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
2310.02903	FroSSL: Frobenius Norm Minimization for Efficient Multiview Self-Supervised Learning	Self-supervised learning (SSL) is a popular paradigm for representation learning. Recent multiview methods can be classified as sample-contrastive, dimension-contrastive, or asymmetric network-based, with each family having its own approach to avoiding informational collapse. While these families converge to solutions ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	397,035
2407.16326	On The Expressive Power of Knowledge Graph Embedding Methods	Knowledge Graph Embedding (KGE) is a popular approach, which aims to represent entities and relations of a knowledge graph in latent spaces. Their representations are known as embeddings. To measure the plausibility of triplets, score functions are defined over embedding spaces. Despite wide dissemination of KGE in var...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	475,559
2401.13627	Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild	We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up. Leveraging multi-modal techniques and advanced generative prior, SUPIR marks a significant advance in intelligent and realistic image restoration. As a pivotal ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	423,801
2201.10463	Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality	Medical entity extraction (EE) is a standard procedure used as a first stage in medical texts processing. Usually Medical EE is a two-step process: named entity recognition (NER) and named entity normalization (NEN). We propose a novel method of doing medical EE from electronic health records (EHR) as a single-step mul...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	277,003
2411.11233	Noise Filtering Benchmark for Neuromorphic Satellites Observations	Event cameras capture sparse, asynchronous brightness changes which offer high temporal resolution, high dynamic range, low power consumption, and sparse data output. These advantages make them ideal for Space Situational Awareness, particularly in detecting resident space objects moving within a telescope's field of v...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	508,971
2307.06007	Building Persuasive Robots with Social Power Strategies	Can social power endow social robots with the capacity to persuade? This paper represents our recent endeavor to design persuasive social robots. We have designed and run three different user studies to investigate the effectiveness of different bases of social power (inspired by French and Raven's theory) on peoples' ...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	378,936
0809.0918	Intersecting random graphs and networks with multiple adjacency constraints: A simple example	When studying networks using random graph models, one is sometimes faced with situations where the notion of adjacency between nodes reflects multiple constraints. Traditional random graph models are insufficient to handle such situations. A simple idea to account for multiple constraints consists in taking the inter...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,292
2210.07313	Bootstrapping Multilingual Semantic Parsers using Large Language Models	Despite cross-lingual generalization demonstrated by pre-trained multilingual models, the translate-train paradigm of transferring English datasets across multiple languages remains to be a key mechanism for training task-specific multilingual models. However, for many low-resource languages, the availability of a reli...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	323,649
2206.07851	Conformal prediction set for time-series	When building either prediction intervals for regression (with real-valued response) or prediction sets for classification (with categorical responses), uncertainty quantification is essential to studying complex machine learning methods. In this paper, we develop Ensemble Regularized Adaptive Prediction Set (ERAPS) to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	302,911
1511.06488	Resiliency of Deep Neural Networks under Quantization	The complexity of deep neural network algorithms for hardware implementation can be much lowered by optimizing the word-length of weights and signals. Direct quantization of floating-point weights, however, does not show good performance when the number of bits assigned is small. Retraining of quantized networks has be...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	49,270
0910.0651	A Simpler Approach to Matrix Completion	This paper provides the best bounds to date on the number of randomly sampled entries required to reconstruct an unknown low rank matrix. These results improve on prior work by Candes and Recht, Candes and Tao, and Keshavan, Montanari, and Oh. The reconstruction is accomplished by minimizing the nuclear norm, or sum of...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	4,626
1806.09038	Deductron -- A Recurrent Neural Network	The current paper is a study in Recurrent Neural Networks (RNN), motivated by the lack of examples simple enough so that they can be thoroughly understood theoretically, but complex enough to be realistic. We constructed an example of structured data, motivated by problems from image-to-text conversion (OCR), which req...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	101,277
2312.16557	Joint empirical risk minimization for instance-dependent positive-unlabeled data	Learning from positive and unlabeled data (PU learning) is actively researched machine learning task. The goal is to train a binary classification model based on a training dataset containing part of positives which are labeled, and unlabeled instances. Unlabeled set includes remaining part of positives and all negativ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	418,428
2403.16937	Hyperspherical Classification with Dynamic Label-to-Prototype Assignment	Aiming to enhance the utilization of metric space by the parametric softmax classifier, recent studies suggest replacing it with a non-parametric alternative. Although a non-parametric classifier may provide better metric space utilization, it introduces the challenge of capturing inter-class relationships. A shared ch...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	441,249
2303.07068	n-Step Temporal Difference Learning with Optimal n	We consider the problem of finding the optimal value of n in the n-step temporal difference (TD) learning algorithm. Our objective function for the optimization problem is the average root mean squared error (RMSE). We find the optimal n by resorting to a model-free optimization technique involving a one-simulation sim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	351,103
2003.08818	Brain MRI-based 3D Convolutional Neural Networks for Classification of Schizophrenia and Controls	Convolutional Neural Network (CNN) has been successfully applied on classification of both natural images and medical images but not yet been applied to differentiating patients with schizophrenia from healthy controls. Given the subtle, mixed, and sparsely distributed brain atrophy patterns of schizophrenia, the capab...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	168,879
1805.04033	Regularizing Output Distribution of Abstractive Chinese Social Media Text Summarization for Improved Semantic Consistency	Abstractive text summarization is a highly difficult problem, and the sequence-to-sequence model has shown success in improving the performance on the task. However, the generated summaries are often inconsistent with the source content in semantics. In such cases, when generating summaries, the model selects semantica...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	97,164
1802.09786	Preferential attachment mechanism of complex network growth: "rich-gets-richer" or "fit-gets-richer"?	We analyze the growth models for complex networks including preferential attachment (A.-L. Barabasi and R. Albert, Science 286, 509 (1999)) and fitness model (Caldarelli et al., Phys. Rev. Lett. 89, 258702 (2002)) and demonstrate that, under very general conditions, these two models yield the same dynamic equation of n...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	91,392
1111.4232	A Model of Spatial Thinking for Computational Intelligence	Trying to be effective (no matter who exactly and in what field) a person face the problem which inevitably destroys all our attempts to easily get to a desired goal. The problem is the existence of some insuperable barriers for our mind, anotherwords barriers for principles of thinking. They are our clue and main reas...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	13,070
1809.02031	Planning with Arithmetic and Geometric Attributes	A desirable property of an intelligent agent is its ability to understand its environment to quickly generalize to novel tasks and compose simpler tasks into more complex ones. If the environment has geometric or arithmetic structure, the agent should exploit these for faster generalization. Building on recent work tha...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	106,942
2502.09449	Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects	Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking Neural Networks (SNNs) excel in handling such data with high efficiency, owing to their rich neuronal dynamics and sparse activity...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	533,453
2412.03791	Coordinate In and Value Out: Training Flow Transformers in Ambient Space	Flow matching models have emerged as a powerful method for generative modeling on domains like images or videos, and even on unstructured data like 3D point clouds. These models are commonly trained in two stages: first, a data compressor (i.e., a variational auto-encoder) is trained, and in a subsequent training stage...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	514,105
2307.05631	Causal Kripke Models	This work extends Halpern and Pearl's causal models for actual causality to a possible world semantics environment. Using this framework we introduce a logic of actual causality with modal operators, which allows for reasoning about causality in scenarios involving multiple possibilities, temporality, knowledge and unc...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	378,809
2011.11788	Stabilizing Queuing Networks with Model Data-Independent Control	Classical queuing network control strategies typically rely on accurate knowledge of model data, i.e., arrival and service rates. However, such data are not always available and may be time-variant. To address this challenge, we consider a class of model data-independent (MDI) control policies that only rely on traffic...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	207,941
2005.02954	Multitask Models for Supervised Protests Detection in Texts	The CLEF 2019 ProtestNews Lab tasks participants to identify text relating to political protests within larger corpora of news data. Three tasks include article classification, sentence detection, and event extraction. I apply multitask neural networks capable of producing predictions for two and three of these tasks s...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	176,016
2305.13413	Syntactic Knowledge via Graph Attention with BERT in Machine Translation	Although the Transformer model can effectively acquire context features via a self-attention mechanism, deeper syntactic knowledge is still not effectively modeled. To alleviate the above problem, we propose Syntactic knowledge via Graph attention with BERT (SGB) in Machine Translation (MT) scenarios. Graph Attention N...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	366,493
1910.09856	On the Beneficial Role of a Finite Number of Scatterers for Wireless Physical Layer Security	We show that for a legitimate communication under multipath quasi-static fading with a reduced number of scatterers, it is possible to achieve perfect secrecy even in the presence of a passive eavesdropper for which no channel state information is available. Specifically, we show that the outage probability of secrecy ...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	150,320
2304.09184	Frequency Enhanced Hybrid Attention Network for Sequential Recommendation	The self-attention mechanism, which equips with a strong capability of modeling long-range dependencies, is one of the extensively used techniques in the sequential recommendation field. However, many recent studies represent that current self-attention based models are low-pass filters and are inadequate to capture hi...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	358,972
1907.11377	Deep Learning Detection of Inaccurate Smart Electricity Meters: A Case Study	Detecting inaccurate smart meters and targeting them for replacement can save significant resources. For this purpose, a novel deep-learning method was developed based on long short-term memory (LSTM) and a modified convolutional neural network (CNN) to predict electricity usage trajectories based on historical data. F...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	139,834
1207.4800	Finite Alphabet Iterative Decoders, Part I: Decoding Beyond Belief Propagation on BSC	We introduce a new paradigm for finite precision iterative decoding on low-density parity-check codes over the Binary Symmetric channel. The messages take values from a finite alphabet, and unlike traditional quantized decoders which are quantized versions of the Belief propagation (BP) decoder, the proposed finite alp...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	17,668
1810.06128	Regrasp Planning Considering Bipedal Stability Constraints	This paper presents a Center of Mass (CoM) based manipulation and regrasp planner that implements stability constraints to preserve the robot balance. The planner provides a graph of IK-feasible, collision-free and stable motion sequences, constructed using an energy based motion planning algorithm. It assures that the...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	110,383
1610.09087	Recent advances in content based video copy detection	With the immense number of videos being uploaded to the video sharing sites, issue of copyright infringement arises with uploading of illicit copies or transformed versions of original video. Thus safeguarding copyright of digital media has become matter of concern. To address this concern, it is obliged to have a vide...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	63,001
1005.2770	Capacity-Achieving Polar Codes for Arbitrarily-Permuted Parallel Channels	Channel coding over arbitrarily-permuted parallel channels was first studied by Willems et al. (2008). This paper introduces capacity-achieving polar coding schemes for arbitrarily-permuted parallel channels where the component channels are memoryless, binary-input and output-symmetric.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,499
1109.3563	Verification, Validation and Testing of Kinetic Mechanisms of Hydrogen Combustion in Fluid Dynamic Computations	A one-step, a two-step, an abridged, a skeletal and four detailed kinetic schemes of hydrogen oxidation have been tested. A new skeletal kinetic scheme of hydrogen oxidation has been developed. The CFD calculations were carried out using ANSYS CFX software. Ignition delay times and speeds of flames were derived from th...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	12,191
1108.5192	Positivity of the English language	Over the last million years, human language has emerged and evolved as a fundamental instrument of social communication and semiotic representation. People use language in part to convey emotional information, leading to the central and contingent questions: (1) What is the emotional spectrum of natural language? and (...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	11,819
2107.08955	From primary to dual affine variety codes over the Klein quartic	In [17] a novel method was established to estimate the minimum distance of primary affine variety codes and a thorough treatment of the Klein quartic led to the discovery of a family of primary codes with good parameters, the duals of which were originally treated in [23][Ex. 3.2, Ex. 4.1]. In the present work we trans...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	246,888
1508.06191	A Neuro-Fuzzy Method to Improving Backfiring Conversion Ratios	Software project estimation is crucial aspect in delivering software on time and on budget. Software size is an important metric in determining the effort, cost, and productivity. Today, source lines of code and function point are the most used sizing metrics. Backfiring is a well-known technique for converting between...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	46,304
2011.14721	Probabilistic Load Forecasting Based on Adaptive Online Learning	Load forecasting is crucial for multiple energy management tasks such as scheduling generation capacity, planning supply and demand, and minimizing energy trade costs. Such relevance has increased even more in recent years due to the integration of renewable energies, electric cars, and microgrids. Conventional load fo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	208,872
2112.07522	LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework	Vast efforts have been devoted to creating high-performance few-shot learners, i.e., large-scale pretrained language models (PLMs) that perform well with little downstream task training data. Training PLMs has incurred significant cost, but utilizing the few-shot learners is still challenging due to their enormous size...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	271,507
2406.10114	Task-aligned Part-aware Panoptic Segmentation through Joint Object-Part Representations	Part-aware panoptic segmentation (PPS) requires (a) that each foreground object and background region in an image is segmented and classified, and (b) that all parts within foreground objects are segmented, classified and linked to their parent object. Existing methods approach PPS by separately conducting object-level...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	464,229
2405.05275	SoMeR: Multi-View User Representation Learning for Social Media	User representation learning aims to capture user preferences, interests, and behaviors in low-dimensional vector representations. These representations have widespread applications in recommendation systems and advertising; however, existing methods typically rely on specific features like text content, activity patte...	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	false	452,866
2409.12350	Advancing Cucumber Disease Detection in Agriculture through Machine Vision and Drone Technology	This study uses machine vision and drone technologies to propose a unique method for the diagnosis of cucumber disease in agriculture. The backbone of this research is a painstakingly curated dataset of hyperspectral photographs acquired under genuine field conditions. Unlike earlier datasets, this study included a wid...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	489,544
2412.18725	On the Performance of Short Binary BCH Codes for Ultra-Low Latency Wireless Communications	In recent years, polar codes have been considered for communication systems that require high re-liability and ultra-low latency, such as sixth generation (6G) wireless communications. This paper presents simulation results showing that short binary extended BCH (eBCH) codes with low-complexity decoding outperform pola...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	520,542
1506.04924	Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation	We propose a novel deep neural network architecture for semi-supervised semantic segmentation using heterogeneous annotations. Contrary to existing approaches posing semantic segmentation as a single task of region-based classification, our algorithm decouples classification and segmentation, and learns a separate netw...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	44,237
2310.06904	Mitigating stereotypical biases in text to image generative systems	State-of-the-art generative text-to-image models are known to exhibit social biases and over-represent certain groups like people of perceived lighter skin tones and men in their outcomes. In this work, we propose a method to mitigate such biases and ensure that the outcomes are fair across different groups of people. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	398,754
2208.04600	IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation	Recent sequential recommendation models rely increasingly on consecutive short-term user-item interaction sequences to model user interests. These approaches have, however, raised concerns about both short- and long-term interests. (1) {\it short-term}: interaction sequences may not result from a monolithic interest, b...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	312,166
2410.00395	Performance Improvement of IaaS Type of Cloud Computing Using Virtualization Technique	Cloud computing has transformed the way organizations manage and scale their IT infrastructure by offering flexible, scalable, and cost-effective solutions. However, the Infrastructure as a Service (IaaS) model faces performance challenges primarily due to the limitations imposed by virtualization technology. This pape...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	493,358
1601.00720	How do neurons operate on sparse distributed representations? A mathematical theory of sparsity, neurons and active dendrites	We propose a formal mathematical model for sparse representations and active dendrites in neocortex. Our model is inspired by recent experimental findings on active dendritic processing and NMDA spikes in pyramidal neurons. These experimental and modeling studies suggest that the basic unit of pattern memory in the neo...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	50,670
2205.09389	Simplifying Node Classification on Heterophilous Graphs with Compatible Label Propagation	Graph Neural Networks (GNNs) have been predominant for graph learning tasks; however, recent studies showed that a well-known graph algorithm, Label Propagation (LP), combined with a shallow neural network can achieve comparable performance to GNNs in semi-supervised node classification on graphs with high homophily. I...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	297,253
1811.09003	On a Sparse Shortcut Topology of Artificial Neural Networks	In established network architectures, shortcut connections are often used to take the outputs of earlier layers as additional inputs to later layers. Despite the extraordinary effectiveness of shortcuts, there remain open questions on the mechanism and characteristics. For example, why are shortcuts powerful? Why do sh...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	114,169
2105.14172	A Stochastic Alternating Balance $k$-Means Algorithm for Fair Clustering	In the application of data clustering to human-centric decision-making systems, such as loan applications and advertisement recommendations, the clustering outcome might discriminate against people across different demographic groups, leading to unfairness. A natural conflict occurs between the cost of clustering (in t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	237,560
2305.20056	Rare Life Event Detection via Mobile Sensing Using Multi-Task Learning	Rare life events significantly impact mental health, and their detection in behavioral studies is a crucial step towards health-based interventions. We envision that mobile sensing data can be used to detect these anomalies. However, the human-centered nature of the problem, combined with the infrequency and uniqueness...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	369,798
2001.04758	Deep Audio-Visual Learning: A Survey	Audio-visual learning, aimed at exploiting the relationship between audio and visual modalities, has drawn considerable attention since deep learning started to be used successfully. Researchers tend to leverage these two modalities either to improve the performance of previously considered single-modality tasks or to ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	160,344
2110.02784	Cooperative Multi-Agent Actor-Critic for Privacy-Preserving Load Scheduling in a Residential Microgrid	As a scalable data-driven approach, multi-agent reinforcement learning (MARL) has made remarkable advances in solving the cooperative residential load scheduling problems. However, the common centralized training strategy of MARL algorithms raises privacy risks for involved households. In this work, we propose a privac...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	true	false	false	false	259,246
2209.04594	Unsupervised Domain Adaptation for Extra Features in the Target Domain Using Optimal Transport	Domain adaptation aims to transfer knowledge of labeled instances obtained from a source domain to a target domain to fill the gap between the domains. Most domain adaptation methods assume that the source and target domains have the same dimensionality. Methods that are applicable when the number of features is differ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	316,823
2202.06303	On the Exactness of an Energy-efficient Train Control model based on Convex Optimization	In this paper, we demonstrate the exactness proof for the energy-efficient train control (EETC) model based on convex optimization. The proof of exactness shows that the convex optimization model will share the same optimization results with the initial model on which the convex relaxations are conducted. We first show...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	280,176
2410.05931	Construction of Musculoskeletal Simulation for Shoulder Complex with Ligaments and Its Validation via Model Predictive Control	The complex ways in which humans utilize their bodies in sports and martial arts are remarkable, and human motion analysis is one of the most effective tools for robot body design and control. On the other hand, motion analysis is not easy, and it is difficult to measure complex body motions in detail due to the influe...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	495,978
2312.15626	RDF-star2Vec: RDF-star Graph Embeddings for Data Mining	Knowledge Graphs (KGs) such as Resource Description Framework (RDF) data represent relationships between various entities through the structure of triples (<subject, predicate, object>). Knowledge graph embedding (KGE) is crucial in machine learning applications, specifically in node classification and link prediction ...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	418,070
2405.12171	State of the Practice for Medical Imaging Software	We selected 29 medical imaging projects from 48 candidates, assessed 10 software qualities by answering 108 questions for each software project, and interviewed 8 of the 29 development teams. Based on the quantitative data, we ranked the MI software with the Analytic Hierarchy Process (AHP). The four top-ranked softwar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	455,424
2410.15155	Pipeline Gradient-based Model Training on Analog In-memory Accelerators	Aiming to accelerate the training of large deep neural models (DNN) in an energy-efficient way, an analog in-memory computing (AIMC) accelerator emerges as a solution with immense potential. In AIMC accelerators, trainable weights are kept in memory without the need to move from memory to processors during the training...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	500,393
1812.11712	On the Complexity of the Inverse Semivalue Problem for Weighted Voting Games	Weighted voting games are a family of cooperative games, typically used to model voting situations where a number of agents (players) vote against or for a proposal. In such games, a proposal is accepted if an appropriately weighted sum of the votes exceeds a prespecified threshold. As the influence of a player over th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	117,615
2306.14030	My Boli: Code-mixed Marathi-English Corpora, Pretrained Language Models and Evaluation Benchmarks	The research on code-mixed data is limited due to the unavailability of dedicated code-mixed datasets and pre-trained language models. In this work, we focus on the low-resource Indian language Marathi which lacks any prior work in code-mixing. We present L3Cube-MeCorpus, a large code-mixed Marathi-English (Mr-En) corp...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	375,503
2003.03186	Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning	One of the key factors of enabling machine learning models to comprehend and solve real-world tasks is to leverage multimodal data. Unfortunately, annotation of multimodal data is challenging and expensive. Recently, self-supervised multimodal methods that combine vision and language were proposed to learn multimodal r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	167,153
1605.05870	Interests Diffusion on a Semantic Multiplex	Exploiting the information about members of a Social Network (SN) represents one of the most attractive and dwelling subjects for both academic and applied scientists. The community of Complexity Science and especially those researchers working on multiplex social systems are devoting increasing efforts to outline gene...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	56,057
1210.6234	Experiments and Direct Numerical Simulations of binary collisions of miscible liquid droplets with different viscosities	Binary droplet collisions are of importance in a variety of practical applications comprising dispersed two-phase flows. The background of our research is the prediction of properties of particulate products formed in spray processes. To gain a more thorough understanding of the elementary sub-processes inside a spray,...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	19,348
2307.06322	Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data"	Crystalline defects, such as line-like dislocations, play an important role for the performance and reliability of many metallic devices. Their interaction and evolution still poses a multitude of open questions to materials science and materials physics. In-situ TEM experiments can provide important insights into how ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	379,032
2111.07383	Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space	As a basic component of SE(3)-equivariant deep feature learning, steerable convolution has recently demonstrated its advantages for 3D semantic analysis. The advantages are, however, brought by expensive computations on dense, volumetric data, which prevent its practical use for efficient processing of 3D data that are...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	266,350
1506.07362	Energy-Efficient 5G Outdoor-to-Indoor Communication: SUDAS Over Licensed and Unlicensed Spectrum	In this paper, we study the joint resource allocation algorithm design for downlink and uplink multicarrier transmission assisted by a shared user equipment (UE)-side distributed antenna system (SUDAS). The proposed SUDAS simultaneously utilizes licensed frequency bands and unlicensed frequency bands, (e.g. millimeter ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	44,511
2207.00048	Privacy-preserving Graph Analytics: Secure Generation and Federated Learning	Directly motivated by security-related applications from the Homeland Security Enterprise, we focus on the privacy-preserving analysis of graph data, which provides the crucial capacity to represent rich attributes and relationships. In particular, we discuss two directions, namely privacy-preserving graph generation a...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	305,619
2304.09116	NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers	Scaling text-to-speech (TTS) to large-scale, multi-speaker, and in-the-wild datasets is important to capture the diversity in human speech such as speaker identities, prosodies, and styles (e.g., singing). Current large TTS systems usually quantize speech into discrete tokens and use language models to generate these t...	false	false	true	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	358,948
1510.08172	Spectrally and Energy Efficient OFDM (SEE-OFDM) for Intensity Modulated Optical Wireless Systems	Spectrally and energy efficient orthogonal frequency division multiplexing (SEE-OFDM) is an optical OFDM technique based on combining multiple asymmetrically clipped optical OFDM (ACO-OFDM) signals into one OFDM signal. By summing different components together, SEE-OFDM can achieve the same spectral efficiency as DC-bi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	48,258
1010.0012	An Embarrassingly Simple Speed-Up of Belief Propagation with Robust Potentials	We present an exact method of greatly speeding up belief propagation (BP) for a wide variety of potential functions in pairwise MRFs and other graphical models. Specifically, our technique applies whenever the pairwise potentials have been {\em truncated} to a constant value for most pairs of states, as is commonly don...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	7,736
2402.19387	SeD: Semantic-Aware Discriminator for Image Super-Resolution	Generative Adversarial Networks (GANs) have been widely used to recover vivid textures in image super-resolution (SR) tasks. In particular, one discriminator is utilized to enable the SR network to learn the distribution of real-world high-quality images in an adversarial training manner. However, the distribution lear...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	433,782
1904.08643	Real-Time Style Transfer With Strength Control	Style transfer is a problem of rendering a content image in the style of another style image. A natural and common practical task in applications of style transfer is to adjust the strength of stylization. Algorithm of Gatys et al. (2016) provides this ability by changing the weighting factors of content and style loss...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	128,138
1409.5783	LDPC Code Density Evolution in the Error Floor Region	This short paper explores density evolution (DE) for low-density parity-check (LDPC) codes at signal-to-noise-ratios (SNRs) that are significantly above the decoding threshold. The focus is on the additive white Gaussian noise channel and LDPC codes in which the variable nodes have regular degree. Prior work, using D...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	36,195
2404.04883	Mixture of Low-rank Experts for Transferable AI-Generated Image Detection	Generative models have shown a giant leap in synthesizing photo-realistic images with minimal expertise, sparking concerns about the authenticity of online information. This study aims to develop a universal AI-generated image detector capable of identifying images from diverse sources. Existing methods struggle to gen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	444,838
2211.09039	UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction	Relational triple extraction is challenging for its difficulty in capturing rich correlations between entities and relations. Existing works suffer from 1) heterogeneous representations of entities and relations, and 2) heterogeneous modeling of entity-entity interactions and entity-relation interactions. Therefore, th...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	330,852
2401.01233	Graph Elimination Networks	Graph Neural Networks (GNNs) are widely applied across various domains, yet they perform poorly in deep layers. Existing research typically attributes this problem to node over-smoothing, where node representations become indistinguishable after multiple rounds of propagation. In this paper, we delve into the neighborh...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	419,283
2409.00214	Enhancing Document-level Argument Extraction with Definition-augmented Heuristic-driven Prompting for LLMs	Event Argument Extraction (EAE) is pivotal for extracting structured information from unstructured text, yet it remains challenging due to the complexity of real-world document-level EAE. We propose a novel Definition-augmented Heuristic-driven Prompting (DHP) method to enhance the performance of Large Language Models ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	484,827
2305.15913	MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization	Memes are a powerful tool for communication over social media. Their affinity for evolving across politics, history, and sociocultural phenomena makes them an ideal communication vehicle. To comprehend the subtle message conveyed within a meme, one must understand the background that facilitates its holistic assimilati...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	367,843
2202.05791	The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance	We study convergence rates of AdaGrad-Norm as an exemplar of adaptive stochastic gradient methods (SGD), where the step sizes change based on observed stochastic gradients, for minimizing non-convex, smooth objectives. Despite their popularity, the analysis of adaptive SGD lags behind that of non adaptive methods in th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	279,986
2407.16732	PyBench: Evaluating LLM Agent on various real-world coding tasks	The LLM Agent, equipped with a code interpreter, is capable of automatically solving real-world coding tasks, such as data analysis and image editing. However, existing benchmarks primarily focus on either simplistic tasks, such as completing a few lines of code, or on extremely complex and specific tasks at the repo...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	475,710
2305.13197	Challenging Decoder helps in Masked Auto-Encoder Pre-training for Dense Passage Retrieval	Recently, various studies have been directed towards exploring dense passage retrieval techniques employing pre-trained language models, among which the masked auto-encoder (MAE) pre-training architecture has emerged as the most promising. The conventional MAE framework relies on leveraging the passage reconstruction o...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	366,395
2201.05213	Parallel Neural Local Lossless Compression	The recently proposed Neural Local Lossless Compression (NeLLoC), which is based on a local autoregressive model, has achieved state-of-the-art (SOTA) out-of-distribution (OOD) generalization performance in the image compression task. In addition to the encouragement of OOD generalization, the local model also allows p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	275,314
1307.6125	Interference alignment using finite and dependent channel extensions: the single beam case	Vector space interference alignment (IA) is known to achieve high degrees of freedom (DoF) with infinite independent channel extensions, but its performance is largely unknown for a finite number of possibly dependent channel extensions. In this paper, we consider a $K$-user $M_t \times M_r$ MIMO interference channel (...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	26,004
2004.01972	Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks	We study multi-turn response generation for open-domain dialogues. The existing state-of-the-art addresses the problem with deep neural architectures. While these models improved response quality, their complexity also hinders the application of the models in real systems. In this work, we pursue a model that has a sim...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	171,073
2103.00238	Color-Coded Symbology and New Computer Vision Tool to Predict the Historical Color Pallets of the Renaissance Oil Artworks	In this paper, we discuss possible color palletes, prediction and analysis of originality of the colors that Artists used on the Renaissance oil paintings. This framework goal is to help to use the color symbology and image enhancement tools, to predict the historical color palletes of the Renaissance oil artworks. Thi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	222,202
2303.00326	Empowering Networks With Scale and Rotation Equivariance Using A Similarity Convolution	The translational equivariant nature of Convolutional Neural Networks (CNNs) is a reason for its great success in computer vision. However, networks do not enjoy more general equivariance properties such as rotation or scaling, ultimately limiting their generalization performance. To address this limitation, we devise ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	348,563
2201.11086	Can Old TREC Collections Reliably Evaluate Modern Neural Retrieval Models?	Neural retrieval models are generally regarded as fundamentally different from the retrieval techniques used in the late 1990's when the TREC ad hoc test collections were constructed. They thus provide the opportunity to empirically test the claim that pooling-built test collections can reliably evaluate retrieval syst...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	277,176
2108.03348	Global Self-Attention as a Replacement for Graph Convolution	We propose an extension to the transformer neural network architecture for general-purpose graph learning by adding a dedicated pathway for pairwise structural information, called edge channels. The resultant framework - which we call Edge-augmented Graph Transformer (EGT) - can directly accept, process and output stru...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	249,625
2010.05427	Towards Expressive Graph Representation	Graph Neural Network (GNN) aggregates the neighborhood of each node into the node embedding and shows its powerful capability for graph representation learning. However, most existing GNN variants aggregate the neighborhood information in a fixed non-injective fashion, which may map different graphs or nodes to the sam...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	200,124
2011.03750	Multi-Antenna Data-Driven Eavesdropping Attacks and Symbol-Level Precoding Countermeasures	In this work, we consider secure communications in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve). In this setting, we exploit machine learning (ML) tools to design soft and hard decoding schemes by using precoded pilot symb...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	205,342
2407.05154	Identifying Intensity of the Structure and Content in Tweets and the Discriminative Power of Attributes in Context with Referential Translation Machines	We use referential translation machines (RTMs) to identify the similarity between an attribute and two words in English by casting the task as machine translation performance prediction (MTPP) between the words and the attribute word and the distance between their similarities for Task 10 with stacked RTM models. RTMs ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	470,845
2210.10196	BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds	Audio denoising has been explored for decades using both traditional and deep learning-based methods. However, these methods are still limited to either manually added artificial noise or lower denoised audio quality. To overcome these challenges, we collect a large-scale natural noise bird sound dataset. We are the fi...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	324,818
2401.08374	Cross-lingual neural fuzzy matching for exploiting target-language monolingual corpora in computer-aided translation	Computer-aided translation (CAT) tools based on translation memories (MT) play a prominent role in the translation workflow of professional translators. However, the reduced availability of in-domain TMs, as compared to in-domain monolingual corpora, limits its adoption for a number of translation tasks. In this paper,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	421,861
2309.04287	Sequential Semantic Generative Communication for Progressive Text-to-Image Generation	This paper proposes new framework of communication system leveraging promising generation capabilities of multi-modal generative models. Regarding nowadays smart applications, successful communication can be made by conveying the perceptual meaning, which we set as text prompt. Text serves as a suitable semantic repres...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	390,670
2412.02901	SuperLoc: The Key to Robust LiDAR-Inertial Localization Lies in Predicting Alignment Risks	Map-based LiDAR localization, while widely used in autonomous systems, faces significant challenges in degraded environments due to lacking distinct geometric features. This paper introduces SuperLoc, a robust LiDAR localization package that addresses key limitations in existing methods. SuperLoc features a novel predi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	513,732
2306.10840	RedMotion: Motion Prediction via Redundancy Reduction	We introduce RedMotion, a transformer model for motion prediction in self-driving vehicles that learns environment representations via redundancy reduction. Our first type of redundancy reduction is induced by an internal transformer decoder and reduces a variable-sized set of local road environment tokens, representin...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	374,388
1806.04555	Logistic Ensemble Models	Predictive models that are developed in a regulated industry or a regulated application, like determination of credit worthiness, must be interpretable and rational (e.g., meaningful improvements in basic credit behavior must result in improved credit worthiness scores). Machine Learning technologies provide very good ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	100,265
1801.02805	DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation	We present a traffic simulation named DeepTraffic where the planning systems for a subset of the vehicles are handled by a neural network as part of a model-free, off-policy reinforcement learning process. The primary goal of DeepTraffic is to make the hands-on study of deep reinforcement learning accessible to thousan...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	false	false	87,989

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