Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
2204.08916	Heterogeneous Feature Augmentation for Ponzi Detection in Ethereum	While blockchain technology triggers new industrial and technological revolutions, it also brings new challenges. Recently, a large number of new scams with a "blockchain" sock-puppet continue to emerge, such as Ponzi schemes, money laundering, etc., seriously threatening financial security. Existing fraud detection me...	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	292,253
2204.00106	A Survey of Robust 3D Object Detection Methods in Point Clouds	The purpose of this work is to review the state-of-the-art LiDAR-based 3D object detection methods, datasets, and challenges. We describe novel data augmentation methods, sampling strategies, activation functions, attention mechanisms, and regularization methods. Furthermore, we list recently introduced normalization m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	289,134
2104.14753	Studying the Consistency and Composability of Lottery Ticket Pruning Masks	Magnitude pruning is a common, effective technique to identify sparse subnetworks at little cost to accuracy. In this work, we ask whether a particular architecture's accuracy-sparsity tradeoff can be improved by combining pruning information across multiple runs of training. From a shared ResNet-20 initialization, we ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	232,930
2409.08687	xTED: Cross-Domain Adaptation via Diffusion-Based Trajectory Editing	Reusing pre-collected data from different domains is an appealing solution for decision-making tasks, especially when data in the target domain are limited. Existing cross-domain policy transfer methods mostly aim at learning domain correspondences or corrections to facilitate policy learning, such as learning task/dom...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	488,019
1610.00493	Pooling Hybrid Representations for Web Structured Data Annotation	Automatically identifying data types of web structured data is a key step in the process of web data integration. Web structured data is usually associated with entities or objects in a particular domain. In this paper, we aim to map attributes of an entity in a given domain to pre-specified classes of attributes in th...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	61,842
1810.11597	Groupcast Index Coding Problem: Joint Extensions	The groupcast index coding problem is the most general version of the classical index coding problem, where any receiver can demand messages that are also demanded by other receivers. Any groupcast index coding problem is described by its \emph{fitting matrix} which contains unknown entries along with $1$'s and $0$'s. ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	111,541
1402.4306	Student-t Processes as Alternatives to Gaussian Processes	We investigate the Student-t process as an alternative to the Gaussian process as a nonparametric prior over functions. We derive closed form expressions for the marginal likelihood and predictive distribution of a Student-t process, by integrating away an inverse Wishart process prior over the covariance kernel of a G...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	30,949
2210.05443	QuCNN : A Quantum Convolutional Neural Network with Entanglement Based Backpropagation	Quantum Machine Learning continues to be a highly active area of interest within Quantum Computing. Many of these approaches have adapted classical approaches to the quantum settings, such as QuantumFlow, etc. We push forward this trend and demonstrate an adaption of the Classical Convolutional Neural Networks to quant...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	322,854
2501.01652	MIRAGE: Exploring How Large Language Models Perform in Complex Social Interactive Environments	Large Language Models (LLMs) have shown remarkable capabilities in environmental perception, reasoning-based decision-making, and simulating complex human behaviors, particularly in interactive role-playing contexts. This paper introduces the Multiverse Interactive Role-play Ability General Evaluation (MIRAGE), a compr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	522,154
1906.06023	Utilizing the Instability in Weakly Supervised Object Detection	Weakly supervised object detection (WSOD) focuses on training object detector with only image-level annotations, and is challenging due to the gap between the supervision and the objective. Most of existing approaches model WSOD as a multiple instance learning (MIL) problem. However, we observe that the result of MIL b...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	135,189
2303.05177	Assistive Robot Teleoperation Using Behavior Trees	Robotic assistance in robot arm teleoperation tasks has recently gained a lot of traction in industrial and domestic environment. A wide variety of input devices is used in such setups. Due to the noise in the input signals (e.g., Brain Computer Interface (BCI)) or delays due to environmental conditions (e.g., space ro...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	350,373
1707.00489	A note on computing range space bases of rational matrices	We discuss computational procedures based on descriptor state-space realizations to compute proper range space bases of rational matrices. The main computation is the orthogonal reduction of the system matrix pencil to a special Kronecker-like form, which allows to extract a full column rank factor, whose columns form ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	76,361
2009.08876	Multi-modal Experts Network for Autonomous Driving	End-to-end learning from sensory data has shown promising results in autonomous driving. While employing many sensors enhances world perception and should lead to more robust and reliable behavior of autonomous vehicles, it is challenging to train and deploy such network and at least two problems are encountered in the...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	196,375
2406.10682	Inverse Kinematics with Vision-Based Constraints	This paper introduces the Visual Inverse Kinematics problem (VIK) to fill the gap between robot Inverse Kinematics (IK) and visual servo control. Different from the IK problem, the VIK problem seeks to find robot configurations subject to vision-based constraints, in addition to kinematic constraints. In this work, we ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	464,507
2305.12600	PRODIGY: Enabling In-context Learning Over Graphs	In-context learning is the ability of a pretrained model to adapt to novel and diverse downstream tasks by conditioning on prompt examples, without optimizing any parameters. While large language models have demonstrated this ability, how in-context learning could be performed over graphs is unexplored. In this paper, ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	366,081
1508.01880	Feedback and Partial Message Side-Information on the Semideterministic Broadcast Channel	The capacity of the semideterministic discrete memoryless broadcast channel (SD-BC) with partial message side-information (P-MSI) at the receivers is established. In the setting without a common message, it is shown that P-MSI to the stochastic receiver alone can increase capacity, whereas P-MSI to the deterministic re...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	45,833
1608.04104	What Information Really Matters in Supervisor Reduction?	To make a supervisor comprehensible to a layman has been a long-lasting goal in the supervisory control community. One strategy is to reduce the size of a supervisor to generate a control equivalent version, whose size is hopefully much smaller than the original one so that a user or control designer can easily check w...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	59,778
2410.11710	MTU-Bench: A Multi-granularity Tool-Use Benchmark for Large Language Models	Large Language Models (LLMs) have displayed massive improvements in reasoning and decision-making skills and can hold natural conversations with users. Recently, many tool-use benchmark datasets have been proposed. However, existing datasets have the following limitations: (1). Insufficient evaluation scenarios (e.g., ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	498,682
2204.03634	Class-Incremental Learning with Strong Pre-trained Models	Class-incremental learning (CIL) has been widely studied under the setting of starting from a small number of classes (base classes). Instead, we explore an understudied real-world setting of CIL that starts with a strong model pre-trained on a large number of base classes. We hypothesize that a strong base model can p...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	290,374
2307.10650	Language-Enhanced Session-Based Recommendation with Decoupled Contrastive Learning	Session-based recommendation techniques aim to capture dynamic user behavior by analyzing past interactions. However, existing methods heavily rely on historical item ID sequences to extract user preferences, leading to challenges such as popular bias and cold-start problems. In this paper, we propose a hybrid multimod...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	380,634
2311.07790	Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning	We address two major challenges in scientific machine learning (SciML): interpretability and computational efficiency. We increase the interpretability of certain learning processes by establishing a new theoretical connection between optimization problems arising from SciML and a generalized Hopf formula, which repres...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	407,472
2412.18873	Cross-PCR: A Robust Cross-Source Point Cloud Registration Framework	Due to the density inconsistency and distribution difference between cross-source point clouds, previous methods fail in cross-source point cloud registration. We propose a density-robust feature extraction and matching scheme to achieve robust and accurate cross-source registration. To address the density inconsistenc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	520,616
2411.06090	Concept Bottleneck Language Models For protein design	We introduce Concept Bottleneck Protein Language Models (CB-pLM), a generative masked language model with a layer where each neuron corresponds to an interpretable concept. Our architecture offers three key benefits: i) Control: We can intervene on concept values to precisely control the properties of generated protein...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	506,961
2309.16071	Influence Pathway Discovery on Social Media	This paper addresses influence pathway discovery, a key emerging problem in today's online media. We propose a discovery algorithm that leverages recently published work on unsupervised interpretable ideological embedding, a mapping of ideological beliefs (done in a self-supervised fashion) into interpretable low-dimen...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	395,204
2210.08729	VoxelCache: Accelerating Online Mapping in Robotics and 3D Reconstruction Tasks	Real-time 3D mapping is a critical component in many important applications today including robotics, AR/VR, and 3D visualization. 3D mapping involves continuously fusing depth maps obtained from depth sensors in phones, robots, and autonomous vehicles into a single 3D representative model of the scene. Many important ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	324,264
1912.05027	SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization	Convolutional neural networks typically encode an input image into a series of intermediate features with decreasing resolutions. While this structure is suited to classification tasks, it does not perform well for tasks requiring simultaneous recognition and localization (e.g., object detection). The encoder-decoder a...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	156,989
1804.07587	ClaimRank: Detecting Check-Worthy Claims in Arabic and English	We present ClaimRank, an online system for detecting check-worthy claims. While originally trained on political debates, the system can work for any kind of text, e.g., interviews or regular news articles. Its aim is to facilitate manual fact-checking efforts by prioritizing the claims that fact-checkers should conside...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	95,556
2310.18936	Adversarial Examples Are Not Real Features	The existence of adversarial examples has been a mystery for years and attracted much interest. A well-known theory by \citet{ilyas2019adversarial} explains adversarial vulnerability from a data perspective by showing that one can extract non-robust features from adversarial examples and these features alone are useful...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	403,777
2104.05031	Deformable Capsules for Object Detection	Capsule networks promise significant benefits over convolutional networks by storing stronger internal representations, and routing information based on the agreement between intermediate representations' projections. Despite this, their success has been limited to small-scale classification datasets due to their compu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	229,577
2412.04111	Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data	Gliomas, a kind of brain tumor characterized by high mortality, present substantial diagnostic challenges in low- and middle-income countries, particularly in Sub-Saharan Africa. This paper introduces a novel approach to glioma segmentation using transfer learning to address challenges in resource-limited regions with ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	514,265
1307.5934	A Near-Optimal Dynamic Learning Algorithm for Online Matching Problems with Concave Returns	We consider an online matching problem with concave returns. This problem is a significant generalization of the Adwords allocation problem and has vast applications in online advertising. In this problem, a sequence of items arrive sequentially and each has to be allocated to one of the bidders, who bid a certain valu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	25,991
2407.12415	Not All Frequencies Are Created Equal:Towards a Dynamic Fusion of Frequencies in Time-Series Forecasting	Long-term time series forecasting is a long-standing challenge in various applications. A central issue in time series forecasting is that methods should expressively capture long-term dependency. Furthermore, time series forecasting methods should be flexible when applied to different scenarios. Although Fourier analy...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	473,918
2108.09645	Improving Mini-batch Optimal Transport via Partial Transportation	Mini-batch optimal transport (m-OT) has been widely used recently to deal with the memory issue of OT in large-scale applications. Despite their practicality, m-OT suffers from misspecified mappings, namely, mappings that are optimal on the mini-batch level but are partially wrong in the comparison with the optimal tra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	251,671
1801.09471	Social Influence (Deep) Learning for Human Behavior Prediction	Influence propagation in social networks has recently received large interest. In fact, the understanding of how influence propagates among subjects in a social network opens the way to a growing number of applications. Many efforts have been made to quantitatively measure the influence probability between pairs of sub...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	89,116
2105.03684	Quantum Machine Learning For Classical Data	In this dissertation, we study the intersection of quantum computing and supervised machine learning algorithms, which means that we investigate quantum algorithms for supervised machine learning that operate on classical data. This area of research falls under the umbrella of quantum machine learning, a research area ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	234,231
1902.09065	NOMA for VLC Downlink Transmission with Random Receiver Orientation	Visible light communications (VLC) is an emerging technology with a promise of viable solution to spectrum crunch problem in conventional radio frequency (RF) bands. In this work, we consider a downlink multiuser VLC network where users randomly change their location and vertical orientation. In order to increase the s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	122,332
2408.14203	Efficient FGM optimization with a novel design space and DeepONet	This manuscript proposes an optimization framework to find the tailor-made functionally graded material (FGM) profiles for thermoelastic applications. This optimization framework consists of (1) a random profile generation scheme, (2) deep learning (DL) based surrogate models for the prediction of thermal and structura...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	483,455
2412.16530	Improving Lip-synchrony in Direct Audio-Visual Speech-to-Speech Translation	Audio-Visual Speech-to-Speech Translation typically prioritizes improving translation quality and naturalness. However, an equally critical aspect in audio-visual content is lip-synchrony-ensuring that the movements of the lips match the spoken content-essential for maintaining realism in dubbed videos. Despite its imp...	false	false	true	false	false	false	false	false	true	false	false	true	false	false	false	false	false	true	519,570
1706.04097	Provable Alternating Gradient Descent for Non-negative Matrix Factorization with Strong Correlations	Non-negative matrix factorization is a basic tool for decomposing data into the feature and weight matrices under non-negativity constraints, and in practice is often solved in the alternating minimization framework. However, it is unclear whether such algorithms can recover the ground-truth feature matrix when the wei...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	75,277
2307.07066	Proof of Training (PoT): Harnessing Crypto Mining Power for Distributed AI Training	In the midst of the emerging trend of integrating artificial intelligence (AI) with crypto mining, we identify three major challenges that create a gap between these two fields. To bridge this gap, we introduce the proof-of-training (PoT) protocol, an approach that combines the strengths of both AI and blockchain techn...	false	true	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	379,270
2310.02751	SHOT: Suppressing the Hessian along the Optimization Trajectory for Gradient-Based Meta-Learning	In this paper, we hypothesize that gradient-based meta-learning (GBML) implicitly suppresses the Hessian along the optimization trajectory in the inner loop. Based on this hypothesis, we introduce an algorithm called SHOT (Suppressing the Hessian along the Optimization Trajectory) that minimizes the distance between th...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	396,981
2410.10171	Generative Human Video Compression with Multi-granularity Temporal Trajectory Factorization	In this paper, we propose a novel Multi-granularity Temporal Trajectory Factorization framework for generative human video compression, which holds great potential for bandwidth-constrained human-centric video communication. In particular, the proposed motion factorization strategy can facilitate to implicitly characte...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	497,952
2408.15046	Distributed Planning for Rigid Robot Formations with Probabilistic Collision Avoidance	This paper presents a distributed method for robots moving in rigid formations while ensuring probabilistic collision avoidance between the robots. The formation is parametrised through the transformation of a base configuration. The robots map their desired velocities into a corresponding desired change in the formati...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	483,779
2310.12541	Large Language Model for Multi-objective Evolutionary Optimization	Multiobjective evolutionary algorithms (MOEAs) are major methods for solving multiobjective optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of which the search operators need a carefully handcrafted design with domain knowledge. Recently, some attempts have been made to replace the manu...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	false	true	401,062
2301.03983	On the Performance of Dual RIS-assisted V2I Communication under Nakagami-m Fading	Vehicle-to-everything (V2X) connectivity in 5G-and-beyond communication networks supports the futuristic intelligent transportation system (ITS) by allowing vehicles to intelligently connect with everything. The advent of reconfigurable intelligent surfaces (RISs) has led to realizing the true potential of V2X communic...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	339,933
2308.05737	Follow Anything: Open-set detection, tracking, and following in real-time	Tracking and following objects of interest is critical to several robotics use cases, ranging from industrial automation to logistics and warehousing, to healthcare and security. In this paper, we present a robotic system to detect, track, and follow any object in real-time. Our approach, dubbed ``follow anything'' (FA...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	384,890
2102.00519	The Zero Cubes Free and Cubes Unique Multidimensional Constraints	This paper studies two families of constraints for two-dimensional and multidimensional arrays. The first family requires that a multidimensional array will not contain a cube of zeros of some fixed size and the second constraint imposes that there will not be two identical cubes of a given size in the array. These con...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	217,813
2409.10504	DILA: Dictionary Label Attention for Mechanistic Interpretability in High-dimensional Multi-label Medical Coding Prediction	Predicting high-dimensional or extreme multilabels, such as in medical coding, requires both accuracy and interpretability. Existing works often rely on local interpretability methods, failing to provide comprehensive explanations of the overall mechanism behind each label prediction within a multilabel set. We propose...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	488,765
2204.13814	An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks	In today's modern world, the usage of technology is unavoidable and the rapid advances in the Internet and communication fields have resulted to expand the Wireless Sensor Network (WSN) technology. A huge number of sensing devices collect and/or generate numerous sensory data throughout time for a wide range of fields ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	293,946
2110.12976	Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks	Deep neural networks (DNNs) are well-known to be vulnerable to adversarial attacks, where malicious human-imperceptible perturbations are included in the input to the deep network to fool it into making a wrong classification. Recent studies have demonstrated that neural Ordinary Differential Equations (ODEs) are intri...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	263,031
1911.09982	HybridNetSeg: A Compact Hybrid Network for Retinal Vessel Segmentation	A large number of retinal vessel analysis methods based on image segmentation have emerged in recent years. However, existing methods depend on cumbersome backbones, such as VGG16 and ResNet-50, benefiting from their powerful feature extraction capabilities but suffering from high computational costs. In this paper, we...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	154,692
2105.07396	Developing an Architecture Method Library	Today, there are millions of professionals worldwide acting as a designer, architect or engineer in the design, realization, and implementation of information systems. At this moment there is no well established and clearly identified body of knowledge that defines their profession in a "standard" way. In this article,...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	235,419
1908.06006	Bidirectional Context-Aware Hierarchical Attention Network for Document Understanding	The Hierarchical Attention Network (HAN) has made great strides, but it suffers a major limitation: at level 1, each sentence is encoded in complete isolation. In this work, we propose and compare several modifications of HAN in which the sentence encoder is able to make context-aware attentional decisions (CAHAN). Fur...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	141,880
2412.09722	GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers	The effectiveness of large language models (LLMs) is closely tied to the design of prompts, making prompt optimization essential for enhancing their performance across a wide range of tasks. Many existing approaches to automating prompt engineering rely exclusively on textual feedback, refining prompts based solely on ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	516,612
2202.10875	Data-Consistent Local Superresolution for Medical Imaging	In this work we propose a new paradigm of iterative model-based reconstruction algorithms for providing real-time solution for zooming-in and refining a region of interest in medical and clinical tomographic (such as CT/MRI/PET, etc) images. This algorithmic framework is tailor for a clinical need in medical imaging pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	281,696
1910.00170	How To Catch A Lion In The Desert -- On The Solution Of The Coverage Directed Generation (CDG) Problem	The testing and verification of a complex hardware or software system, such as modern integrated circuits (ICs) found in everything from smartphones to servers, can be a difficult process. One of the most difficult and time-consuming tasks a verification team faces is reaching coverage closure, or hitting all events in...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	147,595
1803.02269	Personalized Exposure Control Using Adaptive Metering and Reinforcement Learning	We propose a reinforcement learning approach for real-time exposure control of a mobile camera that is personalizable. Our approach is based on Markov Decision Process (MDP). In the camera viewfinder or live preview mode, given the current frame, our system predicts the change in exposure so as to optimize the trade-of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	92,036
2107.03949	Task Fingerprinting for Meta Learning in Biomedical Image Analysis	Shortage of annotated data is one of the greatest bottlenecks in biomedical image analysis. Meta learning studies how learning systems can increase in efficiency through experience and could thus evolve as an important concept to overcome data sparsity. However, the core capability of meta learning-based approaches is ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	245,300
1910.11009	A Graph-Based Framework to Bridge Movies and Synopses	Inspired by the remarkable advances in video analytics, research teams are stepping towards a greater ambition -- movie understanding. However, compared to those activity videos in conventional datasets, movies are significantly different. Generally, movies are much longer and consist of much richer temporal structures...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	150,653
1712.07473	Differentially Private Distributed Learning for Language Modeling Tasks	One of the big challenges in machine learning applications is that training data can be different from the real-world data faced by the algorithm. In language modeling, users' language (e.g. in private messaging) could change in a year and be completely different from what we observe in publicly available data. At the ...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	87,051
1905.07259	Texture Fields: Learning Texture Representations in Function Space	In recent years, substantial progress has been achieved in learning-based reconstruction of 3D objects. At the same time, generative models were proposed that can generate highly realistic images. However, despite this success in these closely related tasks, texture reconstruction of 3D objects has received little atte...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	131,184
2212.03023	FretNet: Continuous-Valued Pitch Contour Streaming for Polyphonic Guitar Tablature Transcription	In recent years, the task of Automatic Music Transcription (AMT), whereby various attributes of music notes are estimated from audio, has received increasing attention. At the same time, the related task of Multi-Pitch Estimation (MPE) remains a challenging but necessary component of almost all AMT approaches, even if ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	334,971
2112.14683	StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2	Videos show continuous events, yet most $-$ if not all $-$ video synthesis frameworks treat them discretely in time. In this work, we think of videos of what they should be $-$ time-continuous signals, and extend the paradigm of neural representations to build a continuous-time video generator. For this, we first desig...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	273,586
2303.00923	On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction	Helpful reviews have been essential for the success of e-commerce services, as they help customers make quick purchase decisions and benefit the merchants in their sales. While many reviews are informative, others provide little value and may contain spam, excessive appraisal, or unexpected biases. With the large volum...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	348,754
2203.10294	From meaning to perception -- exploring the space between word and odor perception embeddings	In this paper we propose the use of the Word2vec algorithm in order to obtain odor perception embeddings (or smell embeddings), only using publicly available perfume descriptions. Besides showing meaningful similarity relationships among each other, these embeddings also demonstrate to possess some shared information w...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	286,479
2311.01614	Alleviating the Curse of Dimensionality in Minkowski Sum Approximations of Storage Flexibility	Many real-world applications require the joint optimization of a large number of flexible devices over time. The flexibility of, e.g., multiple batteries, thermostatically controlled loads, or electric vehicles can be used to support grid operation and to reduce operation costs. Using piecewise constant power values, t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	405,105
2411.08284	Dynamic Thresholding Algorithm with Memory for Linear Inverse Problems	The relaxed optimal $k$-thresholding pursuit (ROTP) is a recent algorithm for linear inverse problems. This algorithm is based on the optimal $k$-thresholding technique which performs vector thresholding and error metric reduction simultaneously. Although ROTP can be used to solve small to medium-sized linear inverse p...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	507,831
2411.16787	Contrastive Multi-graph Learning with Neighbor Hierarchical Sifting for Semi-supervised Text Classification	Graph contrastive learning has been successfully applied in text classification due to its remarkable ability for self-supervised node representation learning. However, explicit graph augmentations may lead to a loss of semantics in the contrastive views. Secondly, existing methods tend to overlook edge features and th...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	511,181
2309.02177	A Quantitative Method to Determine What Collisions Are Reasonably Foreseeable and Preventable	The development of Automated Driving Systems (ADSs) has made significant progress in the last years. To enable the deployment of Automated Vehicles (AVs) equipped with such ADSs, regulations concerning the approval of these systems need to be established. In 2021, the World Forum for Harmonization of Vehicle Regulation...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	389,959
2101.10809	Exploring Transfer Learning on Face Recognition of Dark Skinned, Low Quality and Low Resource Face Data	There is a big difference in the tone of color of skin between dark and light skinned people. Despite this fact, most face recognition tasks almost all classical state-of-the-art models are trained on datasets containing an overwhelming majority of light skinned face images. It is tedious to collect a huge amount of da...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	217,060
2101.11818	Contagion-Preserving Network Sparsifiers: Exploring Epidemic Edge Importance Utilizing Effective Resistance	Network epidemiology has become a vital tool in understanding the effects of high-degree vertices, geographic and demographic communities, and other inhomogeneities in social structure on the spread of disease. However, many networks derived from modern datasets are quite dense, such as mobility networks where each loc...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	217,400
2311.15356	Having Second Thoughts? Let's hear it	Deep learning models loosely mimic bottom-up signal pathways from low-order sensory areas to high-order cognitive areas. After training, DL models can outperform humans on some domain-specific tasks, but their decision-making process has been known to be easily disrupted. Since the human brain consists of multiple func...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	410,482
1210.8193	Decision dynamics in complex networks subject to mass media and social contact transmission mechanisms	The dynamics of decisions in complex networks is studied within a Markov process framework using numerical simulations combined with mathematical insight into the process mechanisms. A mathematical discrete-time model is derived based on a set of basic assumptions on the convincing mechanisms associated to two opinions...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	19,477
2210.15638	LyricJam Sonic: A Generative System for Real-Time Composition and Musical Improvisation	Electronic music artists and sound designers have unique workflow practices that necessitate specialized approaches for developing music information retrieval and creativity support tools. Furthermore, electronic music instruments, such as modular synthesizers, have near-infinite possibilities for sound creation and ca...	false	false	true	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	327,031
1310.4210	Demystifying Information-Theoretic Clustering	We propose a novel method for clustering data which is grounded in information-theoretic principles and requires no parametric assumptions. Previous attempts to use information theory to define clusters in an assumption-free way are based on maximizing mutual information between data and cluster labels. We demonstrate ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	27,798
1903.10742	Generative Tensor Network Classification Model for Supervised Machine Learning	Tensor network (TN) has recently triggered extensive interests in developing machine-learning models in quantum many-body Hilbert space. Here we purpose a generative TN classification (GTNC) approach for supervised learning. The strategy is to train the generative TN for each class of the samples to construct the class...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	125,355
2010.06040	Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model	Masked Language Model (MLM) framework has been widely adopted for self-supervised language pre-training. In this paper, we argue that randomly sampled masks in MLM would lead to undesirably large gradient variance. Thus, we theoretically quantify the gradient variance via correlating the gradient covariance with the Ha...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	200,339
2110.14764	Generalized Funnelling: Ensemble Learning and Heterogeneous Document Embeddings for Cross-Lingual Text Classification	\emph{Funnelling} (Fun) is a recently proposed method for cross-lingual text classification (CLTC) based on a two-tier learning ensemble for heterogeneous transfer learning (HTL). In this ensemble method, 1st-tier classifiers, each working on a different and language-dependent feature space, return a vector of calibrat...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	263,625
2502.00242	Digital-Twin assisted Network Energy Optimization during Low Traffic Hours	As wireless network technology advances towards the sixth generation (6G), increasing network energy consumption has become a critical concern due to the growing demand for diverse services, radio deployments at various frequencies, larger bandwidths, and more antennas. Network operators must manage energy usage not on...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	529,274
2205.10365	A Correlation Information-based Spatiotemporal Network for Traffic Flow Forecasting	The technology of traffic flow forecasting plays an important role in intelligent transportation systems. Based on graph neural networks and attention mechanisms, most previous works utilize the transformer architecture to discover spatiotemporal dependencies and dynamic relationships. However, they have not considered...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	297,675
2209.14115	Deep learning for gradient flows using the Brezis-Ekeland principle	We propose a deep learning method for the numerical solution of partial differential equations that arise as gradient flows. The method relies on the Brezis--Ekeland principle, which naturally defines an objective function to be minimized, and so is ideally suited for a machine learning approach using deep neural netwo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	320,146
2105.14058	Symmetry-driven graph neural networks	Exploiting symmetries and invariance in data is a powerful, yet not fully exploited, way to achieve better generalisation with more efficiency. In this paper, we introduce two graph network architectures that are equivariant to several types of transformations affecting the node coordinates. First, we build equivarianc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	237,501
2306.15517	Enhancing Navigation Benchmarking and Perception Data Generation for Row-based Crops in Simulation	Service robotics is recently enhancing precision agriculture enabling many automated processes based on efficient autonomous navigation solutions. However, data generation and infield validation campaigns hinder the progress of large-scale autonomous platforms. Simulated environments and deep visual perception are spre...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	376,047
2106.10866	Customizing Graph Neural Networks using Path Reweighting	Graph Neural Networks (GNNs) have been extensively used for mining graph-structured data with impressive performance. However, because these traditional GNNs do not distinguish among various downstream tasks, embeddings embedded by them are not always effective. Intuitively, paths in a graph imply different semantics f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	242,188
2305.00769	Multi-scale Transformer-based Network for Emotion Recognition from Multi Physiological Signals	This paper presents an efficient Multi-scale Transformer-based approach for the task of Emotion recognition from Physiological data, which has gained widespread attention in the research community due to the vast amount of information that can be extracted from these signals using modern sensors and machine learning te...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	361,447
2309.11960	A Comprehensive Review on Financial Explainable AI	The success of artificial intelligence (AI), and deep learning models in particular, has led to their widespread adoption across various industries due to their ability to process huge amounts of data and learn complex patterns. However, due to their lack of explainability, there are significant concerns regarding thei...	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	393,605
2306.05083	Revealing the Blind Spot of Sentence Encoder Evaluation by HEROS	Existing sentence textual similarity benchmark datasets only use a single number to summarize how similar the sentence encoder's decision is to humans'. However, it is unclear what kind of sentence pairs a sentence encoder (SE) would consider similar. Moreover, existing SE benchmarks mainly consider sentence pairs with...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	372,047
2407.17816	NC-NCD: Novel Class Discovery for Node Classification	Novel Class Discovery (NCD) involves identifying new categories within unlabeled data by utilizing knowledge acquired from previously established categories. However, existing NCD methods often struggle to maintain a balance between the performance of old and new categories. Discovering unlabeled new categories in a cl...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	476,128
2007.07203	Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations	One of the core problems in large-scale recommendations is to retrieve top relevant candidates accurately and efficiently, preferably in sub-linear time. Previous approaches are mostly based on a two-step procedure: first learn an inner-product model, and then use some approximate nearest neighbor (ANN) search algorith...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	187,256
2408.01112	Agentic LLM Workflows for Generating Patient-Friendly Medical Reports	The application of Large Language Models (LLMs) in healthcare is expanding rapidly, with one potential use case being the translation of formal medical reports into patient-legible equivalents. Currently, LLM outputs often need to be edited and evaluated by a human to ensure both factual accuracy and comprehensibility,...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	478,108
1912.13203	Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey	The advancements in smart power grid and the advocation of ``green communications'' have inspired the wireless communication networks to harness energy from ambient environments and operate in an energy-efficient manner for economic and ecological benefits. This article presents a contemporary review of recent breakthr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	159,034
2407.10377	Enhanced Masked Image Modeling to Avoid Model Collapse on Multi-modal MRI Datasets	Multi-modal magnetic resonance imaging (MRI) provides information of lesions for computer-aided diagnosis from different views. Deep learning algorithms are suitable for identifying specific anatomical structures, segmenting lesions, and classifying diseases. Manual labels are limited due to the high expense, which hin...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	472,963
2403.12088	TMU at TREC Clinical Trials Track 2023	This paper describes Toronto Metropolitan University's participation in the TREC Clinical Trials Track for 2023. As part of the tasks, we utilize advanced natural language processing techniques and neural language models in our experiments to retrieve the most relevant clinical trials. We illustrate the overall methodo...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	439,030
1703.06941	A Unified Effective Capacity Performance Analysis of Lp-norm Diversity Reception over Arbitrary and Correlated Generalized Fading Channels	The effective capacity (EC) has been recently established as a rigorous alternative to the classical Shannon's ergodic capacity since it accounts for the delay constraints imposed by future wireless applications and their impact on the overall system performance. This paper presents a novel moment generating function (...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	70,305
2006.06668	Disentangled Non-Local Neural Networks	The non-local block is a popular module for strengthening the context modeling ability of a regular convolutional neural network. This paper first studies the non-local block in depth, where we find that its attention computation can be split into two terms, a whitened pairwise term accounting for the relationship betw...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	181,517
2306.03877	The Eater and the Mover Game	This paper studies the idea of ``deception by motion'' through a two-player dynamic game played between a Mover who must retrieve resources at a goal location, and an Eater who can consume resources at two candidate goals. The Mover seeks to minimize the resource consumption at the true goal, and the Eater tries to max...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	371,513
2410.03859	SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?	Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench (Jimenez et al., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem st...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	495,010
2010.09473	Double-Linear Thompson Sampling for Context-Attentive Bandits	In this paper, we analyze and extend an online learning framework known as Context-Attentive Bandit, motivated by various practical applications, from medical diagnosis to dialog systems, where due to observation costs only a small subset of a potentially large number of context variables can be observed at each iterat...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	201,554
2211.16701	Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation	Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels. Addressing that, we propose a novel learning approach, called Conservative-Progressive Collabo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	333,713
2212.04037	Demystifying Prompts in Language Models via Perplexity Estimation	Language models can be prompted to perform a wide variety of zero- and few-shot learning problems. However, performance varies significantly with the choice of prompt, and we do not yet understand why this happens or how to pick the best prompts. In this work, we analyze the factors that contribute to this variance and...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	335,299

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