Papers Sparse Learning
“Sparse Learning” 태그가 달린 논문 209편 · 필터 해제
ssProp: Energy-Efficient Training for Convolutional Neural Networks with Scheduled Sparse Back Propagation
Recently, deep learning has made remarkable strides, especially with generative modeling, such as large language models and probabilistic diffusion models. However, training these models often involves significant comput…
Deep Learningimage-classificationImage ClassificationSparse LearningDRFormer: Multi-Scale Transformer Utilizing Diverse Receptive Fields for Long Time-Series Forecasting
Long-term time series forecasting (LTSF) has been widely applied in finance, traffic prediction, and other domains. Recently, patch-based transformers have emerged as a promising approach, segmenting data into sub-level …
PositionSparse LearningTime SeriesTime Series Forecasting+1Generative Adversarial Networks for Imputing Sparse Learning Performance
Learning performance data, such as correct or incorrect responses to questions in Intelligent Tutoring Systems (ITSs) is crucial for tracking and assessing the learners' progress and mastery of knowledge. However, the is…
Generative Adversarial NetworkImputationSparse LearningSLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining
Large language models (LLMs) have shown impressive capabilities across various tasks. However, training LLMs from scratch requires significant computational power and extensive memory capacity. Recent studies have explor…
QuantizationSparse LearningTowards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning
The emergence of deep and large-scale spiking neural networks (SNNs) exhibiting high performance across diverse complex datasets has led to a need for compressing network models due to the presence of a significant numbe…
Model CompressionNetwork PruningSparse 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…
Operator learningSparse LearningCLASSP: a Biologically-Inspired Approach to Continual Learning through Adjustment Suppression and Sparsity Promotion
This paper introduces a new biologically-inspired training method named Continual Learning through Adjustment Suppression and Sparsity Promotion (CLASSP). CLASSP is based on two main principles observed in neuroscience, …
Continual LearningSentiment AnalysisSparse LearningCRB Analysis for Mixed-ADC Based DOA Estimation
We consider a mixed analog-to-digital converter (ADC) based architecture consisting of high-precision and one-bit ADCs with the antenna-varying threshold for direction of arrival (DOA) estimation using a uniform linear a…
QuantizationSparse LearningBlock Sparse Bayesian Learning: A Diversified Scheme
This paper introduces a novel prior called Diversified Block Sparse Prior to characterize the widespread block sparsity phenomenon in real-world data. By allowing diversification on intra-block variance and inter-block c…
SensitivitySparse LearningDynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation
In the realm of deep learning-based recommendation systems, the increasing computational demands, driven by the growing number of users and items, pose a significant challenge to practical deployment. This challenge is p…
Model CompressionRecommendation SystemsSparse LearningDynamic Incremental Optimization for Best Subset Selection
Best subset selection is considered the `gold standard' for many sparse learning problems. A variety of optimization techniques have been proposed to attack this non-smooth non-convex problem. In this paper, we investiga…
Sparse LearningSparse Learning and Class Probability Estimation with Weighted Support Vector Machines
Classification and probability estimation have broad applications in modern machine learning and data science applications, including biology, medicine, engineering, and computer science. The recent development of a clas…
Ensemble LearningSparse LearningVariable SelectionSketching for Convex and Nonconvex Regularized Least Squares with Sharp Guarantees
Randomized algorithms are important for solving large-scale optimization problems. In this paper, we propose a fast sketching algorithm for least square problems regularized by convex or nonconvex regularization function…
Sparse LearningBuilding explainable graph neural network by sparse learning for the drug-protein binding prediction
Explainable Graph Neural Networks (GNNs) have been developed and applied to drug-protein binding prediction to identify the key chemical structures in a drug that have active interactions with the target proteins. Howeve…
Graph Neural NetworkSparse LearningvalidHyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization
Sparse neural networks are a key factor in developing resource-efficient machine learning applications. We propose the novel and powerful sparse learning method Adaptive Regularized Training (ART) to compress dense into …
Sparse LearningResource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks
Brain-inspired Spiking Neural Networks (SNNs) have the characteristics of event-driven and high energy-efficient, which are different from traditional Artificial Neural Networks (ANNs) when deployed on edge devices such …
Model CompressionSparse LearningSmoothing the Edges: Smooth Optimization for Sparse Regularization using Hadamard Overparametrization
We present a framework for smooth optimization of explicitly regularized objectives for (structured) sparsity. These non-smooth and possibly non-convex problems typically rely on solvers tailored to specific models and r…
Sparse LearningDiscovering stochastic partial differential equations from limited data using variational Bayes inference
We propose a novel framework for discovering Stochastic Partial Differential Equations (SPDEs) from data. The proposed approach combines the concepts of stochastic calculus, variational Bayes theory, and sparse learning.…
Sparse LearningClassical Verification of Quantum Learning
Quantum data access and quantum processing can make certain classically intractable learning tasks feasible. However, quantum capabilities will only be available to a select few in the near future. Thus, reliable schemes…
Sparse LearningSparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces
Sparsity of a learning solution is a desirable feature in machine learning. Certain reproducing kernel Banach spaces (RKBSs) are appropriate hypothesis spaces for sparse learning methods. The goal of this paper is to und…
Sparse Learning