Papers Sparse Learning
“Sparse Learning” 태그가 달린 논문 209편 · 필터 해제
Sparser2Sparse: Single-shot Sparser-to-Sparse Learning for Spatial Transcriptomics Imputation with Natural Image Co-learning
Spatial transcriptomics (ST) has revolutionized biomedical research by enabling high resolution gene expression profiling within tissues. However, the high cost and scarcity of high resolution ST data remain significant …
Self-Supervised LearningSparse LearningLearning under Latent Group Sparsity via Diffusion on Networks
Group or cluster structure on explanatory variables in machine learning problems is a very general phenomenon, which has attracted broad interest from practitioners and theoreticians alike. In this work we contribute an …
Sparse LearningDirect Regret Optimization in Bayesian Optimization
Bayesian optimization (BO) is a powerful paradigm for optimizing expensive black-box functions. Traditional BO methods typically rely on separate hand-crafted acquisition functions and surrogate models for the underlying…
Gaussian ProcessesSparse LearningAugmented Physics-Based Li-ion Battery Model via Adaptive Ensemble Sparse Learning and Conformal Prediction
Accurate electrochemical models are essential for the safe and efficient operation of lithium-ion batteries in real-world applications such as electrified vehicles and grid storage. Reduced-order models (ROM) offer a bal…
Computational EfficiencySparse LearningScalable Subset Selection in Linear Mixed Models
Linear mixed models (LMMs), which incorporate fixed and random effects, are key tools for analyzing heterogeneous data, such as in personalized medicine or adaptive marketing. Nowadays, this type of data is increasingly …
MarketingSparse LearningLearning Sparsity for Effective and Efficient Music Performance Question Answering
Music performances, characterized by dense and continuous audio as well as seamless audio-visual integration, present unique challenges for multimodal scene understanding and reasoning. Recent Music Performance Audio-Vis…
Audio-visual Question AnsweringQuestion AnsweringScene UnderstandingSparse Learning+1Optimizing Hard Thresholding for Sparse Model Discovery
Many model selection algorithms rely on sparse dictionary learning to provide interpretable and physics-based governing equations. The optimization algorithms typically use a hard thresholding process to enforce sparse a…
Dictionary LearningmodelModel DiscoveryModel Selection+1STARS: Sparse Learning Correlation Filter with Spatio-temporal Regularization and Super-resolution Reconstruction for Thermal Infrared Target Tracking
Thermal infrared (TIR) target tracking methods often adopt the correlation filter (CF) framework due to its computational efficiency. However, the low resolution of TIR images, along with tracking interference, significa…
Computational EfficiencySparse LearningSuper-ResolutionSparsified-Learning for Heavy-Tailed Locally Stationary Processes
Sparsified Learning is ubiquitous in many machine learning tasks. It aims to regularize the objective function by adding a penalization term that considers the constraints made on the learned parameters. This paper consi…
Sparse LearningLow-Rank Matrix Regression via Least-Angle Regression
Low-rank matrix regression is a fundamental problem in data science with various applications in systems and control. Nuclear norm regularization has been widely applied to solve this problem due to its convexity. Howeve…
Computational EfficiencyregressionSparse LearningDeep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries
Sparse regularization techniques are well-established in machine learning, yet their application in neural networks remains challenging due to the non-differentiability of penalties like the $L_1$ norm, which is incompat…
Sparse LearningNonparametric Sparse Online Learning of the Koopman Operator
The Koopman operator provides a powerful framework for representing the dynamics of general nonlinear dynamical systems. Data-driven techniques to learn the Koopman operator typically assume that the chosen function spac…
Sparse LearningMyESL: Sparse learning in molecular evolution and phylogenetic analysis
Evolutionary sparse learning (ESL) uses a supervised machine learning approach, Least Absolute Shrinkage and Selection Operator (LASSO), to build models explaining the relationship between a hypothesis and the variation …
Sparse LearningPushing the Limits of Sparsity: A Bag of Tricks for Extreme Pruning
Pruning of deep neural networks has been an effective technique for reducing model size while preserving most of the performance of dense networks, crucial for deploying models on memory and power-constrained devices. Wh…
Sparse LearningGUIDE: Real-Time Human-Shaped Agents
The recent rapid advancement of machine learning has been driven by increasingly powerful models with the growing availability of training data and computational resources. However, real-time decision-making tasks with l…
Decision MakingSparse LearningScaling Continuous Kernels with Sparse Fourier Domain Learning
We address three key challenges in learning continuous kernel representations: computational efficiency, parameter efficiency, and spectral bias. Continuous kernels have shown significant potential, but their practical a…
Computational EfficiencySparse LearningOptimizing Rare Word Accuracy in Direct Speech Translation with a Retrieval-and-Demonstration Approach
Direct speech translation (ST) models often struggle with rare words. Incorrect translation of these words can have severe consequences, impacting translation quality and user trust. While rare word translation is inhere…
In-Context LearningRetrievalSparse LearningSpeech-to-Text+2Quasi-potential and drift decomposition in stochastic systems by sparse identification
The quasi-potential is a key concept in stochastic systems as it accounts for the long-term behavior of the dynamics of such systems. It also allows us to estimate mean exit times from the attractors of the system, and t…
Sparse LearningProbabilistic Iterative Hard Thresholding for Sparse Learning
For statistical modeling wherein the data regime is unfavorable in terms of dimensionality relative to the sample size, finding hidden sparsity in the ground truth can be critical in formulating an accurate statistical m…
Sparse LearningMulti-SIGATnet: A multimodal schizophrenia MRI classification algorithm using sparse interaction mechanisms and graph attention networks
Schizophrenia is a serious psychiatric disorder. Its pathogenesis is not completely clear, making it difficult to treat patients precisely. Because of the complicated non-Euclidean network structure of the human brain, l…
Graph AttentionMRI classificationSparse Learning