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
1909.00732	Hierarchical Control for Bipedal Locomotion using Central Pattern Generators and Neural Networks	The complexity of bipedal locomotion may be attributed to the difficulty in synchronizing joint movements while at the same time achieving high-level objectives such as walking in a particular direction. Artificial central pattern generators (CPGs) can produce synchronized joint movements and have been used in the past...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	true	false	false	143,704
2409.04825	Metadata augmented deep neural networks for wild animal classification	Camera trap imagery has become an invaluable asset in contemporary wildlife surveillance, enabling researchers to observe and investigate the behaviors of wild animals. While existing methods rely solely on image data for classification, this may not suffice in cases of suboptimal animal angles, lighting, or image qual...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	486,525
2310.04791	Conditional Diffusion Model for Target Speaker Extraction	We propose DiffSpEx, a generative target speaker extraction method based on score-based generative modelling through stochastic differential equations. DiffSpEx deploys a continuous-time stochastic diffusion process in the complex short-time Fourier transform domain, starting from the target speaker source and convergi...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	397,819
2404.04890	A Unified Diffusion Framework for Scene-aware Human Motion Estimation from Sparse Signals	Estimating full-body human motion via sparse tracking signals from head-mounted displays and hand controllers in 3D scenes is crucial to applications in AR/VR. One of the biggest challenges to this task is the one-to-many mapping from sparse observations to dense full-body motions, which endowed inherent ambiguities. T...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	444,844
1302.7056	KSU KDD: Word Sense Induction by Clustering in Topic Space	We describe our language-independent unsupervised word sense induction system. This system only uses topic features to cluster different word senses in their global context topic space. Using unlabeled data, this system trains a latent Dirichlet allocation (LDA) topic model then uses it to infer the topics distribution...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	22,499
2308.13077	Preserving Modality Structure Improves Multi-Modal Learning	Self-supervised learning on large-scale multi-modal datasets allows learning semantically meaningful embeddings in a joint multi-modal representation space without relying on human annotations. These joint embeddings enable zero-shot cross-modal tasks like retrieval and classification. However, these methods often stru...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	387,773
2210.01785	TabLeak: Tabular Data Leakage in Federated Learning	While federated learning (FL) promises to preserve privacy, recent works in the image and text domains have shown that training updates leak private client data. However, most high-stakes applications of FL (e.g., in healthcare and finance) use tabular data, where the risk of data leakage has not yet been explored. A s...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	321,395
2108.10242	Pattern Inversion as a Pattern Recognition Method for Machine Learning	Artificial neural networks use a lot of coefficients that take a great deal of computing power for their adjustment, especially if deep learning networks are employed. However, there exist coefficients-free extremely fast indexing-based technologies that work, for instance, in Google search engines, in genome sequencin...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	251,839
2409.06267	Mahalanobis k-NN: A Statistical Lens for Robust Point-Cloud Registrations	In this paper, we discuss Mahalanobis k-NN: A Statistical Lens designed to address the challenges of feature matching in learning-based point cloud registration when confronted with an arbitrary density of point clouds. We tackle this by adopting Mahalanobis k-NN's inherent property to capture the distribution of the l...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	487,063
2407.18471	Constructing the CORD-19 Vaccine Dataset	We introduce new dataset 'CORD-19-Vaccination' to cater to scientists specifically looking into COVID-19 vaccine-related research. This dataset is extracted from CORD-19 dataset [Wang et al., 2020] and augmented with new columns for language detail, author demography, keywords, and topic per paper. Facebook's fastText ...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	476,384
2412.08128	Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?	Graph contrastive learning (GCL) has been widely used as an effective self-supervised learning method for graph representation learning. However, how to apply adequate and stable graph augmentation to generating proper views for contrastive learning remains an essential problem. Dropping edges is a primary augmentation...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	515,956
2310.07138	Denoising Task Routing for Diffusion Models	Diffusion models generate highly realistic images by learning a multi-step denoising process, naturally embodying the principles of multi-task learning (MTL). Despite the inherent connection between diffusion models and MTL, there remains an unexplored area in designing neural architectures that explicitly incorporate ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	398,838
2204.08206	TigerLily: Finding drug interactions in silico with the Graph	Tigerlily is a TigerGraph based system designed to solve the drug interaction prediction task. In this machine learning task, we want to predict whether two drugs have an adverse interaction. Our framework allows us to solve this highly relevant real-world problem using graph mining techniques in these steps: (a) Usi...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	292,007
2407.19498	Independent fact-checking organizations exhibit a departure from political neutrality	Independent fact-checking organizations have emerged as the crusaders to debunk fake news. However, they may not always remain neutral, as they can be selective in the false news they choose to expose and in how they present the information. They can deviate from neutrality by being selective in what false news they de...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	476,813
2305.19922	Representation-Driven Reinforcement Learning	We present a representation-driven framework for reinforcement learning. By representing policies as estimates of their expected values, we leverage techniques from contextual bandits to guide exploration and exploitation. Particularly, embedding a policy network into a linear feature space allows us to reframe the exp...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	369,738
1801.02774	Adversarial Spheres	State of the art computer vision models have been shown to be vulnerable to small adversarial perturbations of the input. In other words, most images in the data distribution are both correctly classified by the model and are very close to a visually similar misclassified image. Despite substantial research interest, t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	87,980
0711.2444	Proof nets for display logic	This paper explores several extensions of proof nets for the Lambek calculus in order to handle the different connectives of display logic in a natural way. The new proof net calculus handles some recent additions to the Lambek vocabulary such as Galois connections and Grishin interactions. It concludes with an explora...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	901
2106.07554	Dataset for eye-tracking tasks	In recent years many different deep neural networks were developed, but due to a large number of layers in deep networks, their training requires a long time and a large number of datasets. Today is popular to use trained deep neural networks for various tasks, even for simple ones in which such deep networks are not r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	240,970
2410.16893	Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation	Bayesian optimization relies on iteratively constructing and optimizing an acquisition function. The latter turns out to be a challenging, non-convex optimization problem itself. Despite the relative importance of this step, most algorithms employ sampling- or gradient-based methods, which do not provably converge to g...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	501,233
2401.12630	Full-Stack Optimization for CAM-Only DNN Inference	The accuracy of neural networks has greatly improved across various domains over the past years. Their ever-increasing complexity, however, leads to prohibitively high energy demands and latency in von Neumann systems. Several computing-in-memory (CIM) systems have recently been proposed to overcome this, but trade-off...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	423,448
1911.09487	Chemical-protein Interaction Extraction via Gaussian Probability Distribution and External Biomedical Knowledge	Motivation: The biomedical literature contains a wealth of chemical-protein interactions (CPIs). Automatically extracting CPIs described in biomedical literature is essential for drug discovery, precision medicine, as well as basic biomedical research. Most existing methods focus only on the sentence sequence to identi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	154,536
2311.01944	Swarm Performance Indicators: Metrics for Robustness, Fault Tolerance, Scalability and Adaptability	Swarms have distributed control and so are assumed to inherently have superior robustness, scalability and adaptability compared to centralised multi-agent systems. However, these features have generally only been defined qualitatively and there is a lack of quantitative metrics and experimental measures for the claime...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	405,237
2403.15145	Robust Resource Allocation for STAR-RIS Assisted SWIPT Systems	A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted simultaneous wireless information and power transfer (SWIPT) system is proposed. More particularly, an STAR-RIS is deployed to assist in the information/power transfer from a multi-antenna access point (AP) to multiple s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	440,420
2407.03551	Feelings about Bodies: Emotions on Diet and Fitness Forums Reveal Gendered Stereotypes and Body Image Concerns	The gendered expectations about ideal body types can lead to body image concerns, dissatisfaction, and in extreme cases, disordered eating and other psychopathologies across the gender spectrum. While research has focused on pro-anorexia online communities that glorify the 'thin ideal', less attention has been given to...	false	false	false	true	false	false	false	false	true	false	false	false	false	true	false	false	false	false	470,192
0808.0521	Logics for the Relational Syllogistic	The Aristotelian syllogistic cannot account for the validity of many inferences involving relational facts. In this paper, we investigate the prospects for providing a relational syllogistic. We identify several fragments based on (a) whether negation is permitted on all nouns, including those in the subject of a sente...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	2,159
2403.00344	Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behaviors and Adversarial Style Sampling for Assistive Tasks	Autonomous assistance of people with motor impairments is one of the most promising applications of autonomous robotic systems. Recent studies have reported encouraging results using deep reinforcement learning (RL) in the healthcare domain. Previous studies showed that assistive tasks can be formulated as multi-agent ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	433,950
2012.11975	A Consistent Higher-Order Isogeometric Shell Formulation	Shell analysis is a well-established field, but achieving optimal higher-order convergence rates for such simulations is a difficult challenge. We present an isogeometric Kirchhoff-Love shell framework that treats every numerical aspect in a consistent higher-order accurate way. In particular, a single trimmed B-spline...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	212,792
1407.7182	Conditional Plausibility Measures and Bayesian Networks	A general notion of algebraic conditional plausibility measures is defined. Probability measures, ranking functions, possibility measures, and (under the appropriate definitions) sets of probability measures can all be viewed as defining algebraic conditional plausibility measures. It is shown that the technology of Ba...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	34,924
2406.07532	Hearing Anything Anywhere	Recent years have seen immense progress in 3D computer vision and computer graphics, with emerging tools that can virtualize real-world 3D environments for numerous Mixed Reality (XR) applications. However, alongside immersive visual experiences, immersive auditory experiences are equally vital to our holistic percepti...	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	463,092
2412.13877	RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation	In this paper, we introduce RoboMIND (Multi-embodiment Intelligence Normative Data for Robot Manipulation), a dataset containing 107k demonstration trajectories across 479 diverse tasks involving 96 object classes. RoboMIND is collected through human teleoperation and encompasses comprehensive robotic-related informati...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	518,490
2403.13672	Machine Learning Optimized Approach for Parameter Selection in MESHFREE Simulations	Meshfree simulation methods are emerging as compelling alternatives to conventional mesh-based approaches, particularly in the fields of Computational Fluid Dynamics (CFD) and continuum mechanics. In this publication, we provide a comprehensive overview of our research combining Machine Learning (ML) and Fraunhofer's M...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	439,727
2311.14859	An Empirical Investigation into Benchmarking Model Multiplicity for Trustworthy Machine Learning: A Case Study on Image Classification	Deep learning models have proven to be highly successful. Yet, their over-parameterization gives rise to model multiplicity, a phenomenon in which multiple models achieve similar performance but exhibit distinct underlying behaviours. This multiplicity presents a significant challenge and necessitates additional specif...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	410,281
2103.11824	Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools	Big data has been used widely in many areas including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted for improving the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools use...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	225,959
2405.13547	HighwayLLM: Decision-Making and Navigation in Highway Driving with RL-Informed Language Model	Autonomous driving is a complex task which requires advanced decision making and control algorithms. Understanding the rationale behind the autonomous vehicles' decision is crucial to ensure their safe and effective operation on highway driving. This study presents a novel approach, HighwayLLM, which harnesses the reas...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	455,982
2304.12652	Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur	Rendering novel view images is highly desirable for many applications. Despite recent progress, it remains challenging to render high-fidelity and view-consistent novel views of large-scale scenes from in-the-wild images with inevitable artifacts (e.g., motion blur). To this end, we develop a hybrid neural rendering mo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	360,306
2111.03890	Demystifying Deep Learning Models for Retinal OCT Disease Classification using Explainable AI	In the world of medical diagnostics, the adoption of various deep learning techniques is quite common as well as effective, and its statement is equally true when it comes to implementing it into the retina Optical Coherence Tomography (OCT) sector, but (i)These techniques have the black box characteristics that preven...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	265,303
2205.08029	Automatic Error Classification and Root Cause Determination while Replaying Recorded Workload Data at SAP HANA	Capturing customer workloads of database systems to replay these workloads during internal testing can be beneficial for software quality assurance. However, we experienced that such replays can produce a large amount of false positive alerts that make the results unreliable or time consuming to analyze. Therefore, we ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	296,794
1301.6236	Multi-Trial Guruswami--Sudan Decoding for Generalised Reed--Solomon Codes	An iterated refinement procedure for the Guruswami--Sudan list decoding algorithm for Generalised Reed--Solomon codes based on Alekhnovich's module minimisation is proposed. The method is parametrisable and allows variants of the usual list decoding approach. In particular, finding the list of \emph{closest} codewords ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	21,398
2304.09334	Model-Free Control Design Procedure Applied to Lateral Vehicle Control	Model-Free Control has proven its performance in a wide variety of systems. Although its adequate tuning can be achieved using the knowledge of the system and optimization-based approaches, there is not yet a systematic design procedure for this kind of control scheme. In this paper, a non-iterative Three Term Controll...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	359,014
2110.00337	PhiNets: a scalable backbone for low-power AI at the edge	In the Internet of Things era, where we see many interconnected and heterogeneous mobile and fixed smart devices, distributing the intelligence from the cloud to the edge has become a necessity. Due to limited computational and communication capabilities, low memory and limited energy budget, bringing artificial intell...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	258,360
2302.11909	Multi-Valued Neural Networks I A Multi-Valued Associative Memory	A new concept of a multi-valued associative memory is introduced, generalizing a similar one in fuzzy neural networks. We expand the results on fuzzy associative memory with thresholds, to the case of a multi-valued one: we introduce the novel concept of such a network without numbers, investigate its properties, and g...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	347,360
0905.3201	On the Statistics of Cognitive Radio Capacity in Shadowing and Fast Fading Environments	In this paper we consider the capacity of the cognitive radio channel in a fading environment under a "low interference regime". This capacity depends critically on a power loss parameter, $\alpha$, which governs how much transmit power the cognitive radio dedicates to relaying the primary message. We derive a simple, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,729
1905.08775	On Cycling Risk and Discomfort: Urban Safety Mapping and Bike Route Recommendations	Bike usage in Smart Cities becomes paramount for sustainable urban development. Cycling provides tremendous opportunities for a more healthy lifestyle, lower energy consumption and carbon emissions as well as reduction of traffic jams. While the number of cyclists increase along with the expansion of bike sharing initi...	false	false	false	false	false	true	true	false	false	false	false	false	false	true	false	false	false	false	131,562
2006.02575	Debiased Sinkhorn barycenters	Entropy regularization in optimal transport (OT) has been the driver of many recent interests for Wasserstein metrics and barycenters in machine learning. It allows to keep the appealing geometrical properties of the unregularized Wasserstein distance while having a significantly lower complexity thanks to Sinkhorn's a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	180,074
1904.04445	Semi-Supervised Segmentation of Salt Bodies in Seismic Images using an Ensemble of Convolutional Neural Networks	Seismic image analysis plays a crucial role in a wide range of industrial applications and has been receiving significant attention. One of the essential challenges of seismic imaging is detecting subsurface salt structure which is indispensable for identification of hydrocarbon reservoirs and drill path planning. Unfo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	127,039
2311.14729	App for Resume-Based Job Matching with Speech Interviews and Grammar Analysis: A Review	Through the advancement in natural language processing (NLP), specifically in speech recognition, fully automated complex systems functioning on voice input have started proliferating in areas such as home automation. These systems have been termed Automatic Speech Recognition Systems (ASR). In this review paper, we ex...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	410,226
2112.10493	Measuring Salinity and Density of Seawater Samples with Different Salt Compositions and Suspended Materials	Determining of the solute mass amount in seawater using in situ measurements in seas and oceans remains now an unresolved problem. To solve it, it is necessary to develop both new methods and instruments for measurements. This article analyzes methods for the indirect measuring of salinity and density using parameters ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	272,448
2211.16715	Policy Optimization over General State and Action Spaces	Reinforcement learning (RL) problems over general state and action spaces are notoriously challenging. In contrast to the tableau setting, one can not enumerate all the states and then iteratively update the policies for each state. This prevents the application of many well-studied RL methods especially those with pro...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	333,720
2109.07960	Efficient and Effective Generation of Test Cases for Pedestrian Detection -- Search-based Software Testing of Baidu Apollo in SVL	With the growing capabilities of autonomous vehicles, there is a higher demand for sophisticated and pragmatic quality assurance approaches for machine learning-enabled systems in the automotive AI context. The use of simulation-based prototyping platforms provides the possibility for early-stage testing, enabling inex...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	255,719
1601.07283	Balanced Reed-Solomon Codes	We consider the problem of constructing linear Maximum Distance Separable (MDS) error-correcting codes with generator matrices that are sparsest and balanced. In this context, sparsest means that every row has the least possible number of non-zero entries, and balanced means that every column contains the same number o...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	51,413
2105.01227	Causal factors discovering from Chinese construction accident cases	In China, construction accidents have killed more people than any other industry since 2012. The factors which led to the accident have complex interaction. Real data about accidents is the key to reveal the mechanism among these factors. But the data from the questionnaire and interview has inherent defects. Many beha...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	233,462
2009.03352	A Fast Randomized Algorithm for Finding the Maximal Common Subsequences	Finding the common subsequences of $L$ multiple strings has many applications in the area of bioinformatics, computational linguistics, and information retrieval. A well-known result states that finding a Longest Common Subsequence (LCS) for $L$ strings is NP-hard, e.g., the computational complexity is exponential in $...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	194,795
2310.00702	You Do Not Need Additional Priors in Camouflage Object Detection	Camouflage object detection (COD) poses a significant challenge due to the high resemblance between camouflaged objects and their surroundings. Although current deep learning methods have made significant progress in detecting camouflaged objects, many of them heavily rely on additional prior information. However, acqu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	396,103
1301.2218	Estimation from Relative Measurements in Mobile Networks with Markovian Switching Topology: Clock Skew and Offset Estimation for Time Synchronization	We analyze a distributed algorithm for estimation of scalar parameters belonging to nodes in a mobile network from noisy relative measurements. The motivation comes from the problem of clock skew and offset estimation for the purpose of time synchronization. The time variation of the network was modeled as a Markov cha...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	20,923
2412.18862	WeatherGS: 3D Scene Reconstruction in Adverse Weather Conditions via Gaussian Splatting	3D Gaussian Splatting (3DGS) has gained significant attention for 3D scene reconstruction, but still suffers from complex outdoor environments, especially under adverse weather. This is because 3DGS treats the artifacts caused by adverse weather as part of the scene and will directly reconstruct them, largely reducing ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	520,610
2311.09622	Homography Initialization and Dynamic Weighting Algorithm Based on a Downward-Looking Camera and IMU	In recent years, the technology in visual-inertial odometry (VIO) has matured considerably and has been widely used in many applications. However, we still encounter challenges when applying VIO to a micro air vehicle (MAV) equipped with a downward-looking camera. Specifically, VIO cannot compute the correct initializa...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	408,213
2302.06513	DEPAS: De-novo Pathology Semantic Masks using a Generative Model	The integration of artificial intelligence into digital pathology has the potential to automate and improve various tasks, such as image analysis and diagnostic decision-making. Yet, the inherent variability of tissues, together with the need for image labeling, lead to biased datasets that limit the generalizability o...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	345,430
1210.2474	Level Set Estimation from Compressive Measurements using Box Constrained Total Variation Regularization	Estimating the level set of a signal from measurements is a task that arises in a variety of fields, including medical imaging, astronomy, and digital elevation mapping. Motivated by scenarios where accurate and complete measurements of the signal may not available, we examine here a simple procedure for estimating the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	19,023
2010.11348	Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS Data	Within the domain of Computational Fluid Dynamics, Direct Numerical Simulation (DNS) is used to obtain highly accurate numerical solutions for fluid flows. However, this approach for numerically solving the Navier-Stokes equations is extremely computationally expensive mostly due to the requirement of greatly refined g...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	202,210
2311.06952	A GPU-Accelerated Moving-Horizon Algorithm for Training Deep Classification Trees on Large Datasets	Decision trees are essential yet NP-complete to train, prompting the widespread use of heuristic methods such as CART, which suffers from sub-optimal performance due to its greedy nature. Recently, breakthroughs in finding optimal decision trees have emerged; however, these methods still face significant computational ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	407,137
1002.3344	Iterative exact global histogram specification and SSIM gradient ascent: a proof of convergence, step size and parameter selection	The SSIM-optimized exact global histogram specification (EGHS) is shown to converge in the sense that the first order approximation of the result's quality (i.e., its structural similarity with input) does not decrease in an iteration, when the step size is small. Each iteration is composed of SSIM gradient ascent and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	5,730
2010.10505	SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images	Dense 3D object reconstruction from a single image has recently witnessed remarkable advances, but supervising neural networks with ground-truth 3D shapes is impractical due to the laborious process of creating paired image-shape datasets. Recent efforts have turned to learning 3D reconstruction without 3D supervision ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	201,904
2208.11625	PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Model	Quick global aggregation of effective distributed parameters is crucial to federated learning (FL), which requires adequate bandwidth for parameters communication and sufficient user data for local training. Otherwise, FL may cost excessive training time for convergence and produce inaccurate models. In this paper, we ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	314,493
2403.11996	Accelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph Reasoning	Leveraging generative Artificial Intelligence (AI), we have transformed a dataset comprising 1,000 scientific papers into an ontological knowledge graph. Through an in-depth structural analysis, we have calculated node degrees, identified communities and connectivities, and evaluated clustering coefficients and between...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	438,963
2310.00847	Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?	Out-of-distribution (OOD) detection is critical for safety-sensitive machine learning applications and has been extensively studied, yielding a plethora of methods developed in the literature. However, most studies for OOD detection did not use pre-trained models and trained a backbone from scratch. In recent years, tr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	396,178
2101.01169	Transformers in Vision: A Survey	Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Among their salient benefits, Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence as com...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	214,304
2410.17971	Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning	Spectrum access is an essential problem in device-to-device (D2D) communications. However, with the recent growth in the number of mobile devices, the wireless spectrum is becoming scarce, resulting in low spectral efficiency for D2D communications. To address this problem, this paper aims to integrate the ambient back...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	501,691
2308.00096	AirTouch: Towards Safe Human-Robot Interaction Using Air Pressure Feedback and IR Mocap System	The growing use of robots in urban environments has raised concerns about potential safety hazards, especially in public spaces where humans and robots may interact. In this paper, we present a system for safe human-robot interaction that combines an infrared (IR) camera with a wearable marker and airflow potential fie...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	382,811
2305.16502	Learning When to Ask for Help: Efficient Interactive Navigation via Implicit Uncertainty Estimation	Robots operating alongside humans often encounter unfamiliar environments that make autonomous task completion challenging. Though improving models and increasing dataset size can enhance a robot's performance in unseen environments, data collection and model refinement may be impractical in every environment. Approach...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	368,117
2209.10579	First-order Policy Optimization for Robust Markov Decision Process	We consider the problem of solving robust Markov decision process (MDP), which involves a set of discounted, finite state, finite action space MDPs with uncertain transition kernels. The goal of planning is to find a robust policy that optimizes the worst-case values against the transition uncertainties, and thus encom...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	318,903
1301.2785	A comparison of SVM and RVM for Document Classification	Document classification is a task of assigning a new unclassified document to one of the predefined set of classes. The content based document classification uses the content of the document with some weighting criteria to assign it to one of the predefined classes. It is a major task in library science, electronic doc...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	21,042
2403.12012	Convergence of Kinetic Langevin Monte Carlo on Lie groups	Explicit, momentum-based dynamics for optimizing functions defined on Lie groups was recently constructed, based on techniques such as variational optimization and left trivialization. We appropriately add tractable noise to the optimization dynamics to turn it into a sampling dynamics, leveraging the advantageous feat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	438,976
2502.11169	Leveraging Constrained Monte Carlo Tree Search to Generate Reliable Long Chain-of-Thought for Mathematical Reasoning	Recently, Long Chain-of-Thoughts (CoTs) have gained widespread attention for improving the reasoning capabilities of Large Language Models (LLMs). This necessitates that existing LLMs, which lack the ability to generate Long CoTs, to acquire such capability through post-training methods. Without additional training, LL...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	534,231
2011.06449	Sensors for expert grip force profiling: towards benchmarking manual control of a robotic device for surgical tool movements	STRAS (Single access Transluminal Robotic Assistant for Surgeons) is a new robotic system for application to intraluminal surgical procedures. Preclinical testing of STRAS has recently permitted to demonstrate major advantages of the system in comparison with classic procedures. Benchmark methods permitting to establis...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	206,251
1709.03209	Recurrent neural networks based Indic word-wise script identification using character-wise training	This paper presents a novel methodology of Indic handwritten script recognition using Recurrent Neural Networks and addresses the problem of script recognition in poor data scenarios, such as when only character level online data is available. It is based on the hypothesis that curves of online character data comprise ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	80,428
2106.10482	Unbalanced Feature Transport for Exemplar-based Image Translation	Despite the great success of GANs in images translation with different conditioned inputs such as semantic segmentation and edge maps, generating high-fidelity realistic images with reference styles remains a grand challenge in conditional image-to-image translation. This paper presents a general image translation fram...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	242,037
2004.00784	Learning Agile Robotic Locomotion Skills by Imitating Animals	Reproducing the diverse and agile locomotion skills of animals has been a longstanding challenge in robotics. While manually-designed controllers have been able to emulate many complex behaviors, building such controllers involves a time-consuming and difficult development process, often requiring substantial expertise...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	170,733
2004.00130	A+ Indexes: Tunable and Space-Efficient Adjacency Lists in Graph Database Management Systems	Graph database management systems (GDBMSs) are highly optimized to perform fast traversals, i.e., joins of vertices with their neighbours, by indexing the neighbourhoods of vertices in adjacency lists. However, existing GDBMSs have system-specific and fixed adjacency list structures, which makes each system efficient o...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	170,521
2304.02273	MMVC: Learned Multi-Mode Video Compression with Block-based Prediction Mode Selection and Density-Adaptive Entropy Coding	Learning-based video compression has been extensively studied over the past years, but it still has limitations in adapting to various motion patterns and entropy models. In this paper, we propose multi-mode video compression (MMVC), a block wise mode ensemble deep video compression framework that selects the optimal m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	356,386
1301.7417	Planning with Partially Observable Markov Decision Processes: Advances in Exact Solution Method	There is much interest in using partially observable Markov decision processes (POMDPs) as a formal model for planning in stochastic domains. This paper is concerned with finding optimal policies for POMDPs. We propose several improvements to incremental pruning, presently the most efficient exact algorithm for solving...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	21,650
2305.02982	Preliminary results of a therapeutic lab for promoting autonomies in autistic children	This extended abtract describes the preliminary qualitative results coming from a therapeutic laboratory focused on the use of the Pepper robot to promote autonomies and functional acquisitions in highly functioning (Asperger) children with autism. The field lab, ideated and led by a multidisciplinary team, involved 4 ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	362,232
2109.12030	Toward Efficient and Robust Multiple Camera Visual-inertial Odometry	Efficiency and robustness are the essential criteria for the visual-inertial odometry (VIO) system. To process massive visual data, the high cost on CPU resources and computation latency limits VIO's possibility in integration with other applications. Recently, the powerful embedded GPUs have great potentials to improv...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	257,134
2304.05105	Learning-based Rigid Tube Model Predictive Control	This paper is concerned with model predictive control (MPC) of discrete-time linear systems subject to bounded additive disturbance and mixed constraints on the state and input, whereas the true disturbance set is unknown. Unlike most existing work on robust MPC, we propose an algorithm incorporating online learning th...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	357,489
2004.01600	VGPN: Voice-Guided Pointing Robot Navigation for Humans	Pointing gestures are widely used in robot navigationapproaches nowadays. However, most approaches only use point-ing gestures, and these have two major limitations. Firstly, they need to recognize pointing gestures all the time, which leads to long processing time and significant system overheads. Secondly,the user's ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	170,956
2206.13413	RES: A Robust Framework for Guiding Visual Explanation	Despite the fast progress of explanation techniques in modern Deep Neural Networks (DNNs) where the main focus is handling "how to generate the explanations", advanced research questions that examine the quality of the explanation itself (e.g., "whether the explanations are accurate") and improve the explanation qualit...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	304,958
2109.11011	SOCIALGYM: A Framework for Benchmarking Social Robot Navigation	Robots moving safely and in a socially compliant manner in dynamic human environments is an essential benchmark for long-term robot autonomy. However, it is not feasible to learn and benchmark social navigation behaviors entirely in the real world, as learning is data-intensive, and it is challenging to make safety gua...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	256,812
2104.09952	MGSampler: An Explainable Sampling Strategy for Video Action Recognition	Frame sampling is a fundamental problem in video action recognition due to the essential redundancy in time and limited computation resources. The existing sampling strategy often employs a fixed frame selection and lacks the flexibility to deal with complex variations in videos. In this paper, we present a simple, spa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	231,414
2101.10737	Mining the Stars: Learning Quality Ratings with User-facing Explanations for Vacation Rentals	Online Travel Platforms are virtual two-sided marketplaces where guests search for accommodations and accommodation providers list their properties such as hotels and vacation rentals. The large majority of hotels are rated by official institutions with a number of stars indicating the quality of service they provide. ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	217,033
2106.01899	Adversarially Adaptive Normalization for Single Domain Generalization	Single domain generalization aims to learn a model that performs well on many unseen domains with only one domain data for training. Existing works focus on studying the adversarial domain augmentation (ADA) to improve the model's generalization capability. The impact on domain generalization of the statistics of norma...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	238,657
1711.06636	Segmenting Brain Tumors with Symmetry	We explore encoding brain symmetry into a neural network for a brain tumor segmentation task. A healthy human brain is symmetric at a high level of abstraction, and the high-level asymmetric parts are more likely to be tumor regions. Paying more attention to asymmetries has the potential to boost the performance in bra...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	84,817
2312.17173	Non-Vacuous Generalization Bounds for Large Language Models	Modern language models can contain billions of parameters, raising the question of whether they can generalize beyond the training data or simply parrot their training corpora. We provide the first non-vacuous generalization bounds for pretrained large language models (LLMs), indicating that language models are capable...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	418,641
cs/0106035	Polymorphic type inference for the relational algebra	We give a polymorphic account of the relational algebra. We introduce a formalism of ``type formulas'' specifically tuned for relational algebra expressions, and present an algorithm that computes the ``principal'' type for a given expression. The principal type of an expression is a formula that specifies, in a clear ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	537,369
2006.01304	Rethinking Empirical Evaluation of Adversarial Robustness Using First-Order Attack Methods	We identify three common cases that lead to overestimation of adversarial accuracy against bounded first-order attack methods, which is popularly used as a proxy for adversarial robustness in empirical studies. For each case, we propose compensation methods that either address sources of inaccurate gradient computation...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	179,736
2206.07568	Contrastive Learning as Goal-Conditioned Reinforcement Learning	In reinforcement learning (RL), it is easier to solve a task if given a good representation. While deep RL should automatically acquire such good representations, prior work often finds that learning representations in an end-to-end fashion is unstable and instead equip RL algorithms with additional representation lear...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	302,788
2406.05348	Toward Reliable Ad-hoc Scientific Information Extraction: A Case Study on Two Materials Datasets	We explore the ability of GPT-4 to perform ad-hoc schema based information extraction from scientific literature. We assess specifically whether it can, with a basic prompting approach, replicate two existing material science datasets, given the manuscripts from which they were originally manually extracted. We employ ...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	462,092
2302.10243	Mallat Scattering Transformation based surrogate for MagnetoHydroDynamics	A Machine and Deep Learning methodology is developed and applied to give a high fidelity, fast surrogate for 2D resistive MHD simulations of MagLIF implosions. The resistive MHD code GORGON is used to generate an ensemble of implosions with different liner aspect ratios, initial gas preheat temperatures (that is, diffe...	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	346,717
1404.2728	Real-time Decolorization using Dominant Colors	Decolorization is the process to convert a color image or video to its grayscale version, and it has received great attention in recent years. An ideal decolorization algorithm should preserve the original color contrast as much as possible. Meanwhile, it should provide the final decolorized result as fast as possible....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	32,230
1807.11634	Interactive Summarization and Exploration of Top Aggregate Query Answers	We present a system for summarization and interactive exploration of high-valued aggregate query answers to make a large set of possible answers more informative to the user. Our system outputs a set of clusters on the high-valued query answers showing their common properties such that the clusters are diverse as much ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	104,215
2109.07358	Fermion Sampling Made More Efficient	Fermion sampling is to generate probability distribution of a many-body Slater-determinant wavefunction, which is termed "determinantal point process" in statistical analysis. For its inherently-embedded Pauli exclusion principle, its application reaches beyond simulating fermionic quantum many-body physics to construc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	255,493
1904.00325	ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification with Chest X-rays	Image representation is a fundamental task in computer vision. However, most of the existing approaches for image representation ignore the relations between images and consider each input image independently. Intuitively, relations between images can help to understand the images and maintain model consistency over re...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	125,849

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