverse) Wishart distributions. Remarkably, we find that deep Gaussian processes (DGPs), Bayesian neural networks (BNNs), infinite BNNs, and infinite BNNs with bottl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	198,707
1902.00658	Polarization and Fluctuations in Signed Social Networks	Much recent research on social networks has focused on the modeling and analysis of how opinions evolve as a function of interpersonal relationships. It is also of great interest to model and understand the implications of friendly and antagonistic relationships. In this paper, we propose a new, simple and intuitive mo...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	120,469
2401.17916	Source-free Domain Adaptive Object Detection in Remote Sensing Images	Recent studies have used unsupervised domain adaptive object detection (UDAOD) methods to bridge the domain gap in remote sensing (RS) images. However, UDAOD methods typically assume that the source domain data can be accessed during the domain adaptation process. This setting is often impractical in the real world due...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	425,377
1902.02176	A Linear-complexity Multi-biometric Forensic Document Analysis System, by Fusing the Stylome and Signature Modalities	Forensic Document Analysis (FDA) addresses the problem of finding the authorship of a given document. Identification of the document writer via a number of its modalities (e.g. handwriting, signature, linguistic writing style (i.e. stylome), etc.) has been studied in the FDA state-of-the-art. But, no research is conduc...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	120,823
1709.05206	LSTM Fully Convolutional Networks for Time Series Classification	Fully convolutional neural networks (FCN) have been shown to achieve state-of-the-art performance on the task of classifying time series sequences. We propose the augmentation of fully convolutional networks with long short term memory recurrent neural network (LSTM RNN) sub-modules for time series classification. Our ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	80,808
2502.14801	AVD2: Accident Video Diffusion for Accident Video Description	Traffic accidents present complex challenges for autonomous driving, often featuring unpredictable scenarios that hinder accurate system interpretation and responses.Nonetheless, prevailing methodologies fall short in elucidating the causes of accidents and proposing preventive measures due to the paucity of training d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	535,986
1810.06999	Efficient Greedy Coordinate Descent for Composite Problems	Coordinate descent with random coordinate selection is the current state of the art for many large scale optimization problems. However, greedy selection of the steepest coordinate on smooth problems can yield convergence rates independent of the dimension $n$, and requiring upto $n$ times fewer iterations. In this p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	110,551
2301.02607	A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising	Objective: Gaussian Processes (GP)-based filters, which have been effectively used for various applications including electrocardiogram (ECG) filtering can be computationally demanding and the choice of their hyperparameters is typically ad hoc. Methods: We develop a data-driven GP filter to address both issues, using ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	339,546
2007.01818	Image-based Vehicle Re-identification Model with Adaptive Attention Modules and Metadata Re-ranking	Vehicle Re-identification is a challenging task due to intra-class variability and inter-class similarity across non-overlapping cameras. To tackle these problems, recently proposed methods require additional annotation to extract more features for false positive image exclusion. In this paper, we propose a model power...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	185,542
2305.08800	Measuring Cross-Lingual Transferability of Multilingual Transformers on Sentence Classification	Recent studies have exhibited remarkable capabilities of pre-trained multilingual Transformers, especially cross-lingual transferability. However, current methods do not measure cross-lingual transferability well, hindering the understanding of multilingual Transformers. In this paper, we propose IGap, a cross-lingual ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	364,411
1902.09134	On the Distribution of GSVD	In this paper, some new results on the distribution of the generalized singular value decomposition (GSVD) are presented.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	122,352
2008.01926	What to Do When You Can't Do It All: Temporal Logic Planning with Soft Temporal Logic Constraints	In this paper, we consider a temporal logic planning problem in which the objective is to find an infinite trajectory that satisfies an optimal selection from a set of soft specifications expressed in linear temporal logic (LTL) while nevertheless satisfying a hard specification expressed in LTL. Our previous work cons...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	true	190,470
1911.13024	Abstract Argumentation and the Rational Man	Abstract argumentation has emerged as a method for non-monotonic reasoning that has gained popularity in the symbolic artificial intelligence community. In the literature, the different approaches to abstract argumentation that were refined over the years are typically evaluated from a formal logics perspective; an ana...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	155,561

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