Papers Sparse Learning
“Sparse Learning” 태그가 달린 논문 209편 · 필터 해제
The ART of Transfer Learning: An Adaptive and Robust Pipeline
Transfer learning is an essential tool for improving the performance of primary tasks by leveraging information from auxiliary data resources. In this work, we propose Adaptive Robust Transfer Learning (ART), a flexible …
Learning TheorySparse LearningTransfer LearningChannel Estimation for Underwater Visible Light Communication: A Sparse Learning Perspective
The underwater propagation environment for visible light signals is affected by complex factors such as absorption, shadowing, and reflection, making it very challengeable to achieve effective underwater visible light co…
compressed sensingSparse LearningLearning to Super-Resolve Blurry Images with Events
Super-Resolution from a single motion Blurred image (SRB) is a severely ill-posed problem due to the joint degradation of motion blurs and low spatial resolution. In this paper, we employ events to alleviate the burden o…
Sparse LearningSuper-ResolutionVideo-Text Retrieval by Supervised Sparse Multi-Grained Learning
While recent progress in video-text retrieval has been advanced by the exploration of better representation learning, in this paper, we present a novel multi-grained sparse learning framework, S3MA, to learn an aligned s…
Representation LearningRetrievalSparse LearningText Retrieval+2Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks
Due to the significant computational challenge of training large-scale graph neural networks (GNNs), various sparse learning techniques have been exploited to reduce memory and storage costs. Examples include \textit{gra…
Sparse LearningSuperGF: Unifying Local and Global Features for Visual Localization
Advanced visual localization techniques encompass image retrieval challenges and 6 Degree-of-Freedom (DoF) camera pose estimation, such as hierarchical localization. Thus, they must extract global and local features from…
Camera Pose EstimationComputational EfficiencyImage RetrievalPose Estimation+3Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity
$\ell_0$ constrained optimization is prevalent in machine learning, particularly for high-dimensional problems, because it is a fundamental approach to achieve sparse learning. Hard-thresholding gradient descent is a dom…
Portfolio OptimizationSparse LearningStochastic OptimizationLearning sparse auto-encoders for green AI image coding
Recently, convolutional auto-encoders (CAE) were introduced for image coding. They achieved performance improvements over the state-of-the-art JPEG2000 method. However, these performances were obtained using massive CAEs…
Image CompressionSparse LearningLottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge
Edge devices can benefit remarkably from federated learning due to their distributed nature; however, their limited resource and computing power poses limitations in deployment. A possible solution to this problem is to …
Federated LearningModel CompressionSparse LearningLearning governing physics from output only measurements
Extracting governing physics from data is a key challenge in many areas of science and technology. The existing techniques for equations discovery are dependent on both input and state measurements; however, in practice,…
Sparse LearningControlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints
The performance of trained neural networks is robust to harsh levels of pruning. Coupled with the ever-growing size of deep learning models, this observation has motivated extensive research on learning sparse models. In…
Sparse LearningAMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems
In this work, we propose an adaptive sparse learning algorithm that can be applied to learn the physical processes and obtain a sparse representation of the solution given a large snapshot space. Assume that there is a r…
Sparse LearningAttribute Graphs Underlying Molecular Generative Models: Path to Learning with Limited Data
Training generative models that capture rich semantics of the data and interpreting the latent representations encoded by such models are very important problems in un-/self-supervised learning. In this work, we provide …
AttributeCausal Discoveryfeature selectionSelf-Supervised Learning+1Sparsifying Binary Networks
Binary neural networks (BNNs) have demonstrated their ability to solve complex tasks with comparable accuracy as full-precision deep neural networks (DNNs), while also reducing computational power and storage requirement…
BinarizationQuantizationSparse LearningSparse Learning and binarizationBest Subset Selection with Efficient Primal-Dual Algorithm
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-convex and NP-hard problem. In this paper, we investig…
Sparse LearningA bio-inspired implementation of a sparse-learning spike-based hippocampus memory model
The nervous system, more specifically, the brain, is capable of solving complex problems simply and efficiently, far surpassing modern computers. In this regard, neuromorphic engineering is a research field that focuses …
HippocampusSparse LearningInfinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations
The paper has two major themes. The first part of the paper establishes certain general results for infinite-dimensional optimization problems on Hilbert spaces. These results cover the classical representer theorem and …
Sparse LearningAdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning
Standard fine-tuning of large pre-trained language models (PLMs) for downstream tasks requires updating hundreds of millions to billions of parameters, and storing a large copy of the PLM weights for every task resulting…
Natural Language Understandingparameter-efficient fine-tuningSparse LearningSAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications
Federated learning (FL) enables edge devices to collaboratively learn a model in a distributed fashion. Many existing researches have focused on improving communication efficiency of high-dimensional models and addressin…
Federated LearningSparse LearningAPP: Anytime Progressive Pruning
With the latest advances in deep learning, there has been a lot of focus on the online learning paradigm due to its relevance in practical settings. Although many methods have been investigated for optimal learning setti…
Network PruningSparse Learning