Papers Sparse Learning
“Sparse Learning” 태그가 달린 논문 209편 · 필터 해제
Infinite-Dimensional Sparse Learning in Linear System Identification
Regularized methods have been widely applied to system identification problems without known model structures. This paper proposes an infinite-dimensional sparse learning algorithm based on atomic norm regularization. At…
Sparse LearningERNIE-SPARSE: Learning Hierarchical Efficient Transformer Through Regularized Self-Attention
Sparse Transformer has recently attracted a lot of attention since the ability for reducing the quadratic dependency on the sequence length. We argue that two factors, information bottleneck sensitivity and inconsistency…
Sparse Learningtext-classificationText ClassificationL0Learn: A Scalable Package for Sparse Learning using L0 Regularization
We present L0Learn: an open-source package for sparse linear regression and classification using $\ell_0$ regularization. L0Learn implements scalable, approximate algorithms, based on coordinate descent and local combina…
Combinatorial OptimizationregressionSparse LearningAnalysis of Generalized Bregman Surrogate Algorithms for Nonsmooth Nonconvex Statistical Learning
Modern statistical applications often involve minimizing an objective function that may be nonsmooth and/or nonconvex. This paper focuses on a broad Bregman-surrogate algorithm framework including the local linear approx…
Sparse LearningERNIE-SPARSE: Learning Hierarchical Efficient Transformer Through Regularized Self-Attention
Sparse Transformer has recently attracted a lot of attention since the ability for reducing the quadratic dependency on the sequence length. We argue that two factors, information bottleneck sensitivity and inconsistency…
Sparse Learningtext-classificationText ClassificationSL-CycleGAN: Blind Motion Deblurring in Cycles using Sparse Learning
In this paper, we introduce an end-to-end generative adversarial network (GAN) based on sparse learning for single image blind motion deblurring, which we called SL-CycleGAN. For the first time in blind motion deblurring…
DeblurringGenerative Adversarial NetworkImage DeblurringSparse Learning+1Learning where to learn: Gradient sparsity in meta and continual learning
Finding neural network weights that generalize well from small datasets is difficult. A promising approach is to learn a weight initialization such that a small number of weight changes results in low generalization erro…
Continual LearningInductive BiasMeta-LearningSparse Learningabess: A Fast Best Subset Selection Library in Python and R
We introduce a new library named abess that implements a unified framework of best-subset selection for solving diverse machine learning problems, e.g., linear regression, classification, and principal component analysis…
Model SelectionSparse LearningVariable SelectionMulti-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation
Accurate automatic liver and tumor segmentation plays a vital role in treatment planning and disease monitoring. Recently, deep convolutional neural network (DCNNs) has obtained tremendous success in 2D and 3D medical im…
Automatic Liver And Tumor SegmentationImage SegmentationMedical Image SegmentationSegmentation+3High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data
As one of the most fundamental problems in machine learning, statistics and differential privacy, Differentially Private Stochastic Convex Optimization (DP-SCO) has been extensively studied in recent years. However, most…
Sparse LearningStochastic OptimizationVocal Bursts Intensity PredictionTowards Low-Latency Energy-Efficient Deep SNNs via Attention-Guided Compression
Deep spiking neural networks (SNNs) have emerged as a potential alternative to traditional deep learning frameworks, due to their promise to provide increased compute efficiency on event-driven neuromorphic hardware. How…
Sparse LearningImproving Sequential Recommendation Consistency with Self-Supervised Imitation
Most sequential recommendation models capture the features of consecutive items in a user-item interaction history. Though effective, their representation expressiveness is still hindered by the sparse learning signals. …
Imitation LearningSequential RecommendationSparse LearningSparse Training via Boosting Pruning Plasticity with Neuroregeneration
Works on lottery ticket hypothesis (LTH) and single-shot network pruning (SNIP) have raised a lot of attention currently on post-training pruning (iterative magnitude pruning), and before-training pruning (pruning at ini…
Network PruningSparse LearningSurrogate assisted active subspace and active subspace assisted surrogate -- A new paradigm for high dimensional structural reliability analysis
Performing reliability analysis on complex systems is often computationally expensive. In particular, when dealing with systems having high input dimensionality, reliability estimation becomes a daunting task. A popular …
Sparse LearningGrouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectives
We present a new algorithmic framework for grouped variable selection that is based on discrete mathematical optimization. While there exist several appealing approaches based on convex relaxations and nonconvex heuristi…
Sparse LearningVariable SelectionLearning where to learn
Finding neural network weights that generalize well from small datasets is difficult. A promising approach is to (meta-)learn a weight initialization from a collection of tasks, such that a small number of weight ch…
Meta-LearningSparse LearningDo We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training
In this paper, we introduce a new perspective on training deep neural networks capable of state-of-the-art performance without the need for the expensive over-parameterization by proposing the concept of In-Time Over-Par…
Image ClassificationSparse LearningBayesian data-driven discovery of partial differential equations with variable coefficients
The discovery of Partial Differential Equations (PDEs) is an essential task for applied science and engineering. However, data-driven discovery of PDEs is generally challenging, primarily stemming from the sensitivity of…
Bayesian InferenceModel SelectionSparse LearningUncertainty Quantification+1Approximate Post-Selective Inference for Regression with the Group LASSO
After selection with the Group LASSO (or generalized variants such as the overlapping, sparse, or standardized Group LASSO), inference for the selected parameters is unreliable in the absence of adjustments for selection…
regressionSelection biasSparse LearningFederated Nonconvex Sparse Learning
Nonconvex sparse learning plays an essential role in many areas, such as signal processing and deep network compression. Iterative hard thresholding (IHT) methods are the state-of-the-art for nonconvex sparse learning du…
Edge-computingSparse Learning