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
2009.08128	Multi$^2$OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT	In this paper, we propose Multi$^2$OIE, which performs open information extraction (open IE) by combining BERT with multi-head attention. Our model is a sequence-labeling system with an efficient and effective argument extraction method. We use a query, key, and value setting inspired by the Multimodal Transformer to r...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	196,144
2302.09700	Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty	In online marketplaces, customers have access to hundreds of reviews for a single product. Buyers often use reviews from other customers that share their type -- such as height for clothing, skin type for skincare products, and location for outdoor furniture -- to estimate their values, which they may not know a priori...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	346,531
2004.02929	An Annotated Corpus of Emerging Anglicisms in Spanish Newspaper Headlines	The extraction of anglicisms (lexical borrowings from English) is relevant both for lexicographic purposes and for NLP downstream tasks. We introduce a corpus of European Spanish newspaper headlines annotated with anglicisms and a baseline model for anglicism extraction. In this paper we present: (1) a corpus of 21,570...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	171,379
2305.02748	A computational framework of human values for ethical AI	In the diverse array of work investigating the nature of human values from psychology, philosophy and social sciences, there is a clear consensus that values guide behaviour. More recently, a recognition that values provide a means to engineer ethical AI has emerged. Indeed, Stuart Russell proposed shifting AI's focus ...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	true	false	false	false	362,156
2003.01337	DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation	Segmentation of brain tumors and their subregions remains a challenging task due to their weak features and deformable shapes. In this paper, three patterns (cross-skip, skip-1 and skip-2) of distributed dense connections (DDCs) are proposed to enhance feature reuse and propagation of CNNs by constructing tunnels betwe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	166,621
2412.09769	A Novel Methodology in Credit Spread Prediction Based on Ensemble Learning and Feature Selection	The credit spread is a key indicator in bond investments, offering valuable insights for fixed-income investors to devise effective trading strategies. This study proposes a novel credit spread forecasting model leveraging ensemble learning techniques. To enhance predictive accuracy, a feature selection method based on...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	516,633
2203.14855	Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task Division	Deep imitation learning requires many expert demonstrations, which can be hard to obtain, especially when many tasks are involved. However, different tasks often share similarities, so learning them jointly can greatly benefit them and alleviate the need for many demonstrations. But, joint multi-task learning often suf...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	288,154
2309.10263	Disentangled Information Bottleneck guided Privacy-Protective JSCC for Image Transmission	Joint source and channel coding (JSCC) has attracted increasing attention due to its robustness and high efficiency. However, JSCC is vulnerable to privacy leakage due to the high relevance between the source image and channel input. In this paper, we propose a disentangled information bottleneck guided privacy-protect...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	392,930
2308.08174	Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design	Graph neural networks (GNNs) have shown significant accuracy improvements in a variety of graph learning domains, sparking considerable research interest. To translate these accuracy improvements into practical applications, it is essential to develop high-performance and efficient hardware acceleration for GNN models....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	385,799
1302.1947	A new compressive video sensing framework for mobile broadcast	A new video coding method based on compressive sampling is proposed. In this method, a video is coded using compressive measurements on video cubes. Video reconstruction is performed by minimization of total variation (TV) of the pixelwise DCT coefficients along the temporal direction. A new reconstruction algorithm is...	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	true	21,908
2110.14631	Reed-Muller Codes on BMS Channels Achieve Vanishing Bit-Error Probability for All Rates Below Capacity	This paper considers the performance of Reed-Muller (RM) codes transmitted over binary memoryless symmetric (BMS) channels under bitwise maximum-a-posteriori (bit-MAP) decoding. Its main result is that, for a fixed BMS channel, the family of binary RM codes can achieve a vanishing bit-error probability at rates approac...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	263,592
2206.10073	Finding Optimal Policy for Queueing Models: New Parameterization	Queueing systems appear in many important real-life applications including communication networks, transportation and manufacturing systems. Reinforcement learning (RL) framework is a suitable model for the queueing control problem where the underlying dynamics are usually unknown and the agent receives little informat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	303,780
2111.02484	Accelerated replica exchange stochastic gradient Langevin diffusion enhanced Bayesian DeepONet for solving noisy parametric PDEs	The Deep Operator Networks~(DeepONet) is a fundamentally different class of neural networks that we train to approximate nonlinear operators, including the solution operator of parametric partial differential equations (PDE). DeepONets have shown remarkable approximation and generalization capabilities even when traine...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	264,885
1212.5592	Multiple model software for airflow and thermal building simulation. A case study under tropical humid climate, in R\'eunion Island	The first purpose of our work has been to allow -as far as heat transfer modes, airflow calculation and meteorological data reconstitution are concerned- the integration of diverse interchangeable physical models in a single software tool for professional use, CODYRUN. The designer's objectives, precision requested and...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	20,566
2303.01170	Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning	Transfer learning in Reinforcement Learning (RL) has been widely studied to overcome training issues of Deep-RL, i.e., exploration cost, data availability and convergence time, by introducing a way to enhance training phase with external knowledge. Generally, knowledge is transferred from expert-agents to novices. Whil...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	348,856
2009.00792	Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction	Current machine learning has made great progress on computer vision and many other fields attributed to the large amount of high-quality training samples, while it does not work very well on genomic data analysis, since they are notoriously known as small data. In our work, we focus on few-shot disease subtype predicti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	194,140
1306.0094	Analysis of Mismatched Estimation Errors Using Gradients of Partition Functions	We consider the problem of signal estimation (denoising) from a statistical-mechanical perspective, in continuation to a recent work on the analysis of mean-square error (MSE) estimation using a direct relationship between optimum estimation and certain partition functions. The paper consists of essentially two parts. ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	24,926
2405.04327	Audio-Visual Speech Representation Expert for Enhanced Talking Face Video Generation and Evaluation	In the task of talking face generation, the objective is to generate a face video with lips synchronized to the corresponding audio while preserving visual details and identity information. Current methods face the challenge of learning accurate lip synchronization while avoiding detrimental effects on visual quality, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	452,534
2302.13710	Global Algorithms for Mean-Variance Optimization in Markov Decision Processes	Dynamic optimization of mean and variance in Markov decision processes (MDPs) is a long-standing challenge caused by the failure of dynamic programming. In this paper, we propose a new approach to find the globally optimal policy for combined metrics of steady-state mean and variance in an infinite-horizon undiscounted...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	348,038
2108.10072	Automated Identification of Cell Populations in Flow Cytometry Data with Transformers	Acute Lymphoblastic Leukemia (ALL) is the most frequent hematologic malignancy in children and adolescents. A strong prognostic factor in ALL is given by the Minimal Residual Disease (MRD), which is a measure for the number of leukemic cells persistent in a patient. Manual MRD assessment from Multiparameter Flow Cytome...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	251,794
2010.10508	Polar Deconvolution of Mixed Signals	The signal demixing problem seeks to separate a superposition of multiple signals into its constituent components. This paper studies a two-stage approach that first decompresses and subsequently deconvolves the noisy and undersampled observations of the superposition using two convex programs. Probabilistic error boun...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	201,905
2411.13289	Passive knee flexion increases forward impulse of the trailing leg during the step-to-step transition	Human walking efficiency relies on the elastic recoil of the Achilles tendon, facilitated by a "catapult mechanism" that stores energy during stance and releases it during push-off. The catapult release mechanism could include the passive flexion of the knee, as the main part of knee flexion was reported to happen pass...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	509,744
2203.03059	Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?	Meta learning aims at learning a model that can quickly adapt to unseen tasks. Widely used meta learning methods include model agnostic meta learning (MAML), implicit MAML, Bayesian MAML. Thanks to its ability of modeling uncertainty, Bayesian MAML often has advantageous empirical performance. However, the theoretical ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	283,952
2210.06806	OOOE: Only-One-Object-Exists Assumption to Find Very Small Objects in Chest Radiographs	The accurate localization of inserted medical tubes and parts of human anatomy is a common problem when analyzing chest radiographs and something deep neural networks could potentially automate. However, many foreign objects like tubes and various anatomical structures are small in comparison to the entire chest X-ray,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	323,456
1910.06487	Contrarian effects and echo chamber formation in opinion dynamics	The relationship between the topology of a network and specific types of dynamics unfolding in networks constitutes a subject of substantial interest. One type of dynamics that has attracted increasing attention because of its several potential implications is opinion formation. A phenomenon of particular importance, k...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	149,349
2101.08469	Hybrid Beamforming for Terahertz Wireless Communications: Challenges, Architectures, and Open Problems	Terahertz (THz) communications are regarded as a pillar technology for the sixth generation (6G) wireless systems, by offering multi-ten-GHz bandwidth. To overcome the short transmission distance and huge propagation loss, ultra-massive (UM) MIMO systems that employ sub-millimeter wavelength antennas array are proposed...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	216,333
2304.04049	Deep Generative Modeling with Backward Stochastic Differential Equations	This paper proposes a novel deep generative model, called BSDE-Gen, which combines the flexibility of backward stochastic differential equations (BSDEs) with the power of deep neural networks for generating high-dimensional complex target data, particularly in the field of image generation. The incorporation of stochas...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	357,053
2305.03912	White Matter Hyperintensities Segmentation Using Probabilistic TransUNet	White Matter Hyperintensities (WMH) are areas of the brain that have higher intensity than other normal brain regions on Magnetic Resonance Imaging (MRI) scans. WMH is often associated with small vessel disease in the brain, making early detection of WMH important. However, there are two common issues in the detection ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	362,559
2406.08474	Real2Code: Reconstruct Articulated Objects via Code Generation	We present Real2Code, a novel approach to reconstructing articulated objects via code generation. Given visual observations of an object, we first reconstruct its part geometry using an image segmentation model and a shape completion model. We then represent the object parts with oriented bounding boxes, which are inpu...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	463,507
1610.08914	Ex Machina: Personal Attacks Seen at Scale	The damage personal attacks cause to online discourse motivates many platforms to try to curb the phenomenon. However, understanding the prevalence and impact of personal attacks in online platforms at scale remains surprisingly difficult. The contribution of this paper is to develop and illustrate a method that combin...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	62,981
2008.09316	Explainable Recommender Systems via Resolving Learning Representations	Recommender systems play a fundamental role in web applications in filtering massive information and matching user interests. While many efforts have been devoted to developing more effective models in various scenarios, the exploration on the explainability of recommender systems is running behind. Explanations could ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	192,680
1602.01323	Biclustering Readings and Manuscripts via Non-negative Matrix Factorization, with Application to the Text of Jude	The text-critical practice of grouping witnesses into families or texttypes often faces two obstacles: Contamination in the manuscript tradition, and co-dependence in identifying characteristic readings and manuscripts. We introduce non-negative matrix factorization (NMF) as a simple, unsupervised, and efficient way to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	51,687
1801.02325	Long-term Multi-granularity Deep Framework for Driver Drowsiness Detection	For real-world driver drowsiness detection from videos, the variation of head pose is so large that the existing methods on global face is not capable of extracting effective features, such as looking aside and lowering head. Temporal dependencies with variable length are also rarely considered by the previous approach...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	87,910
1109.2135	A Framework for Sequential Planning in Multi-Agent Settings	This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state space. Agents maintain beliefs over physical states of the environment and over models of other agents, and they use Bayesian updates to maintain...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	12,084
2007.06531	A Robotic Framework for Making Eye Contact with Humans	Meeting eye contact is the essential prerequisite skill of a human to initiate any conversation with others. However, it is not an easy task for a robot to meet eye contact with a human if they are not facing each other initially or the human is intensely engaged his or her task. If the robot would like to start commun...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	187,036
1911.03766	Multi-Sentence Argument Linking	We present a novel document-level model for finding argument spans that fill an event's roles, connecting related ideas in sentence-level semantic role labeling and coreference resolution. Because existing datasets for cross-sentence linking are small, development of our neural model is supported through the creation o...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	152,749
2409.12962	CLAIR-A: Leveraging Large Language Models to Judge Audio Captions	The Automated Audio Captioning (AAC) task asks models to generate natural language descriptions of an audio input. Evaluating these machine-generated audio captions is a complex task that requires considering diverse factors, among them, auditory scene understanding, sound-object inference, temporal coherence, and the ...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	489,790
2412.16581	Effective and Efficient Representation Learning for Flight Trajectories	Flight trajectory data plays a vital role in the traffic management community, especially for downstream tasks such as trajectory prediction, flight recognition, and anomaly detection. Existing works often utilize handcrafted features and design models for different tasks individually, which heavily rely on domain expe...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	519,595
2404.14812	Pattern-Aware Chain-of-Thought Prompting in Large Language Models	Chain-of-thought (CoT) prompting can guide language models to engage in complex multi-step reasoning. The quality of provided demonstrations significantly impacts the success of downstream inference tasks. While existing automated methods prioritize accuracy and semantics in these demonstrations, we show that the under...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	448,829
1110.5609	Self-similar scaling of density in complex real-world networks	Despite their diverse origin, networks of large real-world systems reveal a number of common properties including small-world phenomena, scale-free degree distributions and modularity. Recently, network self-similarity as a natural outcome of the evolution of real-world systems has also attracted much attention within ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	12,767
2002.04264	The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification	Key for solving fine-grained image categorization is finding discriminate and local regions that correspond to subtle visual traits. Great strides have been made, with complex networks designed specifically to learn part-level discriminate feature representations. In this paper, we show it is possible to cultivate subt...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	163,562
2309.03245	Testing properties of distributions in the streaming model	We study distribution testing in the standard access model and the conditional access model when the memory available to the testing algorithm is bounded. In both scenarios, the samples appear in an online fashion and the goal is to test the properties of distribution using an optimal number of samples subject to a mem...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	390,330
2410.03919	Online Posterior Sampling with a Diffusion Prior	Posterior sampling in contextual bandits with a Gaussian prior can be implemented exactly or approximately using the Laplace approximation. The Gaussian prior is computationally efficient but it cannot describe complex distributions. In this work, we propose approximate posterior sampling algorithms for contextual band...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	495,042
0905.3407	Throughput and Delay Scaling in Supportive Two-Tier Networks	Consider a wireless network that has two tiers with different priorities: a primary tier vs. a secondary tier, which is an emerging network scenario with the advancement of cognitive radio technologies. The primary tier consists of randomly distributed legacy nodes of density $n$, which have an absolute priority to acc...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,737
2406.15794	Linear complementary pairs of codes over a finite non-commutative Frobenius ring	In this paper, we study linear complementary pairs (LCP) of codes over finite non-commutative local rings. We further provide a necessary and sufficient condition for a pair of codes $(C,D)$ to be LCP of codes over finite non-commutative Frobenius rings. The minimum distances $d(C)$ and $d(D^\perp)$ are defined as the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	466,870
2412.11682	NEST: A Neuromodulated Small-world Hypergraph Trajectory Prediction Model for Autonomous Driving	Accurate trajectory prediction is essential for the safety and efficiency of autonomous driving. Traditional models often struggle with real-time processing, capturing non-linearity and uncertainty in traffic environments, efficiency in dense traffic, and modeling temporal dynamics of interactions. We introduce NEST (N...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	517,532
1411.4033	Sparse And Low Rank Decomposition Based Batch Image Alignment for Speckle Reduction of retinal OCT Images	Optical Coherence Tomography (OCT) is an emerging technique in the field of biomedical imaging, with applications in ophthalmology, dermatology, coronary imaging etc. Due to the underlying physics, OCT images usually suffer from a granular pattern, called speckle noise, which restricts the process of interpretation. He...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	37,567
2303.13482	TactoFind: A Tactile Only System for Object Retrieval	We study the problem of object retrieval in scenarios where visual sensing is absent, object shapes are unknown beforehand and objects can move freely, like grabbing objects out of a drawer. Successful solutions require localizing free objects, identifying specific object instances, and then grasping the identified obj...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	353,681
2002.02301	Wireless Powered Protocol Exploiting Energy Harvesting During Cognitive Communications	In this letter, a novel wireless powered protocol is proposed to maximize the system throughput of an energy harvesting (EH) based cognitive radio network, while satisfying a minimum primary user rate requirement. For EH, we exploit both dedicated wireless power transfer from primary base station as well as ambient one...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	162,883
2407.14342	Quantifying the value of positive transfer: An experimental case study	In traditional approaches to structural health monitoring, challenges often arise associated with the availability of labelled data. Population-based structural health monitoring seeks to overcomes these challenges by leveraging data/information from similar structures via technologies such as transfer learning. The cu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	474,749
2203.01978	Region-of-Interest Based Neural Video Compression	Humans do not perceive all parts of a scene with the same resolution, but rather focus on few regions of interest (ROIs). Traditional Object-Based codecs take advantage of this biological intuition, and are capable of non-uniform allocation of bits in favor of salient regions, at the expense of increased distortion the...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	283,585
1103.3391	An Integer Linear Programming Model for the Radiotherapy Treatment Scheduling Problem	Radiotherapy represents an important phase of treatment for a large number of cancer patients. It is essential that resources used to deliver this treatment are employed effectively. This paper presents a new integer linear programming model for real-world radiotherapy treatment scheduling and analyses the effectivenes...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	9,649
2004.13486	On the Reliability of Test Collections for Evaluating Systems of Different Types	As deep learning based models are increasingly being used for information retrieval (IR), a major challenge is to ensure the availability of test collections for measuring their quality. Test collections are generated based on pooling results of various retrieval systems, but until recently this did not include deep le...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	174,567
2207.12302	Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning	The availability of large labeled datasets is the key component for the success of deep learning. However, annotating labels on large datasets is generally time-consuming and expensive. Active learning is a research area that addresses the issues of expensive labeling by selecting the most important samples for labelin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	309,967
2211.01848	Circling Back to Recurrent Models of Language	Just because some purely recurrent models suffer from being hard to optimize and inefficient on today's hardware, they are not necessarily bad models of language. We demonstrate this by the extent to which these models can still be improved by a combination of a slightly better recurrent cell, architecture, objective, ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	328,385
cmp-lg/9503025	Co-occurrence Vectors from Corpora vs. Distance Vectors from Dictionaries	A comparison was made of vectors derived by using ordinary co-occurrence statistics from large text corpora and of vectors derived by measuring the inter-word distances in dictionary definitions. The precision of word sense disambiguation by using co-occurrence vectors from the 1987 Wall Street Journal (20M total words...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,325
2409.15766	CHBench: A Chinese Dataset for Evaluating Health in Large Language Models	With the rapid development of large language models (LLMs), assessing their performance on health-related inquiries has become increasingly essential. It is critical that these models provide accurate and trustworthy health information, as their application in real-world contexts--where misinformation can have serious ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	491,055
2302.05412	Achieving Linear Speedup in Non-IID Federated Bilevel Learning	Federated bilevel optimization has received increasing attention in various emerging machine learning and communication applications. Recently, several Hessian-vector-based algorithms have been proposed to solve the federated bilevel optimization problem. However, several important properties in federated learning such...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	345,034
2203.14628	FS6D: Few-Shot 6D Pose Estimation of Novel Objects	6D object pose estimation networks are limited in their capability to scale to large numbers of object instances due to the close-set assumption and their reliance on high-fidelity object CAD models. In this work, we study a new open set problem; the few-shot 6D object poses estimation: estimating the 6D pose of an unk...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	288,076
1601.01507	Fast Kronecker product kernel methods via generalized vec trick	Kronecker product kernel provides the standard approach in the kernel methods literature for learning from graph data, where edges are labeled and both start and end vertices have their own feature representations. The methods allow generalization to such new edges, whose start and end vertices do not appear in the tra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	50,757
2408.12534	Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge	Organ and cancer segmentation in abdomen Computed Tomography (CT) scans is the prerequisite for precise cancer diagnosis and treatment. Most existing benchmarks and algorithms are tailored to specific cancer types, limiting their ability to provide comprehensive cancer analysis. This work presents the first internation...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	482,773
2308.03543	Slepian spatiospectral concentration problem on the $d$-dimensional ball for different notions of bandwidth	We study the asymptotic eigenvalue distribution of the Slepian spatiospectral concentration problem within subdomains of the $d$-dimensional unit ball $\mathbb{B}^d$. The clustering of the eigenvalues near zero and one is a well-known phenomenon. Here, we provide an analytical investigation of this phenomenon for two d...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	384,075
2206.01432	On the Generalization of Wasserstein Robust Federated Learning	In federated learning, participating clients typically possess non-i.i.d. data, posing a significant challenge to generalization to unseen distributions. To address this, we propose a Wasserstein distributionally robust optimization scheme called WAFL. Leveraging its duality, we frame WAFL as an empirical surrogate ris...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	300,474
1604.04378	Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN	Semantic matching, which aims to determine the matching degree between two texts, is a fundamental problem for many NLP applications. Recently, deep learning approach has been applied to this problem and significant improvements have been achieved. In this paper, we propose to view the generation of the global interact...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	true	false	false	54,637
2212.10930	Minimizing Worst-Case Violations of Neural Networks	Machine learning (ML) algorithms are remarkably good at approximating complex non-linear relationships. Most ML training processes, however, are designed to deliver ML tools with good average performance, but do not offer any guarantees about their worst-case estimation error. For safety-critical systems such as power ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	337,656
1503.06952	Comparing published multi-label classifier performance measures to the ones obtained by a simple multi-label baseline classifier	In supervised learning, simple baseline classifiers can be constructed by only looking at the class, i.e., ignoring any other information from the dataset. The single-label learning community frequently uses as a reference the one which always predicts the majority class. Although a classifier might perform worse than ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	41,420
1305.6569	Mathematical Analysis of Temperature Accelerated Dynamics	We give a mathematical framework for temperature accelerated dynamics (TAD), an algorithm proposed by M.R. S{\o}rensen and A.F. Voter to efficiently generate metastable stochastic dynamics. Using the notion of quasistationary distributions, we propose some modifications to TAD. Then considering the modified algorithm i...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	24,847
1401.6512	Achievable Degrees of Freedom in MIMO Correlatively Changing Fading Channels	The relationship between the transmitted signal and the noiseless received signals in correlatively changing fading channels is modeled as a nonlinear mapping over manifolds of different dimensions. Dimension counting argument claims that the dimensionality of the neighborhood in which this mapping is bijective with pr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	30,366
1901.11068	Caching or No Caching in Dense HetNets?	Caching the content closer to the user equipments (UEs) in heterogenous cellular networks (HetNets) improves user-perceived Quality-of-Service (QoS) while lowering the operators backhaul usage/costs. Nevertheless, under the current networking strategy that promotes aggressive densification, it is unclear whether cache-...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	120,157
1404.6272	Scalable Similarity Learning using Large Margin Neighborhood Embedding	Classifying large-scale image data into object categories is an important problem that has received increasing research attention. Given the huge amount of data, non-parametric approaches such as nearest neighbor classifiers have shown promising results, especially when they are underpinned by a learned distance or sim...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	32,572
2006.15693	Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning	Resective surgery may be curative for drug-resistant focal epilepsy, but only 40% to 70% of patients achieve seizure freedom after surgery. Retrospective quantitative analysis could elucidate patterns in resected structures and patient outcomes to improve resective surgery. However, the resection cavity must first be s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	184,592
2203.00190	Semi-supervised Deep Learning for Image Classification with Distribution Mismatch: A Survey	Deep learning methodologies have been employed in several different fields, with an outstanding success in image recognition applications, such as material quality control, medical imaging, autonomous driving, etc. Deep learning models rely on the abundance of labelled observations to train a prospective model. These m...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	282,907
2208.07678	FEC: Fast Euclidean Clustering for Point Cloud Segmentation	Segmentation from point cloud data is essential in many applications such as remote sensing, mobile robots, or autonomous cars. However, the point clouds captured by the 3D range sensor are commonly sparse and unstructured, challenging efficient segmentation. In this paper, we present a fast solution to point cloud ins...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	313,120
1103.1958	On the Root Finding Step in List Decoding of Folded Reed-Solomon Codes	The root finding step of the Guruswami-Rudra list decoding algorithm for folded Reed-Solomon codes is considered. It is shown that a multivariate generalization of the Roth-Ruckenstein algorithm can be used to implement it. This leads to an improved bound on the size of the list produced by the decoder, as well as enab...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	9,555
1805.07475	Learning to Repair Software Vulnerabilities with Generative Adversarial Networks	Motivated by the problem of automated repair of software vulnerabilities, we propose an adversarial learning approach that maps from one discrete source domain to another target domain without requiring paired labeled examples or source and target domains to be bijections. We demonstrate that the proposed adversarial l...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	97,835
2103.02662	Deep Clustering by Semantic Contrastive Learning	Whilst contrastive learning has recently brought notable benefits to deep clustering of unlabelled images by learning sample-specific discriminative visual features, its potential for explicitly inferring class decision boundaries is less well understood. This is because its instance discrimination strategy is not clas...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	223,025
1401.2051	Enhancement performance of road recognition system of autonomous robots in shadow scenario	Road region recognition is a main feature that is gaining increasing attention from intellectuals because it helps autonomous vehicle to achieve a successful navigation without accident. However, different techniques based on camera sensor have been used by various researchers and outstanding results have been achieved...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	29,711
2301.08802	Impact of PCA-based preprocessing and different CNN structures on deformable registration of sonograms	Central venous catheters (CVC) are commonly inserted into the large veins of the neck, e.g. the internal jugular vein (IJV). CVC insertion may cause serious complications like misplacement into an artery or perforation of cervical vessels. Placing a CVC under sonographic guidance is an appropriate method to reduce such...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	341,294
2204.12402	Understanding the Impact of Edge Cases from Occluded Pedestrians for ML Systems	Machine learning (ML)-enabled approaches are considered a substantial support technique of detection and classification of obstacles of traffic participants in self-driving vehicles. Major breakthroughs have been demonstrated the past few years, even covering complete end-to-end data processing chain from sensory input...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	293,461
2303.01788	Visual Exemplar Driven Task-Prompting for Unified Perception in Autonomous Driving	Multi-task learning has emerged as a powerful paradigm to solve a range of tasks simultaneously with good efficiency in both computation resources and inference time. However, these algorithms are designed for different tasks mostly not within the scope of autonomous driving, thus making it hard to compare multi-task m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	349,109
1511.09231	Design of Kernels in Convolutional Neural Networks for Image Classification	Despite the effectiveness of Convolutional Neural Networks (CNNs) for image classification, our understanding of the relationship between shape of convolution kernels and learned representations is limited. In this work, we explore and employ the relationship between shape of kernels which define Receptive Fields (RFs)...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	49,648
1311.5787	Trajectory control of a bipedal walking robot with inertial disc	In this paper we exploit some interesting properties of a class of bipedal robots which have an inertial disc. One of this properties is the ability to control every position and speed except for the disc position. The proposed control is designed in two hierarchic levels. The first will drive the robot geometry, while...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	28,587
2306.04343	Bayesian Optimisation Against Climate Change: Applications and Benchmarks	Bayesian optimisation is a powerful method for optimising black-box functions, popular in settings where the true function is expensive to evaluate and no gradient information is available. Bayesian optimisation can improve responses to many optimisation problems within climate change for which simulator models are una...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	371,704
2405.15568	OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code	Open-ended and AI-generating algorithms aim to continuously generate and solve increasingly complex tasks indefinitely, offering a promising path toward more general intelligence. To accomplish this grand vision, learning must occur within a vast array of potential tasks. Existing approaches to automatically generating...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	456,997
1704.04601	MUSE: Modularizing Unsupervised Sense Embeddings	This paper proposes to address the word sense ambiguity issue in an unsupervised manner, where word sense representations are learned along a word sense selection mechanism given contexts. Prior work focused on designing a single model to deliver both mechanisms, and thus suffered from either coarse-grained representat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	71,843
2402.01505	Code-Switched Language Identification is Harder Than You Think	Code switching (CS) is a very common phenomenon in written and spoken communication but one that is handled poorly by many natural language processing applications. Looking to the application of building CS corpora, we explore CS language identification (LID) for corpus building. We make the task more realistic by scal...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	426,051
1611.09238	Improving Multi-Document Summarization via Text Classification	Developed so far, multi-document summarization has reached its bottleneck due to the lack of sufficient training data and diverse categories of documents. Text classification just makes up for these deficiencies. In this paper, we propose a novel summarization system called TCSum, which leverages plentiful text classif...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	64,643
0908.0358	Outage analysis of Block-Fading Gaussian Interference Channels	This paper considers the asymptotic behavior of two-source block-fading single-antenna Gaussian interference channels in the high-SNR regime by means of the diversity-multiplexing tradeoff. We consider a general setting where the users and the average channel gains are not restricted to be symmetric. Our results are no...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	4,204
2002.06546	Neural Machine Translation with Joint Representation	Though early successes of Statistical Machine Translation (SMT) systems are attributed in part to the explicit modelling of the interaction between any two source and target units, e.g., alignment, the recent Neural Machine Translation (NMT) systems resort to the attention which partially encodes the interaction for ef...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	164,227
2410.14164	Optimal DLT-based Solutions for the Perspective-n-Point	We propose a modified normalized direct linear transform (DLT) algorithm for solving the perspective-n-point (PnP) problem with much better behavior than the conventional DLT. The modification consists of analytically weighting the different measurements in the linear system with a negligible increase in computational ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	499,903
2301.03418	Nuclear Segmentation and Classification: On Color & Compression Generalization	Since the introduction of digital and computational pathology as a field, one of the major problems in the clinical application of algorithms has been the struggle to generalize well to examples outside the distribution of the training data. Existing work to address this in both pathology and natural images has focused...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	339,799
1909.03395	Multi-group connectivity structures and their implications	We investigate the implications of different forms of multi-group connectivity. Four multi-group connectivity modalities are considered: co-memberships, edge bundles, bridges, and liaison hierarchies. We propose generative models to generate these four modalities. Our models are variants of planted partition or stochas...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	144,463
2310.04271	Compositional Servoing by Recombining Demonstrations	Learning-based manipulation policies from image inputs often show weak task transfer capabilities. In contrast, visual servoing methods allow efficient task transfer in high-precision scenarios while requiring only a few demonstrations. In this work, we present a framework that formulates the visual servoing task as gr...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	397,587
2410.08339	Simultaneous Weight and Architecture Optimization for Neural Networks	Neural networks are trained by choosing an architecture and training the parameters. The choice of architecture is often by trial and error or with Neural Architecture Search (NAS) methods. While NAS provides some automation, it often relies on discrete steps that optimize the architecture and then train the parameters...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	497,069
2204.04157	Custom Sine Waves Are Enough for Imitation Learning of Bipedal Gaits with Different Styles	Not until recently, robust bipedal locomotion has been achieved through reinforcement learning. However, existing implementations rely heavily on insights and efforts from human experts, which is costly for the iterative design of robot systems. Also, styles of the learned motion are strictly limited to that of the ref...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	290,557
2203.13090	AziNorm: Exploiting the Radial Symmetry of Point Cloud for Azimuth-Normalized 3D Perception	Studying the inherent symmetry of data is of great importance in machine learning. Point cloud, the most important data format for 3D environmental perception, is naturally endowed with strong radial symmetry. In this work, we exploit this radial symmetry via a divide-and-conquer strategy to boost 3D perception perform...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	287,504
1903.00637	One-Pass Incomplete Multi-view Clustering	Real data are often with multiple modalities or from multiple heterogeneous sources, thus forming so-called multi-view data, which receives more and more attentions in machine learning. Multi-view clustering (MVC) becomes its important paradigm. In real-world applications, some views often suffer from instances missing...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	123,054
2206.00560	Learning common structures in a collection of networks. An application to food webs	Let a collection of networks represent interactions within several (social or ecological) systems. We pursue two objectives: identifying similarities in the topological structures that are held in common between the networks and clustering the collection into sub-collections of structurally homogeneous networks. We tac...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	300,173
2411.00846	Explainable Artificial Intelligence for Dependent Features: Additive Effects of Collinearity	Explainable Artificial Intelligence (XAI) emerged to reveal the internal mechanism of machine learning models and how the features affect the prediction outcome. Collinearity is one of the big issues that XAI methods face when identifying the most informative features in the model. Current XAI approaches assume the fea...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	504,785
2502.07136	A Safe Hybrid Control Framework for Car-like Robot with Guaranteed Global Path-Invariance using a Control Barrier Function	This work proposes a hybrid framework for car-like robots with obstacle avoidance, global convergence, and safety, where safety is interpreted as path invariance, namely, once the robot converges to the path, it never leaves the path. Given a priori obstacle-free feasible path where obstacles can be around the path, th...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	532,439

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.