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
1912.01070	Simultaneously Linking Entities and Extracting Relations from Biomedical Text Without Mention-level Supervision	Understanding the meaning of text often involves reasoning about entities and their relationships. This requires identifying textual mentions of entities, linking them to a canonical concept, and discerning their relationships. These tasks are nearly always viewed as separate components within a pipeline, each requirin...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	155,964
2405.04189	Artificial Intelligence-powered fossil shark tooth identification: Unleashing the potential of Convolutional Neural Networks	All fields of knowledge are being impacted by Artificial Intelligence. In particular, the Deep Learning paradigm enables the development of data analysis tools that support subject matter experts in a variety of sectors, from physics up to the recognition of ancient languages. Palaeontology is now observing this trend ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	452,483
2308.04440	Big Bang, Low Bar -- Risk Assessment in the Public Arena	One of the basic principles of risk management is that we should always keep an eye on ways that things could go badly wrong, even if they seem unlikely. The more disastrous a potential failure, the more improbable it needs to be, before we can safely ignore it. This principle may seem obvious, but it is easily overloo...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	384,412
1309.7824	Linear Regression from Strategic Data Sources	Linear regression is a fundamental building block of statistical data analysis. It amounts to estimating the parameters of a linear model that maps input features to corresponding outputs. In the classical setting where the precision of each data point is fixed, the famous Aitken/Gauss-Markov theorem in statistics stat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	27,426
2007.12066	A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation	The work presented in this paper is to propose a reliable high-quality system of Convolutional Neural Network (CNN) for brain tumor segmentation with a low computation requirement. The system consists of a CNN for the main processing for the segmentation, a pre-CNN block for data reduction and post-CNN refinement block...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	188,717
1506.03899	Place classification with a graph regularized deep neural network model	Place classification is a fundamental ability that a robot should possess to carry out effective human-robot interactions. It is a nontrivial classification problem which has attracted many research. In recent years, there is a high exploitation of Artificial Intelligent algorithms in robotics applications. Inspired by...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	true	false	false	44,109
1409.7850	Distributed Reception with Spatial Multiplexing: MIMO Systems for the Internet of Things	The Internet of things (IoT) holds much commercial potential and could facilitate distributed multiple-input multiple-output (MIMO) communication in future systems. We study a distributed reception scenario in which a transmitter equipped with multiple antennas sends multiple streams via spatial multiplexing to a large...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	36,368
1610.05267	Rule Extraction Algorithm for Deep Neural Networks: A Review	Despite the highest classification accuracy in wide varieties of application areas, artificial neural network has one disadvantage. The way this Network comes to a decision is not easily comprehensible. The lack of explanation ability reduces the acceptability of neural network in data mining and decision system. This ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	62,497
2305.16724	Code-Switched Text Synthesis in Unseen Language Pairs	Existing efforts on text synthesis for code-switching mostly require training on code-switched texts in the target language pairs, limiting the deployment of the models to cases lacking code-switched data. In this work, we study the problem of synthesizing code-switched texts for language pairs absent from the training...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	368,225
2412.09028	Learning and Current Prediction of PMSM Drive via Differential Neural Networks	Learning models for dynamical systems in continuous time is significant for understanding complex phenomena and making accurate predictions. This study presents a novel approach utilizing differential neural networks (DNNs) to model nonlinear systems, specifically permanent magnet synchronous motors (PMSMs), and to pre...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	516,328
2105.05571	"Alexa, what do you do for fun?" Characterizing playful requests with virtual assistants	Virtual assistants such as Amazon's Alexa, Apple's Siri, Google Home, and Microsoft's Cortana, are becoming ubiquitous in our daily lives and successfully help users in various daily tasks, such as making phone calls or playing music. Yet, they still struggle with playful utterances, which are not meant to be interpret...	true	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	234,856
2203.13321	Addressing Client Drift in Federated Continual Learning with Adaptive Optimization	Federated learning has been extensively studied and is the prevalent method for privacy-preserving distributed learning in edge devices. Correspondingly, continual learning is an emerging field targeted towards learning multiple tasks sequentially. However, there is little attention towards additional challenges emergi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	287,588
2011.02127	Sequence-to-Sequence Learning via Attention Transfer for Incremental Speech Recognition	Attention-based sequence-to-sequence automatic speech recognition (ASR) requires a significant delay to recognize long utterances because the output is generated after receiving entire input sequences. Although several studies recently proposed sequence mechanisms for incremental speech recognition (ISR), using differe...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	204,830
2502.04499	Revisiting Intermediate-Layer Matching in Knowledge Distillation: Layer-Selection Strategy Doesn't Matter (Much)	Knowledge distillation (KD) is a popular method of transferring knowledge from a large "teacher" model to a small "student" model. KD can be divided into two categories: prediction matching and intermediate-layer matching. We explore an intriguing phenomenon: layer-selection strategy does not matter (much) in intermedi...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	531,185
2203.02385	MM-DFN: Multimodal Dynamic Fusion Network for Emotion Recognition in Conversations	Emotion Recognition in Conversations (ERC) has considerable prospects for developing empathetic machines. For multimodal ERC, it is vital to understand context and fuse modality information in conversations. Recent graph-based fusion methods generally aggregate multimodal information by exploring unimodal and cross-mod...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	283,728
2002.04720	Improving Molecular Design by Stochastic Iterative Target Augmentation	Generative models in molecular design tend to be richly parameterized, data-hungry neural models, as they must create complex structured objects as outputs. Estimating such models from data may be challenging due to the lack of sufficient training data. In this paper, we propose a surprisingly effective self-training a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	163,674
1909.02909	Game Theoretical Approach to Sequential Hypothesis Test with Byzantine Sensors	In this paper, we consider the problem of sequential binary hypothesis test in adversary environment based on observations from s sensors, with the caveat that a subset of c sensors is compromised by an adversary, whose observations can be manipulated arbitrarily. We choose the asymptotic Average Sample Number (ASN) re...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	144,318
1902.03544	Feature Selection for multi-labeled variables via Dependency Maximization	Feature selection and reducing the dimensionality of data is an essential step in data analysis. In this work, we propose a new criterion for feature selection that is formulated as conditional information between features given the labeled variable. Instead of using the standard mutual information measure based on Kul...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	121,139
2102.04866	Residue Density Segmentation for Monitoring and Optimizing Tillage Practices	"No-till" and cover cropping are often identified as the leading simple, best management practices for carbon sequestration in agriculture. However, the root of the problem is more complex, with the potential benefits of these approaches depending on numerous factors including a field's soil type(s), topography, and ma...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	219,248
1604.02218	A Low Complexity Algorithm with $O(\sqrt{T})$ Regret and $O(1)$ Constraint Violations for Online Convex Optimization with Long Term Constraints	This paper considers online convex optimization over a complicated constraint set, which typically consists of multiple functional constraints and a set constraint. The conventional online projection algorithm (Zinkevich, 2003) can be difficult to implement due to the potentially high computation complexity of the proj...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	54,306
2204.03835	LoCI: An Analysis of the Impact of Optical Loss and Crosstalk Noise in Integrated Silicon-Photonic Neural Networks	Compared to electronic accelerators, integrated silicon-photonic neural networks (SP-NNs) promise higher speed and energy efficiency for emerging artificial-intelligence applications. However, a hitherto overlooked problem in SP-NNs is that the underlying silicon photonic devices suffer from intrinsic optical loss and ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	290,449
1510.07748	Computational models: Bottom-up and top-down aspects	Computational models of visual attention have become popular over the past decade, we believe primarily for two reasons: First, models make testable predictions that can be explored by experimentalists as well as theoreticians, second, models have practical and technological applications of interest to the applied scie...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	48,231
1812.07106	E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs	Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The two major types are Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. It is a challenging task to have real-time, efficient, and accur...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	116,751
2306.11161	Neuro-Symbolic Bi-Directional Translation -- Deep Learning Explainability for Climate Tipping Point Research	In recent years, there has been an increase in using deep learning for climate and weather modeling. Though results have been impressive, explainability and interpretability of deep learning models are still a challenge. A third wave of Artificial Intelligence (AI), which includes logic and reasoning, has been describe...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	374,494
2409.08521	Optimal Classification-based Anomaly Detection with Neural Networks: Theory and Practice	Anomaly detection is an important problem in many application areas, such as network security. Many deep learning methods for unsupervised anomaly detection produce good empirical performance but lack theoretical guarantees. By casting anomaly detection into a binary classification problem, we establish non-asymptotic ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	487,943
1307.6321	An Uncertainty Principle for Discrete Signals	By use of window functions, time-frequency analysis tools like Short Time Fourier Transform overcome a shortcoming of the Fourier Transform and enable us to study the time- frequency characteristics of signals which exhibit transient os- cillatory behavior. Since the resulting representations depend on the choice of th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	26,015
2105.11412	Reproducibility Report: Contextualizing Hate Speech Classifiers with Post-hoc Explanation	The presented report evaluates Contextualizing Hate Speech Classifiers with Post-hoc Explanation paper within the scope of ML Reproducibility Challenge 2020. Our work focuses on both aspects constituting the paper: the method itself and the validity of the stated results. In the following sections, we have described th...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	236,692
2010.01242	Improving Network Slimming with Nonconvex Regularization	Convolutional neural networks (CNNs) have developed to become powerful models for various computer vision tasks ranging from object detection to semantic segmentation. However, most of the state-of-the-art CNNs cannot be deployed directly on edge devices such as smartphones and drones, which need low latency under limi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	198,573
2501.09798	Computing Optimization-Based Prompt Injections Against Closed-Weights Models By Misusing a Fine-Tuning API	We surface a new threat to closed-weight Large Language Models (LLMs) that enables an attacker to compute optimization-based prompt injections. Specifically, we characterize how an attacker can leverage the loss-like information returned from the remote fine-tuning interface to guide the search for adversarial prompts....	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	525,275
2405.14260	Graph Sparsification via Mixture of Graphs	Graph Neural Networks (GNNs) have demonstrated superior performance across various graph learning tasks but face significant computational challenges when applied to large-scale graphs. One effective approach to mitigate these challenges is graph sparsification, which involves removing non-essential edges to reduce com...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	456,339
2411.01442	Online Relational Inference for Evolving Multi-agent Interacting Systems	We introduce a novel framework, Online Relational Inference (ORI), designed to efficiently identify hidden interaction graphs in evolving multi-agent interacting systems using streaming data. Unlike traditional offline methods that rely on a fixed training set, ORI employs online backpropagation, updating the model wit...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	505,074
2306.05476	A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor Segmentation	Magnetic resonance imaging (MRI) is a commonly used technique for brain tumor segmentation, which is critical for evaluating patients and planning treatment. To make the labeling process less laborious and dependent on expertise, weakly-supervised semantic segmentation (WSSS) methods using class activation mapping (CAM...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	372,209
2211.13904	Policy-Adaptive Estimator Selection for Off-Policy Evaluation	Off-policy evaluation (OPE) aims to accurately evaluate the performance of counterfactual policies using only offline logged data. Although many estimators have been developed, there is no single estimator that dominates the others, because the estimators' accuracy can vary greatly depending on a given OPE task such as...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	332,643
1502.06084	A Privacy-Preserving QoS Prediction Framework for Web Service Recommendation	QoS-based Web service recommendation has recently gained much attention for providing a promising way to help users find high-quality services. To facilitate such recommendations, existing studies suggest the use of collaborative filtering techniques for personalized QoS prediction. These approaches, by leveraging part...	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	40,449
1404.3442	Optimal versus Nash Equilibrium Computation for Networked Resource Allocation	Motivated by emerging resource allocation and data placement problems such as web caches and peer-to-peer systems, we consider and study a class of resource allocation problems over a network of agents (nodes). In this model, nodes can store only a limited number of resources while accessing the remaining ones through ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	32,309
2001.09938	Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning	Innovations in batteries take years to formulate and commercialize, requiring extensive experimentation during the design and optimization phases. We approached the design and selection of a battery electrolyte through a black-box optimization algorithm directly integrated into a robotic test-stand. We report here the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	161,704
0901.1683	New Bounds for Binary and Ternary Overloaded CDMA	In this paper, we study binary and ternary matrices that are used for CDMA applications that are injective on binary or ternary user vectors. In other words, in the absence of additive noise, the interference of overloaded CDMA can be removed completely. Some new algorithms are proposed for constructing such matrices. ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	2,928
2012.09542	Weakly-Supervised Action Localization and Action Recognition using Global-Local Attention of 3D CNN	3D Convolutional Neural Network (3D CNN) captures spatial and temporal information on 3D data such as video sequences. However, due to the convolution and pooling mechanism, the information loss seems unavoidable. To improve the visual explanations and classification in 3D CNN, we propose two approaches; i) aggregate l...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	false	false	212,104
2109.03009	Sequential Attention Module for Natural Language Processing	Recently, large pre-trained neural language models have attained remarkable performance on many downstream natural language processing (NLP) applications via fine-tuning. In this paper, we target at how to further improve the token representations on the language models. We, therefore, propose a simple yet effective pl...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	253,928
2401.12992	TranSentence: Speech-to-speech Translation via Language-agnostic Sentence-level Speech Encoding without Language-parallel Data	Although there has been significant advancement in the field of speech-to-speech translation, conventional models still require language-parallel speech data between the source and target languages for training. In this paper, we introduce TranSentence, a novel speech-to-speech translation without language-parallel spe...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	423,574
2411.05548	Equivariant IMU Preintegration with Biases: a Galilean Group Approach	This letter proposes a new approach for Inertial Measurement Unit (IMU) preintegration, a fundamental building block that can be leveraged in different optimization-based Inertial Navigation System (INS) localization solutions. Inspired by recent advances in equivariant theory applied to biased INSs, we derive a discre...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	506,705
2110.15952	On the Performance of Multihop THz Wireless System Over Mixed Channel Fading with Shadowing and Antenna Misalignment	The existing relay-assisted terahertz (THz) wireless system is limited to dual-hop transmission with pointing errors and short-term fading without considering the shadowing effect. This paper analyzes the performance of a multihop-assisted backhaul communication mixed with an access link under the shadowed fading with ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	264,052
2412.06617	AI TrackMate: Finally, Someone Who Will Give Your Music More Than Just "Sounds Great!"	The rise of "bedroom producers" has democratized music creation, while challenging producers to objectively evaluate their work. To address this, we present AI TrackMate, an LLM-based music chatbot designed to provide constructive feedback on music productions. By combining LLMs' inherent musical knowledge with direct ...	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	515,303
2204.11227	Lesion Localization in OCT by Semi-Supervised Object Detection	Over 300 million people worldwide are affected by various retinal diseases. By noninvasive Optical Coherence Tomography (OCT) scans, a number of abnormal structural changes in the retina, namely retinal lesions, can be identified. Automated lesion localization in OCT is thus important for detecting retinal diseases at ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	293,076
2410.02148	Reducing Warning Errors in Driver Support with Personalized Risk Maps	We consider the problem of human-focused driver support. State-of-the-art personalization concepts allow to estimate parameters for vehicle control systems or driver models. However, there are currently few approaches proposed that use personalized models and evaluate the effectiveness in the form of general risk warni...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	494,132
1805.02800	Potential Negative Impact on Reliability of Distributed Generation under Temporary Faults	This paper uncovers potential negative impacts on the SAIFI reliability index produced by the installation of Distributed Generation (DG) in distribution networks when subjected to temporary faults. Detailed network modeling produces accurate time-domain simulations which show how the negative effects on reliability in...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	96,917
2308.02935	Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems	This paper conducts fairness testing of automated pedestrian detection, a crucial but under-explored issue in autonomous driving systems. We evaluate eight state-of-the-art deep learning-based pedestrian detectors across demographic groups on large-scale real-world datasets. To enable thorough fairness testing, we prov...	false	false	false	false	true	false	false	false	false	false	false	true	false	true	false	false	false	true	383,829
1910.14243	DiaNet: BERT and Hierarchical Attention Multi-Task Learning of Fine-Grained Dialect	Prediction of language varieties and dialects is an important language processing task, with a wide range of applications. For Arabic, the native tongue of ~ 300 million people, most varieties remain unsupported. To ease this bottleneck, we present a very large scale dataset covering 319 cities from all 21 Arab countri...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	151,601
2502.10706	Raising the Bar in Graph OOD Generalization: Invariant Learning Beyond Explicit Environment Modeling	Out-of-distribution (OOD) generalization has emerged as a critical challenge in graph learning, as real-world graph data often exhibit diverse and shifting environments that traditional models fail to generalize across. A promising solution to address this issue is graph invariant learning (GIL), which aims to learn in...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	534,011
1709.02556	Game Theory Models for the Verification of the Collective Behaviour of Autonomous Cars	The collective of autonomous cars is expected to generate almost optimal traffic. In this position paper we discuss the multi-agent models and the verification results of the collective behaviour of autonomous cars. We argue that non-cooperative autonomous adaptation cannot guarantee optimal behaviour. The conjecture i...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	80,294
1803.03745	Evolutionary Architecture Search For Deep Multitask Networks	Multitask learning, i.e. learning several tasks at once with the same neural network, can improve performance in each of the tasks. Designing deep neural network architectures for multitask learning is a challenge: There are many ways to tie the tasks together, and the design choices matter. The size and complexity of ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	92,313
2501.18361	Video-based Surgical Tool-tip and Keypoint Tracking using Multi-frame Context-driven Deep Learning Models	Automated tracking of surgical tool keypoints in robotic surgery videos is an essential task for various downstream use cases such as skill assessment, expertise assessment, and the delineation of safety zones. In recent years, the explosion of deep learning for vision applications has led to many works in surgical ins...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	528,671
2303.08312	Interference-Aware Constellation Design for Z-Interference Channels with Imperfect CSI	A deep autoencoder (DAE)-based end-to-end communication over the two-user Z-interference channel (ZIC) with finite-alphabet inputs is designed in this paper. The design is for imperfect channel state information (CSI) where both estimation and quantization errors exist. The proposed structure jointly optimizes the enco...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	351,591
1805.05086	Unsupervised Intuitive Physics from Visual Observations	While learning models of intuitive physics is an increasingly active area of research, current approaches still fall short of natural intelligences in one important regard: they require external supervision, such as explicit access to physical states, at training and sometimes even at test times. Some authors have rela...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	97,375
1908.09892	Does BERT agree? Evaluating knowledge of structure dependence through agreement relations	Learning representations that accurately model semantics is an important goal of natural language processing research. Many semantic phenomena depend on syntactic structure. Recent work examines the extent to which state-of-the-art models for pre-training representations, such as BERT, capture such structure-dependent ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	142,957
2005.11235	Predicting Video features from EEG and Vice versa	In this paper we explore predicting facial or lip video features from electroencephalography (EEG) features and predicting EEG features from recorded facial or lip video frames using deep learning models. The subjects were asked to read out loud English sentences shown to them on a computer screen and their simultaneou...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	178,417
2411.18623	Lift3D Foundation Policy: Lifting 2D Large-Scale Pretrained Models for Robust 3D Robotic Manipulation	3D geometric information is essential for manipulation tasks, as robots need to perceive the 3D environment, reason about spatial relationships, and interact with intricate spatial configurations. Recent research has increasingly focused on the explicit extraction of 3D features, while still facing challenges such as t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	511,933
2311.07928	Towards Improving Robustness Against Common Corruptions in Object Detectors Using Adversarial Contrastive Learning	Neural networks have revolutionized various domains, exhibiting remarkable accuracy in tasks like natural language processing and computer vision. However, their vulnerability to slight alterations in input samples poses challenges, particularly in safety-critical applications like autonomous driving. Current approache...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	407,522
1802.09941	Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis	Deep Neural Networks (DNNs) are becoming an important tool in modern computing applications. Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design. In this survey, we describe the problem from a theoretical perspective, followed by approaches for i...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	true	91,426
2308.03102	Learning-Rate-Free Learning: Dissecting D-Adaptation and Probabilistic Line Search	This paper explores two recent methods for learning rate optimisation in stochastic gradient descent: D-Adaptation (arXiv:2301.07733) and probabilistic line search (arXiv:1502.02846). These approaches aim to alleviate the burden of selecting an initial learning rate by incorporating distance metrics and Gaussian proces...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	383,898
2404.09510	Listen to the Waves: Using a Neuronal Model of the Human Auditory System to Predict Ocean Waves	Artificial neural networks (ANNs) have evolved from the 1940s primitive models of brain function to become tools for artificial intelligence. They comprise many units, artificial neurons, interlinked through weighted connections. ANNs are trained to perform tasks through learning rules that modify the connection weight...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	446,710
2010.13659	Exploiting Neural Query Translation into Cross Lingual Information Retrieval	As a crucial role in cross-language information retrieval (CLIR), query translation has three main challenges: 1) the adequacy of translation; 2) the lack of in-domain parallel training data; and 3) the requisite of low latency. To this end, existing CLIR systems mainly exploit statistical-based machine translation (SM...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	203,206
2412.18241	An Automatic Graph Construction Framework based on Large Language Models for Recommendation	Graph neural networks (GNNs) have emerged as state-of-the-art methods to learn from graph-structured data for recommendation. However, most existing GNN-based recommendation methods focus on the optimization of model structures and learning strategies based on pre-defined graphs, neglecting the importance of the graph ...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	520,335
2208.11311	Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments	In federated learning, all networked clients contribute to the model training cooperatively. However, with model sizes increasing, even sharing the trained partial models often leads to severe communication bottlenecks in underlying networks, especially when communicated iteratively. In this paper, we introduce a feder...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	314,380
2307.10183	Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless Telecommunication Performance	The pervasive nature of wireless telecommunication has made it the foundation for mainstream technologies like automation, smart vehicles, virtual reality, and unmanned aerial vehicles. As these technologies experience widespread adoption in our daily lives, ensuring the reliable performance of cellular networks in mob...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	380,445
2502.03327	Is In-Context Universality Enough? MLPs are Also Universal In-Context	The success of transformers is often linked to their ability to perform in-context learning. Recent work shows that transformers are universal in context, capable of approximating any real-valued continuous function of a context (a probability measure over $\mathcal{X}\subseteq \mathbb{R}^d$) and a query $x\in \mathcal...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	530,672
2106.03160	Multi-agent Modeling of Hazard-Household-Infrastructure Nexus for Equitable Resilience Assessment	To enable integrating social equity considerations in infrastructure resilience assessments, this study created a new computational multi-agent simulation model which enables integrated assessment of hazard, infrastructure system, and household elements and their interactions. With a focus on hurricane-induced power ou...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	239,204
2412.16935	Detecting and Classifying Defective Products in Images Using YOLO	With the continuous advancement of industrial automation, product quality inspection has become increasingly important in the manufacturing process. Traditional inspection methods, which often rely on manual checks or simple machine vision techniques, suffer from low efficiency and insufficient accuracy. In recent year...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	519,757
2412.10975	Recursive Aggregates as Intensional Functions in Answer Set Programming: Semantics and Strong Equivalence	This paper shows that the semantics of programs with aggregates implemented by the solvers clingo and dlv can be characterized as extended First-Order formulas with intensional functions in the logic of Here-and-There. Furthermore, this characterization can be used to study the strong equivalence of programs with aggre...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	517,196
2005.14354	UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content	Recent years have witnessed an explosion of user-generated content (UGC) videos shared and streamed over the Internet, thanks to the evolution of affordable and reliable consumer capture devices, and the tremendous popularity of social media platforms. Accordingly, there is a great need for accurate video quality asses...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	179,243
2010.15578	Exploring the Nuances of Designing (with/for) Artificial Intelligence	Solutions relying on artificial intelligence are devised to predict data patterns and answer questions that are clearly defined, involve an enumerable set of solutions, clear rules, and inherently binary decision mechanisms. Yet, as they become exponentially implemented in our daily activities, they begin to transcend ...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	203,811
2402.12874	Skill or Luck? Return Decomposition via Advantage Functions	Learning from off-policy data is essential for sample-efficient reinforcement learning. In the present work, we build on the insight that the advantage function can be understood as the causal effect of an action on the return, and show that this allows us to decompose the return of a trajectory into parts caused by th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	431,030
1411.4952	From Captions to Visual Concepts and Back	This paper presents a novel approach for automatically generating image descriptions: visual detectors, language models, and multimodal similarity models learnt directly from a dataset of image captions. We use multiple instance learning to train visual detectors for words that commonly occur in captions, including man...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	37,690
1411.1580	Guaranteeing Positive Secrecy Capacity with Finite-Rate Feedback using Artificial Noise	While the impact of finite-rate feedback on the capacity of fading channels has been extensively studied in the literature, not much attention has been paid to this problem under secrecy constraint. In this work, we study the ergodic secret capacity of a multiple-input multiple-output multiple-antenna-eavesdropper (MIM...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	37,355
1812.09903	Adaptive Confidence Smoothing for Generalized Zero-Shot Learning	Generalized zero-shot learning (GZSL) is the problem of learning a classifier where some classes have samples and others are learned from side information, like semantic attributes or text description, in a zero-shot learning fashion (ZSL). Training a single model that operates in these two regimes simultaneously is ch...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	117,255
2406.18397	Second Maximum of a Gaussian Random Field and Exact (t-)Spacing test	In this article, we introduce the novel concept of the second maximum of a Gaussian random field on a Riemannian submanifold. This second maximum serves as a powerful tool for characterizing the distribution of the maximum. By utilizing an ad-hoc Kac Rice formula, we derive the explicit form of the maximum's distributi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	467,997
2010.09252	Dimsum @LaySumm 20: BART-based Approach for Scientific Document Summarization	Lay summarization aims to generate lay summaries of scientific papers automatically. It is an essential task that can increase the relevance of science for all of society. In this paper, we build a lay summary generation system based on the BART model. We leverage sentence labels as extra supervision signals to improve...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	201,464
2310.14556	S3Aug: Segmentation, Sampling, and Shift for Action Recognition	Action recognition is a well-established area of research in computer vision. In this paper, we propose S3Aug, a video data augmenatation for action recognition. Unlike conventional video data augmentation methods that involve cutting and pasting regions from two videos, the proposed method generates new videos from a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	401,914
2502.08436	From Haystack to Needle: Label Space Reduction for Zero-shot Classification	We present Label Space Reduction (LSR), a novel method for improving zero-shot classification performance of Large Language Models (LLMs). LSR iteratively refines the classification label space by systematically ranking and reducing candidate classes, enabling the model to concentrate on the most relevant options. By l...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	533,012
2108.11014	iDARTS: Improving DARTS by Node Normalization and Decorrelation Discretization	Differentiable ARchiTecture Search (DARTS) uses a continuous relaxation of network representation and dramatically accelerates Neural Architecture Search (NAS) by almost thousands of times in GPU-day. However, the searching process of DARTS is unstable, which suffers severe degradation when training epochs become large...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	252,061
1803.09733	Domain transfer convolutional attribute embedding	In this paper, we study the problem of transfer learning with the attribute data. In the transfer learning problem, we want to leverage the data of the auxiliary and the target domains to build an effective model for the classification problem in the target domain. Meanwhile, the attributes are naturally stable cross d...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	93,566
2407.13520	EaDeblur-GS: Event assisted 3D Deblur Reconstruction with Gaussian Splatting	3D deblurring reconstruction techniques have recently seen significant advancements with the development of Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). Although these techniques can recover relatively clear 3D reconstructions from blurry image inputs, they still face limitations in handling severe b...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	474,406
2204.09792	Assessing Machine Learning Algorithms for Near-Real Time Bus Ridership Prediction During Extreme Weather	Given an increasingly volatile climate, the relationship between weather and transit ridership has drawn increasing interest. However, challenges stemming from spatio-temporal dependency and non-stationarity have not been fully addressed in modelling and predicting transit ridership under the influence of weather condi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	292,560
2203.00101	ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction	In this paper, we present ApacheJIT, a large dataset for Just-In-Time defect prediction. ApacheJIT consists of clean and bug-inducing software changes in popular Apache projects. ApacheJIT has a total of 106,674 commits (28,239 bug-inducing and 78,435 clean commits). Having a large number of commits makes ApacheJIT a s...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	282,868
2311.02552	IPVNet: Learning Implicit Point-Voxel Features for Open-Surface 3D Reconstruction	Reconstruction of 3D open surfaces (e.g., non-watertight meshes) is an underexplored area of computer vision. Recent learning-based implicit techniques have removed previous barriers by enabling reconstruction in arbitrary resolutions. Yet, such approaches often rely on distinguishing between the inside and outside of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	405,486
1902.00842	Real-Time Freespace Segmentation on Autonomous Robots for Detection of Obstacles and Drop-Offs	Mobile robots navigating in indoor and outdoor environments must be able to identify and avoid unsafe terrain. Although a significant amount of work has been done on the detection of standing obstacles (solid obstructions), not much work has been done on the detection of negative obstacles (e.g. dropoffs, ledges, downw...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	120,522
2112.04766	Adaptive Methods for Aggregated Domain Generalization	Domain generalization involves learning a classifier from a heterogeneous collection of training sources such that it generalizes to data drawn from similar unknown target domains, with applications in large-scale learning and personalized inference. In many settings, privacy concerns prohibit obtaining domain labels f...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	270,640
2202.07654	Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation	The predictions of question answering (QA)systems are typically evaluated against manually annotated finite sets of one or more answers. This leads to a coverage limitation that results in underestimating the true performance of systems, and is typically addressed by extending over exact match (EM) with pre-defined rul...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	280,621
1701.07262	On the Error Probability of Short Concatenated Polar and Cyclic Codes with Interleaving	In this paper, the analysis of the performance of the concatenation of a short polar code with an outer binary linear block code is addressed from a distance spectrum viewpoint. The analysis targets the case where an outer cyclic code is employed together with an inner systematic polar code. A concatenated code ensembl...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	67,261
2205.12050	Training Efficient CNNS: Tweaking the Nuts and Bolts of Neural Networks for Lighter, Faster and Robust Models	Deep Learning has revolutionized the fields of computer vision, natural language understanding, speech recognition, information retrieval and more. Many techniques have evolved over the past decade that made models lighter, faster, and robust with better generalization. However, many deep learning practitioners persist...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	298,388
2406.17438	Implicit-Zoo: A Large-Scale Dataset of Neural Implicit Functions for 2D Images and 3D Scenes	Neural implicit functions have demonstrated significant importance in various areas such as computer vision, graphics. Their advantages include the ability to represent complex shapes and scenes with high fidelity, smooth interpolation capabilities, and continuous representations. Despite these benefits, the developmen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	467,572
1403.6036	Adaptive MCMC-Based Inference in Probabilistic Logic Programs	Probabilistic Logic Programming (PLP) languages enable programmers to specify systems that combine logical models with statistical knowledge. The inference problem, to determine the probability of query answers in PLP, is intractable in general, thereby motivating the need for approximate techniques. In this paper, we ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	31,786
2407.15881	Data Sharing for Mean Estimation Among Heterogeneous Strategic Agents	We study a collaborative learning problem where $m$ agents estimate a vector $\mu\in\mathbb{R}^d$ by collecting samples from normal distributions, with each agent $i$ incurring a cost $c_{i,k} \in (0, \infty]$ to sample from the $k^{\text{th}}$ distribution $\mathcal{N}(\mu_k, \sigma^2)$. Instead of working on their ow...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	475,388
2306.12609	Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities	There is increasing attention being given to how to regulate AI systems. As governing bodies grapple with what values to encapsulate into regulation, we consider the technical half of the question: To what extent can AI experts vet an AI system for adherence to regulatory requirements? We investigate this question thro...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	374,994
2309.12276	LLMR: Real-time Prompting of Interactive Worlds using Large Language Models	We present Large Language Model for Mixed Reality (LLMR), a framework for the real-time creation and modification of interactive Mixed Reality experiences using LLMs. LLMR leverages novel strategies to tackle difficult cases where ideal training data is scarce, or where the design goal requires the synthesis of interna...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	393,727
2408.12683	New Bounds on Quantum Sample Complexity of Measurement Classes	This paper studies quantum supervised learning for classical inference from quantum states. In this model, a learner has access to a set of labeled quantum samples as the training set. The objective is to find a quantum measurement that predicts the label of the unseen samples. The hardness of learning is measured via ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	482,843
2109.07509	Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis	Visual sentiment analysis has received increasing attention in recent years. However, the dataset's quality is a concern because the sentiment labels are crowd-sourcing, subjective, and prone to mistakes, and poses a severe threat to the data-driven models, especially the deep neural networks. The deep models would gen...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	255,549
1502.02137	A class of cyclic codes whose dual have five zeros	In this paper, a family of cyclic codes over $\mathbb{F}_{p}$ whose duals have five zeros is presented, where $p$ is an odd prime. Furthermore, the weight distributions of these cyclic codes are determined.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	40,002
1906.03525	Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentation	In this paper, we propose a novel Pattern-Affinitive Propagation (PAP) framework to jointly predict depth, surface normal and semantic segmentation. The motivation behind it comes from the statistic observation that pattern-affinitive pairs recur much frequently across different tasks as well as within a task. Thus, we...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	134,405
2110.04002	Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network	Capturing users' precise preferences is of great importance in various recommender systems (eg., e-commerce platforms), which is the basis of how to present personalized interesting product lists to individual users. In spite of significant progress has been made to consider relations between users and items, most of t...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	259,711

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