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
2108.12304	Latent Tree Decomposition Parsers for AMR-to-Text Generation	Graph encoders in AMR-to-text generation models often rely on neighborhood convolutions or global vertex attention. While these approaches apply to general graphs, AMRs may be amenable to encoders that target their tree-like structure. By clustering edges into a hierarchy, a tree decomposition summarizes graph structur...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	252,462
2411.14700	Optimal Energy Dispatch of Grid-Connected Electric Vehicle Considering Lithium Battery Electrochemical Model	The grid-connected electric vehicles (EVs) serve as a promising regulating resource in the distribution grid with Vehicle-to-Grid (V2G) facilities. In the day-ahead stage, electric vehicle batteries (EVBs) need to be precisely dispatched and controlled to ensure high efficiency and prevent degradation. This article foc...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	510,280
2501.15858	Potential Applications of Artificial Intelligence for Cross-language Intelligibility Assessment of Dysarthric Speech	Purpose: This commentary introduces how artificial intelligence (AI) can be leveraged to advance cross-language intelligibility assessment of dysarthric speech. Method: We propose a conceptual framework consisting of a universal model that captures language-universal speech impairments and a language-specific intelligi...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	527,737
2407.04601	Written Term Detection Improves Spoken Term Detection	End-to-end (E2E) approaches to keyword search (KWS) are considerably simpler in terms of training and indexing complexity when compared to approaches which use the output of automatic speech recognition (ASR) systems. This simplification however has drawbacks due to the loss of modularity. In particular, where ASR-base...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	470,627
2204.05263	Maximum entropy optimal density control of discrete-time linear systems and Schr\"odinger bridges	We consider an entropy-regularized version of optimal density control of deterministic discrete-time linear systems. Entropy regularization, or a maximum entropy (MaxEnt) method for optimal control has attracted much attention especially in reinforcement learning due to its many advantages such as a natural exploration...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	290,978
2408.09107	Using neuroevolution for designing soft medical devices	Soft robots can exhibit better performance in specific tasks compared to conventional robots, particularly in healthcare-related tasks. However, the field of soft robotics is still young, and designing them often involves mimicking natural organisms or relying heavily on human experts' creativity. A formal automated de...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	481,277
2207.00959	Group-Theoretic Wideband Radar Waveform Design	We investigate the theory of affine groups in the context of designing radar waveforms that obey the desired wideband ambiguity function (WAF). The WAF is obtained by correlating the signal with its time-dilated, Doppler-shifted, and delayed replicas. We consider the WAF definition as a coefficient function of the unit...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	305,972
1102.4563	Proceedings of the first international workshop on domain-specific languages for robotic systems (DSLRob 2010)	The First International Workshop on Domain-Specific Languages and models for ROBotic systems (DSLRob'10) was held at the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'10), October 2010 in Taipei, Taiwan. The main topics of the workshop were domain-specific languages and models. A doma...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	9,314
2302.06736	Environment Semantic Aided Communication: A Real World Demonstration for Beam Prediction	Millimeter-wave (mmWave) and terahertz (THz) communication systems adopt large antenna arrays to ensure adequate receive signal power. However, adjusting the narrow beams of these antenna arrays typically incurs high beam training overhead that scales with the number of antennas. Recently proposed vision-aided beam pre...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	345,512
2412.07152	Hero-SR: One-Step Diffusion for Super-Resolution with Human Perception Priors	Owing to the robust priors of diffusion models, recent approaches have shown promise in addressing real-world super-resolution (Real-SR). However, achieving semantic consistency and perceptual naturalness to meet human perception demands remains difficult, especially under conditions of heavy degradation and varied inp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	515,528
2405.13758	Counterfactual Gradients-based Quantification of Prediction Trust in Neural Networks	The widespread adoption of deep neural networks in machine learning calls for an objective quantification of esoteric trust. In this paper we propose GradTrust, a classification trust measure for large-scale neural networks at inference. The proposed method utilizes variance of counterfactual gradients, i.e. the requir...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	456,068
2407.05551	Read, Watch and Scream! Sound Generation from Text and Video	Despite the impressive progress of multimodal generative models, video-to-audio generation still suffers from limited performance and limits the flexibility to prioritize sound synthesis for specific objects within the scene. Conversely, text-to-audio generation methods generate high-quality audio but pose challenges i...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	471,023
2409.12024	LEMON: Localized Editing with Mesh Optimization and Neural Shaders	In practical use cases, polygonal mesh editing can be faster than generating new ones, but it can still be challenging and time-consuming for users. Existing solutions for this problem tend to focus on a single task, either geometry or novel view synthesis, which often leads to disjointed results between the mesh and v...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	489,406
2302.09717	Coordinating Multiple Intelligent Reflecting Surfaces without Channel Information	Conventional beamforming methods for intelligent reflecting surfaces (IRSs) or reconfigurable intelligent surfaces (RISs) typically entail the full channel state information (CSI). However, the computational cost of channel acquisition soars exponentially with the number of IRSs. To bypass this difficulty, we propose a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	346,538
2107.06466	Going Beyond Linear RL: Sample Efficient Neural Function Approximation	Deep Reinforcement Learning (RL) powered by neural net approximation of the Q function has had enormous empirical success. While the theory of RL has traditionally focused on linear function approximation (or eluder dimension) approaches, little is known about nonlinear RL with neural net approximations of the Q functi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	246,099
1403.2140	Scientometrics: Untangling the topics	Measuring science is based on comparing articles to similar others. However, keyword-based groups of thematically similar articles are dominantly small. These small sizes keep the statistical errors of comparisons high. With the growing availability of bibliographic data such statistical errors can be reduced by mergin...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	31,463
2212.10722	Contrastive Error Attribution for Finetuned Language Models	Recent work has identified noisy and misannotated data as a core cause of hallucinations and unfaithful outputs in Natural Language Generation (NLG) tasks. Consequently, identifying and removing these examples is a key open challenge in creating reliable NLG systems. In this work, we introduce a framework to identify a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	337,574
2302.01034	An Efficient Convex Hull-based Vehicle Pose Estimation Method for 3D LiDAR	Vehicle pose estimation with LiDAR is essential in the perception technology of autonomous driving. However, due to incomplete observation measurements and sparsity of the LiDAR point cloud, it is challenging to achieve satisfactory pose extraction based on 3D LiDAR with the existing pose estimation methods. In additio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	343,456
2012.14020	Towards Understanding Sensor and Control Nodes Selection in Nonlinear Dynamic Systems: Lyapunov Theory Meets Branch-and-Bound	Sensor and actuator selection problems (SASP) are some of the core problems in dynamic systems design and control. These problems correspond to determining the optimal selection of sensors (measurements) or actuators (control nodes) such that certain estimation/control objectives can be achieved. While the literature o...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	213,386
1409.1365	Feasibility of In-band Full-Duplex Radio Transceivers with Imperfect RF Components: Analysis and Enhanced Cancellation Algorithms	In this paper we provide an overview regarding the feasibility of in-band full-duplex transceivers under imperfect RF components. We utilize results and findings from the recent research on full-duplex communications, while introducing also transmitter-induced thermal noise into the analysis. This means that the model ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	35,821
1612.06962	Stochastic Runtime Analysis of a Cross Entropy Algorithm for Traveling Salesman Problems	This article analyzes the stochastic runtime of a Cross-Entropy Algorithm on two classes of traveling salesman problems. The algorithm shares main features of the famous Max-Min Ant System with iteration-best reinforcement. For simple instances that have a $\{1,n\}$-valued distance function and a unique optimal solut...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	true	65,883
2401.01481	Optimizing UAV-UGV Coalition Operations: A Hybrid Clustering and Multi-Agent Reinforcement Learning Approach for Path Planning in Obstructed Environment	One of the most critical applications undertaken by coalitions of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) is reaching predefined targets by following the most time-efficient routes while avoiding collisions. Unfortunately, UAVs are hampered by limited battery life, and UGVs face challenges i...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	419,369
2405.20178	Non-intrusive data-driven model order reduction for circuits based on Hammerstein architectures	We demonstrate that data-driven system identification techniques can provide a basis for effective, non-intrusive model order reduction (MOR) for common circuits that are key building blocks in microelectronics. Our approach is motivated by the practical operation of these circuits and utilizes a canonical Hammerstein ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	459,229
1804.03421	Ubiquitous Cell-Free Massive MIMO Communications	Since the first cellular networks were trialled in the 1970s, we have witnessed an incredible wireless revolution. From 1G to 4G, the massive traffic growth has been managed by a combination of wider bandwidths, refined radio interfaces, and network densification, namely increasing the number of antennas per site. Due ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	94,624
2301.00016	Behave-XAI: Deep Explainable Learning of Behavioral Representational Data	According to the latest trend of artificial intelligence, AI-systems needs to clarify regarding general,specific decisions,services provided by it. Only consumer is satisfied, with explanation , for example, why any classification result is the outcome of any given time. This actually motivates us using explainable or ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	338,774
2008.00482	Investigating the Effect of Emoji in Opinion Classification of Uzbek Movie Review Comments	Opinion mining on social media posts has become more and more popular. Users often express their opinion on a topic not only with words but they also use image symbols such as emoticons and emoji. In this paper, we investigate the effect of emoji-based features in opinion classification of Uzbek texts, and more specifi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	190,021
2502.02770	Twilight: Adaptive Attention Sparsity with Hierarchical Top-$p$ Pruning	Leveraging attention sparsity to accelerate long-context large language models (LLMs) has been a hot research topic. However, current algorithms such as sparse attention or key-value (KV) cache compression tend to use a fixed budget, which presents a significant challenge during deployment because it fails to account f...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	530,462
2404.05578	Social-MAE: Social Masked Autoencoder for Multi-person Motion Representation Learning	For a complete comprehension of multi-person scenes, it is essential to go beyond basic tasks like detection and tracking. Higher-level tasks, such as understanding the interactions and social activities among individuals, are also crucial. Progress towards models that can fully understand scenes involving multiple peo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	445,128
2310.17675	Early Detection of Tuberculosis with Machine Learning Cough Audio Analysis: Towards More Accessible Global Triaging Usage	Tuberculosis (TB), a bacterial disease mainly affecting the lungs, is one of the leading infectious causes of mortality worldwide. To prevent TB from spreading within the body, which causes life-threatening complications, timely and effective anti-TB treatment is crucial. Cough, an objective biomarker for TB, is a tria...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	403,242
2102.07192	Improved Bengali Image Captioning via deep convolutional neural network based encoder-decoder model	Image Captioning is an arduous task of producing syntactically and semantically correct textual descriptions of an image in natural language with context related to the image. Existing notable pieces of research in Bengali Image Captioning (BIC) are based on encoder-decoder architecture. This paper presents an end-to-e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	220,024
2202.12555	6D Rotation Representation For Unconstrained Head Pose Estimation	In this paper, we present a method for unconstrained end-to-end head pose estimation. We address the problem of ambiguous rotation labels by introducing the rotation matrix formalism for our ground truth data and propose a continuous 6D rotation matrix representation for efficient and robust direct regression. This way...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	282,286
2009.12179	Improved Dimensionality Reduction of various Datasets using Novel Multiplicative Factoring Principal Component Analysis (MPCA)	Principal Component Analysis (PCA) is known to be the most widely applied dimensionality reduction approach. A lot of improvements have been done on the traditional PCA, in order to obtain optimal results in the dimensionality reduction of various datasets. In this paper, we present an improvement to the traditional PC...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	197,353
2104.02611	PointShuffleNet: Learning Non-Euclidean Features with Homotopy Equivalence and Mutual Information	Point cloud analysis is still a challenging task due to the disorder and sparsity of samplings of their geometric structures from 3D sensors. In this paper, we introduce the homotopy equivalence relation (HER) to make the neural networks learn the data distribution from a high-dimension manifold. A shuffle operation is...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	228,788
1805.07418	Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly	When confronted with massive data streams, summarizing data with dimension reduction methods such as PCA raises theoretical and algorithmic pitfalls. Principal curves act as a nonlinear generalization of PCA and the present paper proposes a novel algorithm to automatically and sequentially learn principal curves from d...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	97,808
2211.16733	A minor extension of the logistic equation for growth of word counts on online media: Parametric description of diversity of growth phenomena in society	To understand the growing phenomena of new vocabulary on nationwide online social media, we analyzed monthly word count time series extracted from approximately 1 billion Japanese blog articles from 2007 to 2019. In particular, we first introduced the extended logistic equation by adding one parameter to the original e...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	333,727
1911.10524	Causally Denoise Word Embeddings Using Half-Sibling Regression	Distributional representations of words, also known as word vectors, have become crucial for modern natural language processing tasks due to their wide applications. Recently, a growing body of word vector postprocessing algorithm has emerged, aiming to render off-the-shelf word vectors even stronger. In line with thes...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	154,862
1701.03566	Integer-Forcing Linear Receivers: A Design Criterion for Full-Diversity STBCs	In multiple-input multiple-output (MIMO) fading channels, the design criterion for full-diversity space-time block codes (STBCs) is primarily determined by the decoding method at the receiver. Although constructions of STBCs have predominantly matched the maximum-likelihood (ML) decoder, design criteria and constructio...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	66,720
2207.09649	GenText: Unsupervised Artistic Text Generation via Decoupled Font and Texture Manipulation	Automatic artistic text generation is an emerging topic which receives increasing attention due to its wide applications. The artistic text can be divided into three components, content, font, and texture, respectively. Existing artistic text generation models usually focus on manipulating one aspect of the above compo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,963
2403.07283	A Framework for Cost-Effective and Self-Adaptive LLM Shaking and Recovery Mechanism	As Large Language Models (LLMs) gain great success in real-world applications, an increasing number of users are seeking to develop and deploy their customized LLMs through cloud services. Nonetheless, in some specific domains, there are still concerns regarding cost and trade-offs between privacy issues and accuracy. ...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	436,814
2402.00994	A Cost-Efficient Approach for Creating Virtual Fitting Room using Generative Adversarial Networks (GANs)	Customers all over the world want to see how the clothes fit them or not before purchasing. Therefore, customers by nature prefer brick-and-mortar clothes shopping so they can try on products before purchasing them. But after the Pandemic of COVID19 many sellers either shifted to online shopping or closed their fitting...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	425,805
2403.04146	FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learning	Federated learning (FL) is a promising approach for learning a model from data distributed on massive clients without exposing data privacy. It works effectively in the ideal federation where clients share homogeneous data distribution and learning behavior. However, FL may fail to function appropriately when the feder...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	435,476
2405.05299	Challenges for Responsible AI Design and Workflow Integration in Healthcare: A Case Study of Automatic Feeding Tube Qualification in Radiology	Nasogastric tubes (NGTs) are feeding tubes that are inserted through the nose into the stomach to deliver nutrition or medication. If not placed correctly, they can cause serious harm, even death to patients. Recent AI developments demonstrate the feasibility of robustly detecting NGT placement from Chest X-ray images ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	452,877
2110.05587	Evaluation of Latent Space Disentanglement in the Presence of Interdependent Attributes	Controllable music generation with deep generative models has become increasingly reliant on disentanglement learning techniques. However, current disentanglement metrics, such as mutual information gap (MIG), are often inadequate and misleading when used for evaluating latent representations in the presence of interde...	false	false	true	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	260,316
2306.13888	L3Cube-MahaSent-MD: A Multi-domain Marathi Sentiment Analysis Dataset and Transformer Models	The exploration of sentiment analysis in low-resource languages, such as Marathi, has been limited due to the availability of suitable datasets. In this work, we present L3Cube-MahaSent-MD, a multi-domain Marathi sentiment analysis dataset, with four different domains - movie reviews, general tweets, TV show subtitles,...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	375,441
2406.13316	Reinforcing Pre-trained Models Using Counterfactual Images	This paper proposes a novel framework to reinforce classification models using language-guided generated counterfactual images. Deep learning classification models are often trained using datasets that mirror real-world scenarios. In this training process, because learning is based solely on correlations with labels, t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	465,799
2206.06096	An Enactivist-Inspired Mathematical Model of Cognition	We formulate five basic tenets of enactivist cognitive science that we have carefully identified in the relevant literature as the main underlying principles of that philosophy. We then develop a mathematical framework to talk about cognitive systems (both artificial and natural) which complies with these enactivist te...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	302,251
2312.03330	Measuring Misogyny in Natural Language Generation: Preliminary Results from a Case Study on two Reddit Communities	Generic `toxicity' classifiers continue to be used for evaluating the potential for harm in natural language generation, despite mounting evidence of their shortcomings. We consider the challenge of measuring misogyny in natural language generation, and argue that generic `toxicity' classifiers are inadequate for this ...	false	false	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	413,217
2409.17668	A Database Engineered System for Big Data Analytics on Tornado Climatology	Recognizing the challenges with current tornado warning systems, we investigate alternative approaches. In particular, we present a database engi-neered system that integrates information from heterogeneous rich data sources, including climatology data for tornadoes and data just before a tornado warning. The system ai...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	491,923
2111.02845	Attacking Deep Reinforcement Learning-Based Traffic Signal Control Systems with Colluding Vehicles	The rapid advancements of Internet of Things (IoT) and artificial intelligence (AI) have catalyzed the development of adaptive traffic signal control systems (ATCS) for smart cities. In particular, deep reinforcement learning (DRL) methods produce the state-of-the-art performance and have great potentials for practical...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	264,989
2009.10776	On Hybrid-ARQ-Based Intelligent Reflecting Surface-Assisted Communication System	The intelligent reflecting surface (IRS) is an emerging technique to extend the wireless coverage. In this letter, the performance of hybrid automatic repeat request (hybrid-ARQ) for an IRS-assisted system is analyzed. More specifically, the outage performance of the IRS-aided system using hybrid-ARQ protocol with chas...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	196,977
2401.06930	PizzaCommonSense: Learning to Model Commonsense Reasoning about Intermediate Steps in Cooking Recipes	Understanding procedural texts, such as cooking recipes, is essential for enabling machines to follow instructions and reason about tasks, a key aspect of intelligent reasoning. In cooking, these instructions can be interpreted as a series of modifications to a food preparation. For a model to effectively reason about ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	421,348
1109.1530	Daily Deals: Prediction, Social Diffusion, and Reputational Ramifications	Daily deal sites have become the latest Internet sensation, providing discounted offers to customers for restaurants, ticketed events, services, and other items. We begin by undertaking a study of the economics of daily deals on the web, based on a dataset we compiled by monitoring Groupon and LivingSocial sales in 20 ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	12,035
2404.17427	Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection	Object detectors in real-world applications often fail to detect objects due to varying factors such as weather conditions and noisy input. Therefore, a process that mitigates false detections is crucial for both safety and accuracy. While uncertainty-based thresholding shows promise, previous works demonstrate an impe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	449,851
2105.08540	Kemeny Consensus Complexity	The computational study of election problems generally focuses on questions related to the winner or set of winners of an election. But social preference functions such as Kemeny rule output a full ranking of the candidates (a consensus). We study the complexity of consensus-related questions, with a particular focus o...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	235,789
2411.17831	Rapid Distributed Fine-tuning of a Segmentation Model Onboard Satellites	Segmentation of Earth observation (EO) satellite data is critical for natural hazard analysis and disaster response. However, processing EO data at ground stations introduces delays due to data transmission bottlenecks and communication windows. Using segmentation models capable of near-real-time data analysis onboard ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	511,624
2302.09738	Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning	Riemannian submanifold optimization with momentum is computationally challenging because, to ensure that the iterates remain on the submanifold, we often need to solve difficult differential equations. Here, we simplify such difficulties for a class of sparse or structured symmetric positive-definite matrices with the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	346,544
1811.01463	Security for Machine Learning-based Systems: Attacks and Challenges during Training and Inference	The exponential increase in dependencies between the cyber and physical world leads to an enormous amount of data which must be efficiently processed and stored. Therefore, computing paradigms are evolving towards machine learning (ML)-based systems because of their ability to efficiently and accurately process the eno...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	112,374
1805.00716	Transmit Precoding and Receive Power Splitting for Harvested Power Maximization in MIMO SWIPT Systems	We consider the problem of maximizing the harvested power in Multiple Input Multiple Output (MIMO) Simultaneous Wireless Information and Power Transfer (SWIPT) systems with power splitting reception. Different from recently proposed designs, with our optimization problem formulation we target for the jointly optimal tr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	96,495
2108.04417	Privacy-Preserving Machine Learning: Methods, Challenges and Directions	Machine learning (ML) is increasingly being adopted in a wide variety of application domains. Usually, a well-performing ML model relies on a large volume of training data and high-powered computational resources. Such a need for and the use of huge volumes of data raise serious privacy concerns because of the potentia...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	249,991
2402.15013	Filter Bubble or Homogenization? Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns	Recommendation algorithms play a pivotal role in shaping our media choices, which makes it crucial to comprehend their long-term impact on user behavior. These algorithms are often linked to two critical outcomes: homogenization, wherein users consume similar content despite disparate underlying preferences, and the fi...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	431,956
2211.03387	Temporal superimposed crossover module for effective continuous sign language	The ultimate goal of continuous sign language recognition(CSLR) is to facilitate the communication between special people and normal people, which requires a certain degree of real-time and deploy-ability of the model. However, in the previous research on CSLR, little attention has been paid to the real-time and deploy...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	328,916
2307.14721	Singularity Distance Computations for 3-RPR Manipulators Using Intrinsic Metrics	We present an efficient algorithm for computing the closest singular configuration to each non-singular pose of a 3-RPR planar manipulator performing a 1-parametric motion. By considering a 3-RPR manipulator as a planar framework, one can use methods from rigidity theory to compute the singularity distance with respect...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	382,028
2302.04945	Efficient Propagation of Uncertainty via Reordering Monte Carlo Samples	Uncertainty analysis in the outcomes of model predictions is a key element in decision-based material design to establish confidence in the models and evaluate the fidelity of models. Uncertainty Propagation (UP) is a technique to determine model output uncertainties based on the uncertainty in its input variables. The...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	344,871
2304.11445	Improving Stain Invariance of CNNs for Segmentation by Fusing Channel Attention and Domain-Adversarial Training	Variability in staining protocols, such as different slide preparation techniques, chemicals, and scanner configurations, can result in a diverse set of whole slide images (WSIs). This distribution shift can negatively impact the performance of deep learning models on unseen samples, presenting a significant challenge ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	359,816
1601.01411	Learning Kernels for Structured Prediction using Polynomial Kernel Transformations	Learning the kernel functions used in kernel methods has been a vastly explored area in machine learning. It is now widely accepted that to obtain 'good' performance, learning a kernel function is the key challenge. In this work we focus on learning kernel representations for structured regression. We propose use of po...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	50,743
2403.11521	A Data-driven Approach for Rapid Detection of Aeroelastic Modes from Flutter Flight Test Based on Limited Sensor Measurements	Flutter flight test involves the evaluation of the airframes aeroelastic stability by applying artificial excitation on the aircraft lifting surfaces. The subsequent responses are captured and analyzed to extract the frequencies and damping characteristics of the system. However, noise contamination, turbulence, non-op...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	438,742
2204.02123	Improved and Efficient Conversational Slot Labeling through Question Answering	Transformer-based pretrained language models (PLMs) offer unmatched performance across the majority of natural language understanding (NLU) tasks, including a body of question answering (QA) tasks. We hypothesize that improvements in QA methodology can also be directly exploited in dialog NLU; however, dialog tasks mus...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	289,835
1907.12378	Music Recommendations in Hyperbolic Space: An Application of Empirical Bayes and Hierarchical Poincar\'e Embeddings	Matrix Factorization (MF) is a common method for generating recommendations, where the proximity of entities like users or items in the embedded space indicates their similarity to one another. Though almost all applications implicitly use a Euclidean embedding space to represent two entity types, recent work has sugge...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	140,106
2312.14183	On Early Detection of Hallucinations in Factual Question Answering	While large language models (LLMs) have taken great strides towards helping humans with a plethora of tasks, hallucinations remain a major impediment towards gaining user trust. The fluency and coherence of model generations even when hallucinating makes detection a difficult task. In this work, we explore if the artif...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	417,522
2012.08236	Point-Level Temporal Action Localization: Bridging Fully-supervised Proposals to Weakly-supervised Losses	Point-Level temporal action localization (PTAL) aims to localize actions in untrimmed videos with only one timestamp annotation for each action instance. Existing methods adopt the frame-level prediction paradigm to learn from the sparse single-frame labels. However, such a framework inevitably suffers from a large sol...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	211,713
2401.13472	Segmenting Cardiac Muscle Z-disks with Deep Neural Networks	Z-disks are complex structures that delineate repeating sarcomeres in striated muscle. They play significant roles in cardiomyocytes such as providing mechanical stability for the contracting sarcomere, cell signalling and autophagy. Changes in Z-disk architecture have been associated with impaired cardiac function. He...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	423,739
2408.01738	Adaptive Safety with Control Barrier Functions and Triggered Batch Least-Squares Identifier	In this paper, a triggered Batch Least-Squares Identifier (BaLSI) based adaptive safety control scheme is proposed for uncertain systems with potentially conflicting control objectives and safety constraints. A relaxation term is added to the Quadratic Programs (QP) combining the transformed Control Lyapunov Functions ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	478,357
2302.12324	Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization	Good figure captions help paper readers understand complex scientific figures. Unfortunately, even published papers often have poorly written captions. Automatic caption generation could aid paper writers by providing good starting captions that can be refined for better quality. Prior work often treated figure caption...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	347,517
2106.08371	Rinascimento: searching the behaviour space of Splendor	The use of Artificial Intelligence (AI) for play-testing is still on the sidelines of main applications of AI in games compared to performance-oriented game-playing. One of the main purposes of play-testing a game is gathering data on the gameplay, highlighting good and bad features of the design of the game, providing...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	241,266
1902.07653	On the effect of age perception biases for real age regression	Automatic age estimation from facial images represents an important task in computer vision. This paper analyses the effect of gender, age, ethnic, makeup and expression attributes of faces as sources of bias to improve deep apparent age prediction. Following recent works where it is shown that apparent age labels bene...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	122,030
2502.10014	Recovering nonlinear dynamics from non-uniform observations: A physics-based identification approach with practical case studies	Uniform and smooth data collection is often infeasible in real-world scenarios. In this paper, we propose an identification framework to effectively handle the so-called non-uniform observations, i.e., data scenarios that include missing measurements, multiple runs, or aggregated observations. The goal is to provide a ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	533,702
quant-ph/0108073	Quantum Information in Space and Time	Many important results in modern quantum information theory have been obtained for an idealized situation when the spacetime dependence of quantum phenomena is neglected. However the transmission and processing of (quantum) information is a physical process in spacetime. Therefore such basic notions in quantum informat...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	540,865
2203.12852	Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges	Many physical systems can be best understood as sets of discrete data with associated relationships. Where previously these sets of data have been formulated as series or image data to match the available machine learning architectures, with the advent of graph neural networks (GNNs), these systems can be learned nativ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	287,418
2112.01918	Heuristic Search Planning with Deep Neural Networks using Imitation, Attention and Curriculum Learning	Learning a well-informed heuristic function for hard task planning domains is an elusive problem. Although there are known neural network architectures to represent such heuristic knowledge, it is not obvious what concrete information is learned and whether techniques aimed at understanding the structure help in improv...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	269,672
2501.09499	VanGogh: A Unified Multimodal Diffusion-based Framework for Video Colorization	Video colorization aims to transform grayscale videos into vivid color representations while maintaining temporal consistency and structural integrity. Existing video colorization methods often suffer from color bleeding and lack comprehensive control, particularly under complex motion or diverse semantic cues. To this...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	525,163
1909.03253	NuClick: From Clicks in the Nuclei to Nuclear Boundaries	Best performing nuclear segmentation methods are based on deep learning algorithms that require a large amount of annotated data. However, collecting annotations for nuclear segmentation is a very labor-intensive and time-consuming task. Thereby, providing a tool that can facilitate and speed up this procedure is very ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	144,414
2209.15165	Distilling Style from Image Pairs for Global Forward and Inverse Tone Mapping	Many image enhancement or editing operations, such as forward and inverse tone mapping or color grading, do not have a unique solution, but instead a range of solutions, each representing a different style. Despite this, existing learning-based methods attempt to learn a unique mapping, disregarding this style. In this...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	320,492
2307.14676	Performance of RIS-Assisted Full-Duplex Space Shift Keying With Imperfect Self-Interference Cancellation	In this paper, we consider a full-duplex (FD) space shift keying (SSK) communication system, where information exchange between two users is assisted only by a reconfigurable intelligent surface (RIS). In particular, the impact of loop interference (LI) between the transmit and receive antennas as well as residual self...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	382,018
2408.07362	BadMerging: Backdoor Attacks Against Model Merging	Fine-tuning pre-trained models for downstream tasks has led to a proliferation of open-sourced task-specific models. Recently, Model Merging (MM) has emerged as an effective approach to facilitate knowledge transfer among these independently fine-tuned models. MM directly combines multiple fine-tuned task-specific mode...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	480,552
2501.07809	Conformal mapping Coordinates Physics-Informed Neural Networks (CoCo-PINNs): learning neural networks for designing neutral inclusions	We focus on designing and solving the neutral inclusion problem via neural networks. The neutral inclusion problem has a long history in the theory of composite materials, and it is exceedingly challenging to identify the precise condition that precipitates a general-shaped inclusion into a neutral inclusion. Physics-i...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	524,518
2307.16348	Rating-based Reinforcement Learning	This paper develops a novel rating-based reinforcement learning approach that uses human ratings to obtain human guidance in reinforcement learning. Different from the existing preference-based and ranking-based reinforcement learning paradigms, based on human relative preferences over sample pairs, the proposed rating...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	382,568
2310.10245	Mask wearing object detection algorithm based on improved YOLOv5	Wearing a mask is one of the important measures to prevent infectious diseases. However, it is difficult to detect people's mask-wearing situation in public places with high traffic flow. To address the above problem, this paper proposes a mask-wearing face detection model based on YOLOv5l. Firstly, Multi-Head Attentio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	400,147
1409.0084	Kernel Coding: General Formulation and Special Cases	Representing images by compact codes has proven beneficial for many visual recognition tasks. Most existing techniques, however, perform this coding step directly in image feature space, where the distributions of the different classes are typically entangled. In contrast, here, we study the problem of performing codin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	35,699
1802.02040	Multispectral Compressive Imaging Strategies using Fabry-P\'erot Filtered Sensors	This paper introduces two acquisition device architectures for multispectral compressive imaging. Unlike most existing methods, the proposed computational imaging techniques do not include any dispersive element, as they use a dedicated sensor which integrates narrowband Fabry-P\'erot spectral filters at the pixel leve...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	89,692
2201.09308	Basket-based Softmax	Softmax-based losses have achieved state-of-the-art performances on various tasks such as face recognition and re-identification. However, these methods highly relied on clean datasets with global labels, which limits their usage in many real-world applications. An important reason is that merging and organizing datase...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	276,627
1507.02188	AutoCompete: A Framework for Machine Learning Competition	In this paper, we propose AutoCompete, a highly automated machine learning framework for tackling machine learning competitions. This framework has been learned by us, validated and improved over a period of more than two years by participating in online machine learning competitions. It aims at minimizing human interf...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	44,955
2501.16863	HD-CB: The First Exploration of Hyperdimensional Computing for Contextual Bandits Problems	Hyperdimensional Computing (HDC), also known as Vector Symbolic Architectures, is a computing paradigm that combines the strengths of symbolic reasoning with the efficiency and scalability of distributed connectionist models in artificial intelligence. HDC has recently emerged as a promising alternative for performing ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	528,131
2407.20165	Meta-Learning for Adaptive Control with Automated Mirror Descent	Adaptive control achieves concurrent parameter learning and stable control under uncertainties that are linearly parameterized with known nonlinear features. Nonetheless, it is often difficult to obtain such nonlinear features. To address this difficulty, recent progress has been made in integrating meta-learning with ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	477,066
1712.05359	Seasonal Stochastic Blockmodeling for Anomaly Detection in Dynamic Networks	Sociotechnological and geospatial processes exhibit time varying structure that make insight discovery challenging. To detect abnormal moments in these processes, a definition of `normal' must be established. This paper proposes a new statistical model for such systems, modeled as dynamic networks, to address this chal...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	86,725
1901.08558	Quantifying Interpretability and Trust in Machine Learning Systems	Decisions by Machine Learning (ML) models have become ubiquitous. Trusting these decisions requires understanding how algorithms take them. Hence interpretability methods for ML are an active focus of research. A central problem in this context is that both the quality of interpretability methods as well as trust in ML...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	119,510
2403.04382	Acceleron: A Tool to Accelerate Research Ideation	Several tools have recently been proposed for assisting researchers during various stages of the research life-cycle. However, these primarily concentrate on tasks such as retrieving and recommending relevant literature, reviewing and critiquing the draft, and writing of research manuscripts. Our investigation reveals ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	435,578
1910.12614	CycleGAN Voice Conversion of Spectral Envelopes using Adversarial Weights	This paper tackles GAN optimization and stability issues in the context of voice conversion. First, to simplify the conversion task, we propose to use spectral envelopes as inputs. Second we propose two adversarial weight training paradigms, the generalized weighted GAN and the generator impact GAN, both aim at reducin...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	151,145
2404.02738	Adaptive Affinity-Based Generalization For MRI Imaging Segmentation Across Resource-Limited Settings	The joint utilization of diverse data sources for medical imaging segmentation has emerged as a crucial area of research, aiming to address challenges such as data heterogeneity, domain shift, and data quality discrepancies. Integrating information from multiple data domains has shown promise in improving model general...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	443,988
2212.13350	A Generalization of ViT/MLP-Mixer to Graphs	Graph Neural Networks (GNNs) have shown great potential in the field of graph representation learning. Standard GNNs define a local message-passing mechanism which propagates information over the whole graph domain by stacking multiple layers. This paradigm suffers from two major limitations, over-squashing and poor lo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	338,279
2301.05380	Prompting Neural Machine Translation with Translation Memories	Improving machine translation (MT) systems with translation memories (TMs) is of great interest to practitioners in the MT community. However, previous approaches require either a significant update of the model architecture and/or additional training efforts to make the models well-behaved when TMs are taken as additi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	340,334

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