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
2405.20071	A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture	Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. We propose a novel staged model that combine...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	459,182
1803.02042	Accelerated Gradient Boosting	Gradient tree boosting is a prediction algorithm that sequentially produces a model in the form of linear combinations of decision trees, by solving an infinite-dimensional optimization problem. We combine gradient boosting and Nesterov's accelerated descent to design a new algorithm, which we call AGB (for Accelerated...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	91,979
2204.13604	MeSHup: A Corpus for Full Text Biomedical Document Indexing	Medical Subject Heading (MeSH) indexing refers to the problem of assigning a given biomedical document with the most relevant labels from an extremely large set of MeSH terms. Currently, the vast number of biomedical articles in the PubMed database are manually annotated by human curators, which is time consuming and c...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	293,873
2412.08763	Beyond Knowledge Silos: Task Fingerprinting for Democratization of Medical Imaging AI	The field of medical imaging AI is currently undergoing rapid transformations, with methodical research increasingly translated into clinical practice. Despite these successes, research suffers from knowledge silos, hindering collaboration and progress: Existing knowledge is scattered across publications and many detai...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	516,217
1902.06562	Intra- and Inter-epoch Temporal Context Network (IITNet) Using Sub-epoch Features for Automatic Sleep Scoring on Raw Single-channel EEG	A deep learning model, named IITNet, is proposed to learn intra- and inter-epoch temporal contexts from raw single-channel EEG for automatic sleep scoring. To classify the sleep stage from half-minute EEG, called an epoch, sleep experts investigate sleep-related events and consider the transition rules between the foun...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	121,791
2008.03247	Investigation of Speaker-adaptation methods in Transformer based ASR	End-to-end models are fast replacing the conventional hybrid models in automatic speech recognition. Transformer, a sequence-to-sequence model, based on self-attention popularly used in machine translation tasks, has given promising results when used for automatic speech recognition. This paper explores different ways ...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	190,848
1906.03169	A Novel Deep Neural Network Based Approach for Sparse Code Multiple Access	Sparse code multiple access (SCMA) has been one of non-orthogonal multiple access (NOMA) schemes aiming to support high spectral efficiency and ubiquitous access requirements for 5G wireless communication networks. Conventional SCMA approaches are confronting remarkable challenges in designing low complexity high accur...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	134,296
2404.03988	Model Selection with Model Zoo via Graph Learning	Pre-trained deep learning (DL) models are increasingly accessible in public repositories, i.e., model zoos. Given a new prediction task, finding the best model to fine-tune can be computationally intensive and costly, especially when the number of pre-trained models is large. Selecting the right pre-trained models is c...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	444,469
1810.05983	Finding Similar Medical Questions from Question Answering Websites	The past few years have witnessed the flourishing of crowdsourced medical question answering (Q&A) websites. Patients who have medical information demands tend to post questions about their health conditions on these crowdsourced Q&A websites and get answers from other users. However, we observe that a large portion of...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	110,357
2108.05969	Scalable3-BO: Big Data meets HPC - A scalable asynchronous parallel high-dimensional Bayesian optimization framework on supercomputers	Bayesian optimization (BO) is a flexible and powerful framework that is suitable for computationally expensive simulation-based applications and guarantees statistical convergence to the global optimum. While remaining as one of the most popular optimization methods, its capability is hindered by the size of data, the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	250,467
1012.1565	A Survey on Data Warehouse Evolution	The data warehouse (DW) technology was developed to integrate heterogeneous information sources for analysis purposes. Information sources are more and more autonomous and they often change their content due to perpetual transactions (data changes) and may change their structure due to continual users' requirements evo...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	8,446
1908.06376	VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual Navigation	In this paper, we show how novel transfer reinforcement learning techniques can be applied to the complex task of target driven navigation using the photorealistic AI2THOR simulator. Specifically, we build on the concept of Universal Successor Features with an A3C agent. We introduce the novel architectural contributio...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	141,999
2412.13536	MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules	Recent studies have highlighted the limitations of large language models in mathematical reasoning, particularly their inability to capture the underlying logic. Inspired by meta-learning, we propose that models should acquire not only task-specific knowledge but also transferable problem-solving skills. We introduce M...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	518,329
2106.01128	Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs	The ability to align points across two related yet incomparable point clouds (e.g. living in different spaces) plays an important role in machine learning. The Gromov-Wasserstein (GW) framework provides an increasingly popular answer to such problems, by seeking a low-distortion, geometry-preserving assignment between ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	238,398
2101.05996	Convolutional Neural Network with Pruning Method for Handwritten Digit Recognition	CNN model is a popular method for imagery analysis, so it could be utilized to recognize handwritten digits based on MNIST datasets. For higher recognition accuracy, various CNN models with different fully connected layer sizes are exploited to figure out the relationship between the CNN fully connected layer size and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	215,581
2412.17961	Extending Graph Condensation to Multi-Label Datasets: A Benchmark Study	As graph data grows increasingly complicate, training graph neural networks (GNNs) on large-scale datasets presents significant challenges, including computational resource constraints, data redundancy, and transmission inefficiencies. While existing graph condensation techniques have shown promise in addressing these ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	520,187
2206.00241	Asymptotic Properties for Bayesian Neural Network in Besov Space	Neural networks have shown great predictive power when dealing with various unstructured data such as images and natural languages. The Bayesian neural network captures the uncertainty of prediction by putting a prior distribution for the parameter of the model and computing the posterior distribution. In this paper, w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	300,044
1708.05133	Deep Scene Text Detection with Connected Component Proposals	A growing demand for natural-scene text detection has been witnessed by the computer vision community since text information plays a significant role in scene understanding and image indexing. Deep neural networks are being used due to their strong capabilities of pixel-wise classification or word localization, similar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	79,081
1401.7114	Fundamental Limits in Correlated Fading MIMO Broadcast Channels: Benefits of Transmit Correlation Diversity	We investigate asymptotic capacity limits of the Gaussian MIMO broadcast channel (BC) with spatially correlated fading to understand when and how much transmit correlation helps the capacity. By imposing a structure on channel covariances (equivalently, transmit correlations at the transmitter side) of users, also refe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	30,426
2009.13964	CokeBERT: Contextual Knowledge Selection and Embedding towards Enhanced Pre-Trained Language Models	Several recent efforts have been devoted to enhancing pre-trained language models (PLMs) by utilizing extra heterogeneous knowledge in knowledge graphs (KGs) and achieved consistent improvements on various knowledge-driven NLP tasks. However, most of these knowledge-enhanced PLMs embed static sub-graphs of KGs ("knowle...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	197,899
2007.00937	A differential neural network learns stochastic differential equations and the Black-Scholes equation for pricing multi-asset options	Neural networks with sufficiently smooth activation functions can approximate values and derivatives of any smooth function, and they are differentiable themselves. We improve the approximation capability of neural networks by utilizing the differentiability of neural networks; the gradient and Hessian of neural networ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	185,274
2103.09203	ReconResNet: Regularised Residual Learning for MR Image Reconstruction of Undersampled Cartesian and Radial Data	MRI is an inherently slow process, which leads to long scan time for high-resolution imaging. The speed of acquisition can be increased by ignoring parts of the data (undersampling). Consequently, this leads to the degradation of image quality, such as loss of resolution or introduction of image artefacts. This work ai...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	225,108
1909.01568	AMR Normalization for Fairer Evaluation	Meaning Representation (AMR; Banarescu et al., 2013) encodes the meaning of sentences as a directed graph and Smatch (Cai and Knight, 2013) is the primary metric for evaluating AMR graphs. Smatch, however, is unaware of some meaning-equivalent variations in graph structure allowed by the AMR Specification and gives dif...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	143,942
2402.00672	Exploring Homogeneous and Heterogeneous Consistent Label Associations for Unsupervised Visible-Infrared Person ReID	Unsupervised visible-infrared person re-identification (USL-VI-ReID) endeavors to retrieve pedestrian images of the same identity from different modalities without annotations. While prior work focuses on establishing cross-modality pseudo-label associations to bridge the modality-gap, they ignore maintaining the insta...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	425,675
2107.09627	Precision-Weighted Federated Learning	Federated Learning using the Federated Averaging algorithm has shown great advantages for large-scale applications that rely on collaborative learning, especially when the training data is either unbalanced or inaccessible due to privacy constraints. We hypothesize that Federated Averaging underestimates the full exten...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	247,080
2302.02006	Robust Budget Pacing with a Single Sample	Major Internet advertising platforms offer budget pacing tools as a standard service for advertisers to manage their ad campaigns. Given the inherent non-stationarity in an advertiser's value and also competing advertisers' values over time, a commonly used approach is to learn a target expenditure plan that specifies ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	343,814
2311.03584	Dimensions of Online Conflict: Towards Modeling Agonism	Agonism plays a vital role in democratic dialogue by fostering diverse perspectives and robust discussions. Within the realm of online conflict there is another type: hateful antagonism, which undermines constructive dialogue. Detecting conflict online is central to platform moderation and monetization. It is also vita...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	405,906
2409.19226	Learning to Bridge the Gap: Efficient Novelty Recovery with Planning and Reinforcement Learning	The real world is unpredictable. Therefore, to solve long-horizon decision-making problems with autonomous robots, we must construct agents that are capable of adapting to changes in the environment during deployment. Model-based planning approaches can enable robots to solve complex, long-horizon tasks in a variety of...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	492,589
2402.06695	Integrating LLMs for Explainable Fault Diagnosis in Complex Systems	This paper introduces an integrated system designed to enhance the explainability of fault diagnostics in complex systems, such as nuclear power plants, where operator understanding is critical for informed decision-making. By combining a physics-based diagnostic tool with a Large Language Model, we offer a novel solut...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	428,403
1212.5679	Cumulative Distance Enumerators of Random Codes and their Thresholds	Cumulative weight enumerators of random linear codes are introduced, their asymptotic properties are studied, and very sharp thresholds are exhibited; as a consequence, it is shown that the asymptotic Gilbert-Varshamov bound is a very sharp threshold point for the density of the linear codes whose relative distance is ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	20,580
1903.01013	Hand Pose Estimation: A Survey	The success of Deep Convolutional Neural Networks (CNNs) in recent years in almost all the Computer Vision tasks on one hand, and the popularity of low-cost consumer depth cameras on the other, has made Hand Pose Estimation a hot topic in computer vision field. In this report, we will first explain the hand pose estima...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	123,166
1805.06375	#phramacovigilance - Exploring Deep Learning Techniques for Identifying Mentions of Medication Intake from Twitter	Mining social media messages for health and drug related information has received significant interest in pharmacovigilance research. Social media sites (e.g., Twitter), have been used for monitoring drug abuse, adverse reactions of drug usage and analyzing expression of sentiments related to drugs. Most of these studi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	97,595
1705.09711	Is It Reasonable to Substitute Discontinuous SMC by Continuous HOSMC?	Professor Utkin in his discussion paper proposed an example showing that the amplitude of chattering caused by the presence of parasitic dynamics in systems governed by First-Order Sliding-Mode Control (FOSMC) is lower than the obtained using Super-Twisting Algorithm (STA). This example served to motivate this research...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	74,247
2208.03926	Achievable Refined Asymptotics for Successive Refinement Using Gaussian Codebooks	We study the mismatched successive refinement problem where one uses Gaussian codebooks to compress an arbitrary memoryless source with successive minimum Euclidean distance encoding under the quadratic distortion measure. Specifically, we derive achievable refined asymptotics under both the joint excess-distortion pro...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	311,942
1812.00880	Joint Mapping and Calibration via Differentiable Sensor Fusion	We leverage automatic differentiation (AD) and probabilistic programming to develop an end-to-end optimization algorithm for batch triangulation of a large number of unknown objects. Given noisy detections extracted from noisily geo-located street level imagery without depth information, we jointly estimate the number ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	115,368
2403.06636	Design and Control of Delta: Deformable Multilinked Multirotor with Rolling Locomotion Ability in Terrestrial Domain	In recent years, multiple types of locomotion methods for robots have been developed and enabled to adapt to multiple domains. In particular, aerial robots are useful for exploration in several situations, taking advantage of its three-dimensional mobility. Moreover, some aerial robots have achieved manipulation tasks ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	436,541
1904.10424	Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting	For person re-identification, existing deep networks often focus on representation learning. However, without transfer learning, the learned model is fixed as is, which is not adaptable for handling various unseen scenarios. In this paper, beyond representation learning, we consider how to formulate person image matchi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	128,626
1912.03549	lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal data	Longitudinal study designs are indispensable for studying disease progression. Inferring covariate effects from longitudinal data, however, requires interpretable methods that can model complicated covariance structures and detect nonlinear effects of both categorical and continuous covariates, as well as their interac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	156,620
2205.14851	Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models	Face anti-spoofing aims to discriminate the spoofing face images (e.g., printed photos) from live ones. However, adversarial examples greatly challenge its credibility, where adding some perturbation noise can easily change the predictions. Previous works conducted adversarial attack methods to evaluate the face anti-s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	299,515
2010.12718	Learning Guidance Rewards with Trajectory-space Smoothing	Long-term temporal credit assignment is an important challenge in deep reinforcement learning (RL). It refers to the ability of the agent to attribute actions to consequences that may occur after a long time interval. Existing policy-gradient and Q-learning algorithms typically rely on dense environmental rewards that ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	202,815
2410.03989	Symmetry From Scratch: Group Equivariance as a Supervised Learning Task	In machine learning datasets with symmetries, the paradigm for backward compatibility with symmetry-breaking has been to relax equivariant architectural constraints, engineering extra weights to differentiate symmetries of interest. However, this process becomes increasingly over-engineered as models are geared towards...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	495,079
2009.11742	A compute-bound formulation of Galerkin model reduction for linear time-invariant dynamical systems	This work aims to advance computational methods for projection-based reduced order models (ROMs) of linear time-invariant (LTI) dynamical systems. For such systems, current practice relies on ROM formulations expressing the state as a rank-1 tensor (i.e., a vector), leading to computational kernels that are memory band...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	197,243
2212.04497	UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation	Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks. Within the transformer models, the self-attention mechanism is one of the main building blocks that strives to capture long-range dependencies. However, the self-attention operation has quadratic complex...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	335,465
2310.09453	Effects of Same-Race Mentorship Preferences on Academic Performance and Survival	Same-race mentorship preference refers to mentors or mentees forming connections significantly influenced by a shared race. Although racial diversity in science has been well-studied and linked to favorable outcomes, the extent and effects of same-race mentorship preferences remain largely underexplored. Here, we analy...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	399,777
2409.17111	Self-Sensing for Proprioception and Contact Detection in Soft Robots Using Shape Memory Alloy Artificial Muscles	Estimating a soft robot's pose and applied forces, also called proprioception, is crucial for safe interaction of the robot with its environment. However, most solutions for soft robot proprioception use dedicated sensors, particularly for external forces, which introduce design trade-offs, rigidity, and risk of failur...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	491,654
2211.12222	Event Transformer+. A multi-purpose solution for efficient event data processing	Event cameras record sparse illumination changes with high temporal resolution and high dynamic range. Thanks to their sparse recording and low consumption, they are increasingly used in applications such as AR/VR and autonomous driving. Current topperforming methods often ignore specific event-data properties, leading...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	332,041
2401.06836	Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought	Large Language Models (LLMs) have shown remarkable performance in various emotion recognition tasks, thereby piquing the research community's curiosity for exploring their potential in emotional intelligence. However, several issues in the field of emotional generation tasks remain unresolved, including human preferenc...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	421,318
2401.10394	Distribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels	Few-shot node classification poses a significant challenge for Graph Neural Networks (GNNs) due to insufficient supervision and potential distribution shifts between labeled and unlabeled nodes. Self-training has emerged as a widely popular framework to leverage the abundance of unlabeled data, which expands the traini...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	422,619
2401.13905	Dynamic embedded topic models and change-point detection for exploring literary-historical hypotheses	We present a novel combination of dynamic embedded topic models and change-point detection to explore diachronic change of lexical semantic modality in classical and early Christian Latin. We demonstrate several methods for finding and characterizing patterns in the output, and relating them to traditional scholarship ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	423,890
1301.7359	Merging Uncertain Knowledge Bases in a Possibilistic Logic Framework	This paper addresses the problem of merging uncertain information in the framework of possibilistic logic. It presents several syntactic combination rules to merge possibilistic knowledge bases, provided by different sources, into a new possibilistic knowledge base. These combination rules are first described at the me...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	21,593
2409.06154	Static for Dynamic: Towards a Deeper Understanding of Dynamic Facial Expressions Using Static Expression Data	Dynamic facial expression recognition (DFER) infers emotions from the temporal evolution of expressions, unlike static facial expression recognition (SFER), which relies solely on a single snapshot. This temporal analysis provides richer information and promises greater recognition capability. However, current DFER met...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	487,017
2209.15159	MobileViTv3: Mobile-Friendly Vision Transformer with Simple and Effective Fusion of Local, Global and Input Features	MobileViT (MobileViTv1) combines convolutional neural networks (CNNs) and vision transformers (ViTs) to create light-weight models for mobile vision tasks. Though the main MobileViTv1-block helps to achieve competitive state-of-the-art results, the fusion block inside MobileViTv1-block, creates scaling challenges and h...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	320,488
1604.03426	Sweep Distortion Removal from THz Images via Blind Demodulation	Heavy sweep distortion induced by alignments and inter-reflections of layers of a sample is a major burden in recovering 2D and 3D information in time resolved spectral imaging. This problem cannot be addressed by conventional denoising and signal processing techniques as it heavily depends on the physics of the acquis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	54,506
2003.12382	PyMatting: A Python Library for Alpha Matting	An important step of many image editing tasks is to extract specific objects from an image in order to place them in a scene of a movie or compose them onto another background. Alpha matting describes the problem of separating the objects in the foreground from the background of an image given only a rough sketch. We i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	169,904
2304.06016	PD-ADSV: An Automated Diagnosing System Using Voice Signals and Hard Voting Ensemble Method for Parkinson's Disease	Parkinson's disease (PD) is the most widespread movement condition and the second most common neurodegenerative disorder, following Alzheimer's. Movement symptoms and imaging techniques are the most popular ways to diagnose this disease. However, they are not accurate and fast and may only be accessible to a few people...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	357,817
2411.04681	A dynamical model of platform choice and online segregation	In order to truly understand how social media might shape online discourses or contribute to societal polarization, we need refined models of platform choice, that is: models that help us understand why users prefer one social media platform over another. This study develops a dynamic model of platform selection, exten...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	506,368
1707.05947	Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints	Algorithm-dependent generalization error bounds are central to statistical learning theory. A learning algorithm may use a large hypothesis space, but the limited number of iterations controls its model capacity and generalization error. The impacts of stochastic gradient methods on generalization error for non-convex ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	77,324
2009.04794	MAT: Motion-Aware Multi-Object Tracking	Modern multi-object tracking (MOT) systems usually model the trajectories by associating per-frame detections. However, when camera motion, fast motion, and occlusion challenges occur, it is difficult to ensure long-range tracking or even the tracklet purity, especially for small objects. Although re-identification is ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	195,146
2411.04501	Pose2Trajectory: Using Transformers on Body Pose to Predict Tennis Player's Trajectory	Tracking the trajectory of tennis players can help camera operators in production. Predicting future movement enables cameras to automatically track and predict a player's future trajectory without human intervention. Predicting future human movement in the context of complex physical tasks is also intellectually satis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	506,294
2410.00544	Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research	Multi-fidelity Bayesian Optimization (MFBO) is a promising framework to speed up materials and molecular discovery as sources of information of different accuracies are at hand at increasing cost. Despite its potential use in chemical tasks, there is a lack of systematic evaluation of the many parameters playing a role...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	493,427
2312.00535	RIS-Based On-the-Air Semantic Communications -- a Diffractional Deep Neural Network Approach	Semantic communication has gained significant attention recently due to its advantages in achieving higher transmission efficiency by focusing on semantic information instead of bit-level information. However, current AI-based semantic communication methods require digital hardware for implementation. With the rapid ad...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	412,084
cs/0703124	Modelling Complexity in Musical Rhythm	This paper constructs a tree structure for the music rhythm using the L-system. It models the structure as an automata and derives its complexity. It also solves the complexity for the L-system. This complexity can resolve the similarity between trees. This complexity serves as a measure of psychological complexity for...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	540,260
1202.5284	Elitism Levels Traverse Mechanism For The Derivation of Upper Bounds on Unimodal Functions	In this article we present an Elitism Levels Traverse Mechanism that we designed to find bounds on population-based Evolutionary algorithms solving unimodal functions. We prove its efficiency theoretically and test it on OneMax function deriving bounds c{\mu}n log n - O({\mu} n). This analysis can be generalized to any...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	14,544
2108.09779	Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger	We present a system for learning a challenging dexterous manipulation task involving moving a cube to an arbitrary 6-DoF pose with only 3-fingers trained with NVIDIA's IsaacGym simulator. We show empirical benefits, both in simulation and sim-to-real transfer, of using keypoints as opposed to position+quaternion repres...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	251,706
2501.14588	Data Assetization via Resources-decoupled Federated Learning	With the development of the digital economy, data is increasingly recognized as an essential resource for both work and life. However, due to privacy concerns, data owners tend to maximize the value of data through the circulation of information rather than direct data transfer. Federated learning (FL) provides an effe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	527,177
1210.2067	An Approximation of the First Order Marcum $Q$-Function with Application to Network Connectivity Analysis	An exponential-type approximation of the first order Marcum $Q$-function is presented, which is robust to changes in its first argument and can easily be integrated with respect to the second argument. Such characteristics are particularly useful in network connectivity analysis. The proposed approximation is exact in ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	18,985
2006.11695	Uncertainty-Aware (UNA) Bases for Deep Bayesian Regression Using Multi-Headed Auxiliary Networks	Neural Linear Models (NLM) are deep Bayesian models that produce predictive uncertainties by learning features from the data and then performing Bayesian linear regression over these features. Despite their popularity, few works have focused on methodically evaluating the predictive uncertainties of these models. In th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,337
2407.19886	A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation	With the rapid development of online multimedia services, especially in e-commerce platforms, there is a pressing need for personalised recommendation systems that can effectively encode the diverse multi-modal content associated with each item. However, we argue that existing multi-modal recommender systems typically ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	476,968
2302.14208	Methods and Mechanisms for Interactive Novelty Handling in Adversarial Environments	Learning to detect, characterize and accommodate novelties is a challenge that agents operating in open-world domains need to address to be able to guarantee satisfactory task performance. Certain novelties (e.g., changes in environment dynamics) can interfere with the performance or prevent agents from accomplishing t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	348,195
1903.05882	Guaranteed Control of Sampled Switched Systems using Semi-Lagrangian Schemes and One-Sided Lipschitz Constants	In this paper, we propose a new method for ensuring formally that a controlled trajectory stay inside a given safety set S for a given duration T. Using a finite gridding X of S, we first synthesize, for a subset of initial nodes x of X , an admissible control for which the Euler-based approximate trajectories lie in S...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	124,256
1807.10029	LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks	Although weight and activation quantization is an effective approach for Deep Neural Network (DNN) compression and has a lot of potentials to increase inference speed leveraging bit-operations, there is still a noticeable gap in terms of prediction accuracy between the quantized model and the full-precision model. To a...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	103,861
1406.0905	Provenance and data differencing for workflow reproducibility analysis	One of the foundations of science is that researchers must publish the methodology used to achieve their results so that others can attempt to reproduce them. This has the added benefit of allowing methods to be adopted and adapted for other purposes. In the field of e-Science, services -- often choreographed through w...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	33,579
2502.05462	Motion Planning of Nonholonomic Cooperative Mobile Manipulators	We propose a real-time implementable motion planning technique for cooperative object transportation by nonholonomic mobile manipulator robots (MMRs) in an environment with static and dynamic obstacles. The proposed motion planning technique works in two steps. A novel visibility vertices-based path planning algorithm ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	531,622
2008.03973	Deep Reinforcement Learning with Label Embedding Reward for Supervised Image Hashing	Deep hashing has shown promising results in image retrieval and recognition. Despite its success, most existing deep hashing approaches are rather similar: either multi-layer perceptron or CNN is applied to extract image feature, followed by different binarization activation functions such as sigmoid, tanh or autoencod...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	191,087
1803.00628	GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger	This work describes the development of a high-resolution tactile-sensing finger for robot grasping. This finger, inspired by previous GelSight sensing techniques, features an integration that is slimmer, more robust, and with more homogeneous output than previous vision-based tactile sensors. To achieve a compact integ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	91,710
2109.11993	A Scenario-oriented Approach to Multi-period Energy-Reserve Joint Procurement and Pricing	In [1], a single-period co-optimization model of energy and reserve is considered to better illustrate the properties of the co-optimization model and the associated market mechanism. To make the discussion more general, in this paper, the single-period co-optimization model (II) in [1] will be extended into a one-shot...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	257,119
2301.04608	Padding Module: Learning the Padding in Deep Neural Networks	During the last decades, many studies have been dedicated to improving the performance of neural networks, for example, the network architectures, initialization, and activation. However, investigating the importance and effects of learnable padding methods in deep learning remains relatively open. To mitigate the gap,...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	340,113
2108.08296	Deep Contrastive Multiview Network Embedding	Multiview network embedding aims at projecting nodes in the network to low-dimensional vectors, while preserving their multiple relations and attribute information. Contrastive learning approaches have shown promising performance in this task. However, they neglect the semantic consistency between fused and view repres...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	251,210
2304.08865	Romanization-based Large-scale Adaptation of Multilingual Language Models	Large multilingual pretrained language models (mPLMs) have become the de facto state of the art for cross-lingual transfer in NLP. However, their large-scale deployment to many languages, besides pretraining data scarcity, is also hindered by the increase in vocabulary size and limitations in their parameter budget. In...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	358,849
2010.00702	Learned Dual-View Reflection Removal	Traditional reflection removal algorithms either use a single image as input, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconvenient for users. We instead propose a learning-based dereflection algorithm that uses stereo images as input. This is an effective trade-of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	198,360
1803.09208	Unsupervised Domain Adaptation: A Multi-task Learning-based Method	This paper presents a novel multi-task learning-based method for unsupervised domain adaptation. Specifically, the source and target domain classifiers are jointly learned by considering the geometry of target domain and the divergence between the source and target domains based on the concept of multi-task learning. T...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	93,453
2406.00907	DDA: Dimensionality Driven Augmentation Search for Contrastive Learning in Laparoscopic Surgery	Self-supervised learning (SSL) has potential for effective representation learning in medical imaging, but the choice of data augmentation is critical and domain-specific. It remains uncertain if general augmentation policies suit surgical applications. In this work, we automate the search for suitable augmentation pol...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	460,077
1105.4618	Bounding the Fat Shattering Dimension of a Composition Function Class Built Using a Continuous Logic Connective	We begin this report by describing the Probably Approximately Correct (PAC) model for learning a concept class, consisting of subsets of a domain, and a function class, consisting of functions from the domain to the unit interval. Two combinatorial parameters, the Vapnik-Chervonenkis (VC) dimension and its generalizati...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	10,475
2009.00514	XCSP3-core: A Format for Representing Constraint Satisfaction/Optimization Problems	In this document, we introduce XCSP3-core, a subset of XCSP3 that allows us to represent constraint satisfaction/optimization problems. The interest of XCSP3-core is multiple: (i) focusing on the most popular frameworks (CSP and COP) and constraints, (ii) facilitating the parsing process by means of dedicated XCSP3-cor...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	194,061
2410.17957	MCUBERT: Memory-Efficient BERT Inference on Commodity Microcontrollers	In this paper, we propose MCUBERT to enable language models like BERT on tiny microcontroller units (MCUs) through network and scheduling co-optimization. We observe the embedding table contributes to the major storage bottleneck for tiny BERT models. Hence, at the network level, we propose an MCU-aware two-stage neura...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	501,683
1810.09079	Sparsemax and Relaxed Wasserstein for Topic Sparsity	Topic sparsity refers to the observation that individual documents usually focus on several salient topics instead of covering a wide variety of topics, and a real topic adopts a narrow range of terms instead of a wide coverage of the vocabulary. Understanding this topic sparsity is especially important for analyzing u...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	110,979
2409.03891	Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality	We consider the overfitting behavior of minimum norm interpolating solutions of Gaussian kernel ridge regression (i.e. kernel ridgeless regression), when the bandwidth or input dimension varies with the sample size. For fixed dimensions, we show that even with varying or tuned bandwidth, the ridgeless solution is never...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	486,210
1802.06292	Nonparametric Estimation of Low Rank Matrix Valued Function	Let $A:[0,1]\rightarrow\mathbb{H}_m$ (the space of Hermitian matrices) be a matrix valued function which is low rank with entries in H\"{o}lder class $\Sigma(\beta,L)$. The goal of this paper is to study statistical estimation of $A$ based on the regression model $\mathbb{E}(Y_j\|\tau_j,X_j) = \langle A(\tau_j), X_j \ra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	90,635
2110.04157	Velocity Level Approximation of Pressure Field Contact Patches	Pressure Field Contact (PFC) was recently introduced as a method for detailed modeling of contact interface regions at rates much faster than elasticity-theory models, while at the same time predicting essential trends and capturing rich contact behavior. The PFC model was designed to work in conjunction with error-con...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	259,779
1803.01373	A Successive Optimization Approach to Pilot Design for Multi-Cell Massive MIMO Systems	In this letter, we introduce a novel pilot design approach that minimizes the total mean square errors of the minimum mean square error estimators of all base stations (BSs) subject to the transmit power constraints of individual users in the network, while tackling the pilot contamination in multi-cell Massive MIMO sy...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	91,863
2201.05923	Theoretical analysis and computation of the sample Frechet mean for sets of large graphs based on spectral information	To characterize the location (mean, median) of a set of graphs, one needs a notion of centrality that is adapted to metric spaces, since graph sets are not Euclidean spaces. A standard approach is to consider the Frechet mean. In this work, we equip a set of graphs with the pseudometric defined by the norm between the ...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	275,551
2207.05140	Field Evaluation of Four Low-cost PM Sensors and Design, Development and Field Evaluation of A Wearable PM Exposure Monitoring System	To mitigate the significant biases/errors in research studying the associations between PM and health, which are introduced by the coarse/inadequate assessments of PM exposure from conventional PM monitoring paradigm, a personalized monitoring system consisting of a low-cost wearable PM device is proposed. However, due...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	307,429
1510.02644	Free-hand Sketch Synthesis with Deformable Stroke Models	We present a generative model which can automatically summarize the stroke composition of free-hand sketches of a given category. When our model is fit to a collection of sketches with similar poses, it discovers and learns the structure and appearance of a set of coherent parts, with each part represented by a group o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	47,735
2106.08364	Unsupervised Enrichment of Persona-grounded Dialog with Background Stories	Humans often refer to personal narratives, life experiences, and events to make a conversation more engaging and rich. While persona-grounded dialog models are able to generate responses that follow a given persona, they often miss out on stating detailed experiences or events related to a persona, often leaving conver...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	241,261
1709.07357	Retrofitting Concept Vector Representations of Medical Concepts to Improve Estimates of Semantic Similarity and Relatedness	Estimation of semantic similarity and relatedness between biomedical concepts has utility for many informatics applications. Automated methods fall into two categories: methods based on distributional statistics drawn from text corpora, and methods using the structure of existing knowledge resources. Methods in the for...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	81,263
2105.10117	Towards Automatic Comparison of Data Privacy Documents: A Preliminary Experiment on GDPR-like Laws	General Data Protection Regulation (GDPR) becomes a standard law for data protection in many countries. Currently, twelve countries adopt the regulation and establish their GDPR-like regulation. However, to evaluate the differences and similarities of these GDPR-like regulations is time-consuming and needs a lot of man...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	236,276
2502.00008	Zoning in American Cities: Are Reforms Making a Difference? An AI-based Analysis	Cities are at the forefront of addressing global sustainability challenges, particularly those exacerbated by climate change. Traditional zoning codes, which often segregate land uses, have been linked to increased vehicular dependence, urban sprawl, and social disconnection, undermining broader social and environmenta...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	529,158
2310.14184	Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis	$\textit{Implicit neural representations}$ (INRs) aim to learn a $\textit{continuous function}$ (i.e., a neural network) to represent an image, where the input and output of the function are pixel coordinates and RGB/Gray values, respectively. However, images tend to consist of many objects whose colors are not perfect...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	401,748
1602.07884	Firefly Algorithm for optimization problems with non-continuous variables: A Review and Analysis	Firefly algorithm is a swarm based metaheuristic algorithm inspired by the flashing behavior of fireflies. It is an effective and an easy to implement algorithm. It has been tested on different problems from different disciplines and found to be effective. Even though the algorithm is proposed for optimization problems...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	52,578
2307.13621	Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points	Idealized first-principles models of chemical plants can be inaccurate. An alternative is to fit a Machine Learning (ML) model directly to plant sensor data. We use a structured approach: Each unit within the plant gets represented by one ML model. After fitting the models to the data, the models are connected into a f...	false	true	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	381,648

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