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
2212.10448	Parameter-efficient Zero-shot Transfer for Cross-Language Dense Retrieval with Adapters	A popular approach to creating a zero-shot cross-language retrieval model is to substitute a monolingual pretrained language model in the retrieval model with a multilingual pretrained language model such as Multilingual BERT. This multilingual model is fined-tuned to the retrieval task with monolingual data such as En...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	337,468
2103.12171	Adversarial Feature Augmentation and Normalization for Visual Recognition	Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings, instead of relying on computationally-...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	226,073
2201.05767	Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems	Large transformer models can highly improve Answer Sentence Selection (AS2) tasks, but their high computational costs prevent their use in many real-world applications. In this paper, we explore the following research question: How can we make the AS2 models more accurate without significantly increasing their model co...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	275,490
2101.07175	Deep Reinforcement Learning with Embedded LQR Controllers	Reinforcement learning is a model-free optimal control method that optimizes a control policy through direct interaction with the environment. For reaching tasks that end in regulation, popular discrete-action methods are not well suited due to chattering in the goal state. We compare three different ways to solve this...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	215,958
2301.08970	The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making	The Cauchy-Schwarz (CS) divergence was developed by Pr\'{i}ncipe et al. in 2000. In this paper, we extend the classic CS divergence to quantify the closeness between two conditional distributions and show that the developed conditional CS divergence can be simply estimated by a kernel density estimator from given sampl...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	341,358
2405.14426	A hybrid systems framework for data-based adaptive control of linear time-varying systems	We consider the data-driven stabilization of discrete-time linear time-varying systems. The controller is defined as a linear state-feedback law whose gain is adapted to the plant changes through a data-based event-triggering rule. To do so, we monitor the evolution of a data-based Lyapunov function along the solution....	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	456,419
1208.5024	Brain-Computer Interface Controlled Robotic Gait Orthosis	Reliance on wheelchairs after spinal cord injury (SCI) leads to many medical co-morbidities. Treatment of these conditions contributes to the majority of SCI health care costs. Restoring able-body-like ambulation after SCI may reduce the incidence of these conditions, and increase independence and quality of life. Howe...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	18,243
2009.07583	Video Compression with CNN-based Post Processing	In recent years, video compression techniques have been significantly challenged by the rapidly increased demands associated with high quality and immersive video content. Among various compression tools, post-processing can be applied on reconstructed video content to mitigate visible compression artefacts and to enha...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	195,985
2202.03007	Learning Sound Localization Better From Semantically Similar Samples	The objective of this work is to localize the sound sources in visual scenes. Existing audio-visual works employ contrastive learning by assigning corresponding audio-visual pairs from the same source as positives while randomly mismatched pairs as negatives. However, these negative pairs may contain semantically match...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	279,037
2203.01671	Constrained unsupervised anomaly segmentation	Current unsupervised anomaly localization approaches rely on generative models to learn the distribution of normal images, which is later used to identify potential anomalous regions derived from errors on the reconstructed images. However, a main limitation of nearly all prior literature is the need of employing anoma...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	283,475
1703.08046	Message-Passing Methods for Complex Contagions	Message-passing methods provide a powerful approach for calculating the expected size of cascades either on random networks (e.g., drawn from a configuration-model ensemble or its generalizations) asymptotically as the number $N$ of nodes becomes infinite or on specific finite-size networks. We review the message-passi...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	70,511
1710.07547	Learning compressed representations of blood samples time series with missing data	Clinical measurements collected over time are naturally represented as multivariate time series (MTS), which often contain missing data. An autoencoder can learn low dimensional vectorial representations of MTS that preserve important data characteristics, but cannot deal explicitly with missing data. In this work, we ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	82,951
1210.4872	Nested Dictionary Learning for Hierarchical Organization of Imagery and Text	A tree-based dictionary learning model is developed for joint analysis of imagery and associated text. The dictionary learning may be applied directly to the imagery from patches, or to general feature vectors extracted from patches or superpixels (using any existing method for image feature extraction). Each image is ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	19,198
2409.08595	Automatic Generation of Fast and Accurate Performance Models for Deep Neural Network Accelerators	Implementing Deep Neural Networks (DNNs) on resource-constrained edge devices is a challenging task that requires tailored hardware accelerator architectures and a clear understanding of their performance characteristics when executing the intended AI workload. To facilitate this, we present an automated generation app...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	487,976
2501.08303	Advancing Semantic Future Prediction through Multimodal Visual Sequence Transformers	Semantic future prediction is important for autonomous systems navigating dynamic environments. This paper introduces FUTURIST, a method for multimodal future semantic prediction that uses a unified and efficient visual sequence transformer architecture. Our approach incorporates a multimodal masked visual modeling obj...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	524,708
2310.00504	Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology	Medical imaging plays a critical role in the diagnosis and treatment planning of various medical conditions, with radiology and pathology heavily reliant on precise image segmentation. The Segment Anything Model (SAM) has emerged as a promising framework for addressing segmentation challenges across different domains. ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	396,008
2411.11217	MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs	Efficient deployment of large language models, particularly Mixture of Experts (MoE), on resource-constrained platforms presents significant challenges, especially in terms of computational efficiency and memory utilization. The MoE architecture, renowned for its ability to increase model capacity without a proportiona...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	508,961
1506.03540	DisCoF$^+$: Asynchronous DisCoF with Flexible Decoupling for Cooperative Pathfinding in Distributed Systems	In our prior work, we outlined an approach, named DisCoF, for cooperative pathfinding in distributed systems with limited sensing and communication range. Contrasting to prior works on cooperative pathfinding with completeness guarantees, which often assume the access to global information, DisCoF does not make this as...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	44,067
1312.3269	Power Scheduling of Kalman Filtering in Wireless Sensor Networks with Data Packet Drops	For a wireless sensor network (WSN) with a large number of low-cost, battery-driven, multiple transmission power leveled sensor nodes of limited transmission bandwidth, then conservation of transmission resources (power and bandwidth) is of paramount importance. Towards this end, this paper considers the problem of pow...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	29,030
2002.04392	How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalise to rare congenital heart diseases for surgical planning?	Planning the optimal time of intervention for pulmonary valve replacement surgery in patients with the congenital heart disease Tetralogy of Fallot (TOF) is mainly based on ventricular volume and function according to current guidelines. Both of these two biomarkers are most reliably assessed by segmentation of 3D card...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	163,597
2401.09678	Integrating Graceful Degradation and Recovery through Requirement-driven Adaptation	Cyber-physical systems (CPS) are subject to environmental uncertainties such as adverse operating conditions, malicious attacks, and hardware degradation. These uncertainties may lead to failures that put the system in a sub-optimal or unsafe state. Systems that are resilient to such uncertainties rely on two types of ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	422,342
2310.09594	QuITO: Numerical software for constrained nonlinear optimal control problems -- extended version	We introduce the MATLAB-based software QuITO (Quasi-Interpolation based Trajectory Optimization) to numerically solve a wide class of constrained nonlinear optimal control problems (OCP). The solver is based on the QuITO (the same abbreviation) algorithm, which is a direct multiple shooting (DMS) technique that leverag...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	399,840
2304.00639	PowerModelsADA: A Framework for Solving Optimal Power Flow using Distributed Algorithms	This paper presents PowerModelsADA, an open-source framework for solving Optimal Power Flow (OPF) problems using Alternating Distributed Algorithms (ADA). PowerModelsADA provides a framework to test, verify, and benchmark both existing and new ADAs. This paper demonstrates use cases for PowerModelsADA and validates its...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	355,760
2201.13372	Robust supervised learning with coordinate gradient descent	This paper considers the problem of supervised learning with linear methods when both features and labels can be corrupted, either in the form of heavy tailed data and/or corrupted rows. We introduce a combination of coordinate gradient descent as a learning algorithm together with robust estimators of the partial deri...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	277,961
2112.02478	Classification of COVID-19 on chest X-Ray images using Deep Learning model with Histogram Equalization and Lungs Segmentation	Background and Objective: Artificial intelligence (AI) methods coupled with biomedical analysis has a critical role during pandemics as it helps to release the overwhelming pressure from healthcare systems and physicians. As the ongoing COVID-19 crisis worsens in countries having dense populations and inadequate testin...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	269,856
1701.02444	Energy Harvesting Communication Using Finite-Capacity Batteries with Internal Resistance	Modern systems will increasingly rely on energy harvested from their environment. Such systems utilize batteries to smoothen out the random fluctuations in harvested energy. These fluctuations induce highly variable battery charge and discharge rates, which affect the efficiencies of practical batteries that typically ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	66,553
2310.16224	Poison is Not Traceless: Fully-Agnostic Detection of Poisoning Attacks	The performance of machine learning models depends on the quality of the underlying data. Malicious actors can attack the model by poisoning the training data. Current detectors are tied to either specific data types, models, or attacks, and therefore have limited applicability in real-world scenarios. This paper prese...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	402,628
2003.11246	FastDTW is approximate and Generally Slower than the Algorithm it Approximates	Many time series data mining problems can be solved with repeated use of distance measure. Examples of such tasks include similarity search, clustering, classification, anomaly detection and segmentation. For over two decades it has been known that the Dynamic Time Warping (DTW) distance measure is the best measure to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	169,559
2410.10329	GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphs	Recently, research on Text-Attributed Graphs (TAGs) has gained significant attention due to the prevalence of free-text node features in real-world applications and the advancements in Large Language Models (LLMs) that bolster TAG methodologies. However, current TAG approaches face two primary challenges: (i) Heavy rel...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	498,021
1705.07038	The Landscape of Deep Learning Algorithms	This paper studies the landscape of empirical risk of deep neural networks by theoretically analyzing its convergence behavior to the population risk as well as its stationary points and properties. For an $l$-layer linear neural network, we prove its empirical risk uniformly converges to its population risk at the rat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	73,728
2202.06198	Data standardization for robust lip sync	Lip sync is a fundamental audio-visual task. However, existing lip sync methods fall short of being robust in the wild. One important cause could be distracting factors on the visual input side, making extracting lip motion information difficult. To address these issues, this paper proposes a data standardization pipel...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	280,130
2203.16578	Code Switched and Code Mixed Speech Recognition for Indic languages	Training multilingual automatic speech recognition (ASR) systems is challenging because acoustic and lexical information is typically language specific. Training multilingual system for Indic languages is even more tougher due to lack of open source datasets and results on different approaches. We compare the performan...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	288,835
2306.14351	Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions	The aim of this paper is to make clear and precise the relationship between the Rubin causal model (RCM) and structural causal model (SCM) frameworks for causal inference. Adopting a neutral logical perspective, and drawing on previous work, we show what is required for an RCM to be representable by an SCM. A key resul...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	375,651
2109.09505	Unsupervised domain adaptation with non-stochastic missing data	We consider unsupervised domain adaptation (UDA) for classification problems in the presence of missing data in the unlabelled target domain. More precisely, motivated by practical applications, we analyze situations where distribution shift exists between domains and where some components are systematically absent on ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	256,306
1302.3570	Quasi-Bayesian Strategies for Efficient Plan Generation: Application to the Planning to Observe Problem	Quasi-Bayesian theory uses convex sets of probability distributions and expected loss to represent preferences about plans. The theory focuses on decision robustness, i.e., the extent to which plans are affected by deviations in subjective assessments of probability. The present work presents solutions for plan generat...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	22,036
1809.01202	Causal Explanation Analysis on Social Media	Understanding causal explanations - reasons given for happenings in one's life - has been found to be an important psychological factor linked to physical and mental health. Causal explanations are often studied through manual identification of phrases over limited samples of personal writing. Automatic identification ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	106,747
2106.05145	Relative Clustering Coefficient	In this paper, we relatively extend the definition of global clustering coefficient to another clustering, which we call it relative clustering coefficient. The idea of this definition is to ignore the edges in the network that the probability of having an edge is 0. Here, we also consider a model as an example that us...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	239,990
2307.15829	Seeing Behind Dynamic Occlusions with Event Cameras	Unwanted camera occlusions, such as debris, dust, rain-drops, and snow, can severely degrade the performance of computer-vision systems. Dynamic occlusions are particularly challenging because of the continuously changing pattern. Existing occlusion-removal methods currently use synthetic aperture imaging or image inpa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	382,378
2409.15375	DS2TA: Denoising Spiking Transformer with Attenuated Spatiotemporal Attention	Vision Transformers (ViT) are current high-performance models of choice for various vision applications. Recent developments have given rise to biologically inspired spiking transformers that thrive in ultra-low power operations on neuromorphic hardware, however, without fully unlocking the potential of spiking neural ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	true	false	false	490,899
2310.07736	Observatory: Characterizing Embeddings of Relational Tables	Language models and specialized table embedding models have recently demonstrated strong performance on many tasks over tabular data. Researchers and practitioners are keen to leverage these models in many new application contexts; but limited understanding of the strengths and weaknesses of these models, and the table...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	399,104
1208.1740	On the Relation between Centrality Measures and Consensus Algorithms	This paper introduces some tools from graph theory and distributed consensus algorithms to construct an optimal, yet robust, hierarchical information sharing structure for large-scale decision making and control problems. The proposed method is motivated by the robustness and optimality of leaf-venation patterns. We in...	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	17,985
cs/9902018	ZBroker: A Query Routing Broker for Z39.50 Databases	A query routing broker is a software agent that determines from a large set of accessing information sources the ones most relevant to a user's information need. As the number of information sources on the Internet increases dramatically, future users will have to rely on query routing brokers to decide a small number ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	540,474
2202.08418	Neural Marionette: Unsupervised Learning of Motion Skeleton and Latent Dynamics from Volumetric Video	We present Neural Marionette, an unsupervised approach that discovers the skeletal structure from a dynamic sequence and learns to generate diverse motions that are consistent with the observed motion dynamics. Given a video stream of point cloud observation of an articulated body under arbitrary motion, our approach d...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	280,866
2305.09993	Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling	We introduce Reprompting, an iterative sampling algorithm that automatically learns the Chain-of-Thought (CoT) recipes for a given task without human intervention. Through Gibbs sampling, Reprompting infers the CoT recipes that work consistently well for a set of training samples by iteratively sampling new recipes usi...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	364,860
1912.01046	TutorialVQA: Question Answering Dataset for Tutorial Videos	Despite the number of currently available datasets on video question answering, there still remains a need for a dataset involving multi-step and non-factoid answers. Moreover, relying on video transcripts remains an under-explored topic. To adequately address this, We propose a new question answering task on instructi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	155,958
2103.17207	State-Dependent Processing in Payment Channel Networks for Throughput Optimization	Payment channel networks (PCNs) have emerged as a scalability solution for blockchains built on the concept of a payment channel: a setting that allows two nodes to safely transact between themselves in high frequencies based on pre-committed peer-to-peer balances. Transaction requests in these networks may be declined...	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	true	227,818
cs/9904021	Hadamard product nonlinear formulation of Galerkin and finite element methods	A novel nonlinear formulation of the finite element and Galerkin methods is presented here, which leads to the Hadamard product expression of the resultant nonlinear algebraic analogue. The presented formulation attains the advantages of weak formulation in the standard finite element and Galerkin schemes and avoids th...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	540,499
2410.20219	Pseudo-Label Enhanced Prototypical Contrastive Learning for Uniformed Intent Discovery	New intent discovery is a crucial capability for task-oriented dialogue systems. Existing methods focus on transferring in-domain (IND) prior knowledge to out-of-domain (OOD) data through pre-training and clustering stages. They either handle the two processes in a pipeline manner, which exhibits a gap between intent r...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	502,704
2305.18221	GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification	Eye tracking research is important in computer vision because it can help us understand how humans interact with the visual world. Specifically for high-risk applications, such as in medical imaging, eye tracking can help us to comprehend how radiologists and other medical professionals search, analyze, and interpret i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	368,903
2302.00586	PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets	The financial markets, which involve more than $90 trillion market capitals, attract the attention of innumerable investors around the world. Recently, reinforcement learning in financial markets (FinRL) has emerged as a promising direction to train agents for making profitable investment decisions. However, the evalua...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	343,264
2302.05765	Adversarial Online Collaborative Filtering	We investigate the problem of online collaborative filtering under no-repetition constraints, whereby users need to be served content in an online fashion and a given user cannot be recommended the same content item more than once. We start by designing and analyzing an algorithm that works under biclustering assumptio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	345,161
1505.01539	Graphical Potential Games	Potential games, originally introduced in the early 1990's by Lloyd Shapley, the 2012 Nobel Laureate in Economics, and his colleague Dov Monderer, are a very important class of models in game theory. They have special properties such as the existence of Nash equilibria in pure strategies. This note introduces graphical...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	42,850
1606.01167	How Deep is the Feature Analysis underlying Rapid Visual Categorization?	Rapid categorization paradigms have a long history in experimental psychology: Characterized by short presentation times and speedy behavioral responses, these tasks highlight the efficiency with which our visual system processes natural object categories. Previous studies have shown that feed-forward hierarchical mode...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	56,764
2002.03766	Testing Unsatisfiability of Constraint Satisfaction Problems via Tensor Products	We study the design of stochastic local search methods to prove unsatisfiability of a constraint satisfaction problem (CSP). For a binary CSP, such methods have been designed using the microstructure of the CSP. Here, we develop a method to decompose the microstructure into graph tensors. We show how to use the tensor ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	163,396
2405.04042	Space-time Reinforcement Network for Video Object Segmentation	Recently, video object segmentation (VOS) networks typically use memory-based methods: for each query frame, the mask is predicted by space-time matching to memory frames. Despite these methods having superior performance, they suffer from two issues: 1) Challenging data can destroy the space-time coherence between adj...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	452,420
2310.00508	Analytical Modeling of Parameter Imbalance in Permanent Magnet Synchronous Machines	This paper presents a systematic and comprehensive analysis of the impact of parameter imbalance in permanent magnet synchronous machines. Analytical models that reveal the effects of imbalance are obtained for each parameter. Thereafter, the models are verified for accuracy by comparison with complex simulations that ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	396,010
1911.06716	A Generalized Markov Chain Model to Capture Dynamic Preferences and Choice Overload	Assortment optimization is an important problem that arises in many industries such as retailing and online advertising where the goal is to find a subset of products from a universe of substitutable products which maximize seller's expected revenue. One of the key challenges in this problem is to model the customer su...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	153,609
2304.01349	Optimized EEG based mood detection with signal processing and deep neural networks for brain-computer interface	Electroencephalogram (EEG) is a very promising and widely implemented procedure to study brain signals and activities by amplifying and measuring the post-synaptical potential arising from electrical impulses produced by neurons and detected by specialized electrodes attached to specific points in the scalp. It can be ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	356,035
2312.06221	CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels	Learning with noisy labels (LNL) poses a significant challenge in training a well-generalized model while avoiding overfitting to corrupted labels. Recent advances have achieved impressive performance by identifying clean labels and correcting corrupted labels for training. However, the current approaches rely heavily ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	414,429
1303.1858	On the Minimum Distance of Generalized Spatially Coupled LDPC Codes	Families of generalized spatially-coupled low-density parity-check (GSC-LDPC) code ensembles can be formed by terminating protograph-based generalized LDPC convolutional (GLDPCC) codes. It has previously been shown that ensembles of GSC-LDPC codes constructed from a protograph have better iterative decoding thresholds ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	22,766
2007.00153	Conditional Gradient Methods for Convex Optimization with General Affine and Nonlinear Constraints	Conditional gradient methods have attracted much attention in both machine learning and optimization communities recently. These simple methods can guarantee the generation of sparse solutions. In addition, without the computation of full gradients, they can handle huge-scale problems sometimes even with an exponential...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	185,024
2305.16597	Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models	Parameter-efficient tuning (PET) methods fit pre-trained language models (PLMs) to downstream tasks by either computing a small compressed update for a subset of model parameters, or appending and fine-tuning a small number of new model parameters to the pre-trained network. Hand-designed PET architectures from the lit...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	368,167
1711.04498	Targeted Advertising Based on Browsing History	Audience interest, demography, purchase behavior and other possible classifications are ex- tremely important factors to be carefully studied in a targeting campaign. This information can help advertisers and publishers deliver advertisements to the right audience group. How- ever, it is not easy to collect such inform...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	84,403
2304.06906	Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding	The use of pretrained backbones with fine-tuning has been successful for 2D vision and natural language processing tasks, showing advantages over task-specific networks. In this work, we introduce a pretrained 3D backbone, called {\SST}, for 3D indoor scene understanding. We design a 3D Swin transformer as our backbone...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	358,149
2303.04627	Fairness-driven Skilled Task Assignment with Extra Budget in Spatial Crowdsourcing	With the prevalence of mobile devices and ubiquitous wireless networks, spatial crowdsourcing has attracted much attention from both academic and industry communities. On spatial crowdsourcing platforms, task requesters can publish spatial tasks and workers need to move to destinations to perform them. In this paper, w...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	350,161
2306.08258	Transmission and Distribution Coordination for DER-rich Energy Markets: A Parametric Programming Approach	In this paper, a framework is proposed to coordinate the operation of the independent system operator (ISO) and distribution system operator (DSO). The framework is compatible with current practice of the U.S. wholesale market to enable massive distributed energy resources (DERs) to participate in the wholesale market....	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	373,345
2405.11273	Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts	Recent advancements in Multimodal Large Language Models (MLLMs) underscore the significance of scalable models and data to boost performance, yet this often incurs substantial computational costs. Although the Mixture of Experts (MoE) architecture has been employed to efficiently scale large language and image-text mod...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	true	455,070
1905.11675	Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems	First-order methods such as stochastic gradient descent (SGD) are currently the standard algorithm for training deep neural networks. Second-order methods, despite their better convergence rate, are rarely used in practice due to the prohibitive computational cost in calculating the second-order information. In this pa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	132,508
2405.00911	Stabilization of infinite-dimensional systems under quantization and packet loss	We study the problem of stabilizing infinite-dimensional systems with input and output quantization. The closed-loop system we consider is subject to packet loss in the sensor-to-controller channels, whose duration is assumed to be averagely bounded. Given a bound on the initial state, we propose design methods for dyn...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	451,127
2309.01963	Generalized Simple Regenerating Codes: Trading Sub-packetization and Fault Tolerance	Maximum distance separable (MDS) codes have the optimal trade-off between storage efficiency and fault tolerance, which are widely used in distributed storage systems. As typical non-MDS codes, simple regenerating codes (SRCs) can achieve both smaller repair bandwidth and smaller repair locality than traditional MDS co...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	389,872
2008.10599	The Hessian Penalty: A Weak Prior for Unsupervised Disentanglement	Existing disentanglement methods for deep generative models rely on hand-picked priors and complex encoder-based architectures. In this paper, we propose the Hessian Penalty, a simple regularization term that encourages the Hessian of a generative model with respect to its input to be diagonal. We introduce a model-agn...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	true	193,050
2412.03258	Learning on One Mode: Addressing Multi-Modality in Offline Reinforcement Learning	Offline reinforcement learning (RL) seeks to learn optimal policies from static datasets without interacting with the environment. A common challenge is handling multi-modal action distributions, where multiple behaviours are represented in the data. Existing methods often assume unimodal behaviour policies, leading to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	513,888
2305.15746	Assessing the Spatial Structure of the Association between Attendance at Preschool and Childrens Developmental Vulnerabilities in Queensland Australia	The research explores the influence of preschool attendance (one year before full-time school) on the development of children during their first year of school. Using data collected by the Australian Early Development Census, the findings show that areas with high proportions of preschool attendance tended to have lowe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	367,758
2005.04586	Ensemble Wrapper Subsampling for Deep Modulation Classification	Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms that rely on the output samples. We propose a subsampling technique to facilitate the use of deep learning for automatic modulation classification in wireless commun...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	176,507
1309.4844	Network Anomaly Detection: A Survey and Comparative Analysis of Stochastic and Deterministic Methods	We present five methods to the problem of network anomaly detection. These methods cover most of the common techniques in the anomaly detection field, including Statistical Hypothesis Tests (SHT), Support Vector Machines (SVM) and clustering analysis. We evaluate all methods in a simulated network that consists of nomi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	27,122
2502.03147	Scalable In-Context Learning on Tabular Data via Retrieval-Augmented Large Language Models	Recent studies have shown that large language models (LLMs), when customized with post-training on tabular data, can acquire general tabular in-context learning (TabICL) capabilities. These models are able to transfer effectively across diverse data schemas and different task domains. However, existing LLM-based TabICL...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	530,615
1609.00864	Identifiability of linear dynamic networks	Dynamic networks are structured interconnections of dynamical systems (modules) driven by external excitation and disturbance signals. In order to identify their dynamical properties and/or their topology consistently from measured data, we need to make sure that the network model set is identifiable. We introduce the ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	60,530
2405.08334	Could Chemical LLMs benefit from Message Passing	Pretrained language models (LMs) showcase significant capabilities in processing molecular text, while concurrently, message passing neural networks (MPNNs) demonstrate resilience and versatility in the domain of molecular science. Despite these advancements, we find there are limited studies investigating the bidirect...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	454,064
1910.10352	A Transformer with Interleaved Self-attention and Convolution for Hybrid Acoustic Models	Transformer with self-attention has achieved great success in the area of nature language processing. Recently, there have been a few studies on transformer for end-to-end speech recognition, while its application for hybrid acoustic model is still very limited. In this paper, we revisit the transformer-based hybrid ac...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	150,468
2109.07843	Label Assignment Distillation for Object Detection	This article has been removed by arXiv administrators due to a claim of copyright infringement	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	255,672
1910.10006	Image recovery from rotational and translational invariants	We introduce a framework for recovering an image from its rotationally and translationally invariant features based on autocorrelation analysis. This work is an instance of the multi-target detection statistical model, which is mainly used to study the mathematical and computational properties of single-particle recons...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	150,362
2103.00432	Learning-Based Phase Compression and Quantization for Massive MIMO CSI Feedback with Magnitude-Aided Information	Massive MIMO wireless FDD systems are often confronted by the challenge to efficiently obtain downlink channel state information (CSI). Previous works have demonstrated the potential in CSI encoding and recovery by take advantage of uplink/downlink reciprocity between their CSI magnitudes. However, such a framework sep...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	222,274
2412.15342	DCRA-Net: Attention-Enabled Reconstruction Model for Dynamic Fetal Cardiac MRI	Dynamic fetal heart magnetic resonance imaging (MRI) presents unique challenges due to the fast heart rate of the fetus compared to adult subjects and uncontrolled fetal motion. This requires high temporal and spatial resolutions over a large field of view, in order to encompass surrounding maternal anatomy. In this wo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	519,071
2308.09033	Uni-NLX: Unifying Textual Explanations for Vision and Vision-Language Tasks	Natural Language Explanations (NLE) aim at supplementing the prediction of a model with human-friendly natural text. Existing NLE approaches involve training separate models for each downstream task. In this work, we propose Uni-NLX, a unified framework that consolidates all NLE tasks into a single and compact multi-ta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,125
2502.09389	S$^2$-Diffusion: Generalizing from Instance-level to Category-level Skills in Robot Manipulation	Recent advances in skill learning has propelled robot manipulation to new heights by enabling it to learn complex manipulation tasks from a practical number of demonstrations. However, these skills are often limited to the particular action, object, and environment \textit{instances} that are shown in the training data...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	533,434
1908.11036	DWnet: Deep-Wide Network for 3D Action Recognition	We propose in this paper a deep-wide network (DWnet) which combines the deep structure with the broad learning system (BLS) to recognize actions. Compared with the deep structure, the novel model saves lots of testing time and almost achieves real-time testing. Furthermore, the DWnet can capture better features than br...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	143,274
2101.11959	Syntactic Nuclei in Dependency Parsing -- A Multilingual Exploration	Standard models for syntactic dependency parsing take words to be the elementary units that enter into dependency relations. In this paper, we investigate whether there are any benefits from enriching these models with the more abstract notion of nucleus proposed by Tesni\`{e}re. We do this by showing how the concept o...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	217,443
2409.02503	eRSS-RAMP: A Rule-Adherence Motion Planner Based on Extended Responsibility-Sensitive Safety for Autonomous Driving	Driving safety and responsibility determination are indispensable pieces of the puzzle for autonomous driving. They are also deeply related to the allocation of right-of-way and the determination of accident liability. Therefore, Intel/Mobileye designed the responsibility-sensitive safety (RSS) framework to further enh...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	485,731
2408.05350	Enabling Quick, Accurate Crowdsourced Annotation for Elevation-Aware Flood Extent Mapping	In order to assess damage and properly allocate relief efforts, mapping the extent of flood events is a necessary and important aspect of disaster management. In recent years, deep learning methods have evolved as an effective tool to quickly label high-resolution imagery and provide necessary flood extent mappings. Th...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	479,742
2403.14454	Prediction of Translation Techniques for the Translation Process	Machine translation (MT) encompasses a variety of methodologies aimed at enhancing the accuracy of translations. In contrast, the process of human-generated translation relies on a wide range of translation techniques, which are crucial for ensuring linguistic adequacy and fluency. This study suggests that these transl...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	440,087
2402.11925	Energy-Efficient Edge Learning via Joint Data Deepening-and-Prefetching	The vision of pervasive artificial intelligence (AI) services can be realized by training an AI model on time using real-time data collected by internet of things (IoT) devices. To this end, IoT devices require offloading their data to an edge server in proximity. However, transmitting high-dimensional and voluminous d...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	430,645
2012.03454	Stronger Calibration Lower Bounds via Sidestepping	We consider an online binary prediction setting where a forecaster observes a sequence of $T$ bits one by one. Before each bit is revealed, the forecaster predicts the probability that the bit is $1$. The forecaster is called well-calibrated if for each $p \in [0, 1]$, among the $n_p$ bits for which the forecaster pred...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	210,121
2201.03002	MaskMTL: Attribute prediction in masked facial images with deep multitask learning	Predicting attributes in the landmark free facial images is itself a challenging task which gets further complicated when the face gets occluded due to the usage of masks. Smart access control gates which utilize identity verification or the secure login to personal electronic gadgets may utilize face as a biometric tr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	274,722
1211.2361	Genetic Algorithm for Designing a Convenient Facility Layout for a Circular Flow Path	In this paper, we present a heuristic for designing facility layouts that are convenient for designing a unidirectional loop for material handling. We use genetic algorithm where the objective function and crossover and mutation operators have all been designed specifically for this purpose. Our design is made under fl...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	19,674
2403.17711	Using quantum computers in control: interval matrix properties	Quantum computing provides a powerful framework for tackling computational problems that are classically intractable. The goal of this paper is to explore the use of quantum computers for solving relevant problems in systems and control theory. In the recent literature, different quantum algorithms have been developed ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	441,588
1301.3845	Separation Properties of Sets of Probability Measures	This paper analyzes independence concepts for sets of probability measures associated with directed acyclic graphs. The paper shows that epistemic independence and the standard Markov condition violate desirable separation properties. The adoption of a contraction condition leads to d-separation but still fails to guar...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	21,157
2303.06360	FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learning	Federated learning (FL) has prevailed as an efficient and privacy-preserved scheme for distributed learning. In this work, we mainly focus on the optimization of computation and communication in FL from a view of pruning. By adopting layer-wise pruning in local training and federated updating, we formulate an explicit ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	true	350,816
1910.12706	Interrupted and cascaded permutation invariant training for speech separation	Permutation Invariant Training (PIT) has long been a stepping stone method for training speech separation model in handling the label ambiguity problem. With PIT selecting the minimum cost label assignments dynamically, very few studies considered the separation problem to be optimizing both the model parameters and th...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	151,170
1503.01446	Predicting opponent team activity in a RoboCup environment	The goal of this project is to predict the opponent's configuration in a RoboCup SSL environment. For simplicity, a Markov model assumption is made such that the predicted formation of the opponent team only depends on its current formation. The field is divided into a grid and a robot state per player is created with ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	40,832
cs/0702143	Attribute Value Reordering For Efficient Hybrid OLAP	The normalization of a data cube is the ordering of the attribute values. For large multidimensional arrays where dense and sparse chunks are stored differently, proper normalization can lead to improved storage efficiency. We show that it is NP-hard to compute an optimal normalization even for 1x3 chunks, although we ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	540,190

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