Sparse Learning Method

Sparse learning methods aim to find efficient and accurate solutions by using only a small subset of available data or model parameters. Current research focuses on improving the speed and scalability of sparse algorithms, particularly within applications like model predictive control, multi-robot path planning, and natural language processing, often employing techniques such as variational Bayesian inference, sparse Gaussian processes, and structured pruning of neural networks. These advancements enhance the efficiency of complex computations, leading to faster inference times and reduced computational costs across various fields. The resulting improvements in efficiency and accuracy have significant implications for resource-constrained applications and large-scale data analysis.

Papers

April 15, 2024

Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control
Qi Zhang, Lei Wang, Weihua Xu, Hongye Su, Lei Xie
Full Model Predictive Control Thermal Control Fuel Cell Sparse Variational Sparse Learning Method

September 13, 2023

Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes (with Appendix)
Kalvik Jakkala, Srinivas Akella
Non Humanoid Robot Novel Regression Document Relevance Informative Path Planning Sparse Gaussian Process Sparse Learning Method

June 28, 2023

An Efficient Sparse Inference Software Accelerator for Transformer-based Language Models on CPUs
Haihao Shen, Hengyu Meng, Bo Dong, Zhe Wang, Ofir Zafrir, Yi Ding, Yu Luo, Hanwen Chang, Qun Gao, Ziheng Wang, Guy Boudoukh, Moshe Wasserblat
Transformer Based Language Model Neural Network Inference Sparse Accelerator Sparse Learning Method

May 21, 2023

Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces
Rui Wang, Yuesheng Xu, Mingsong Yan
LeArning Abstract Sparsity Increase Kernel Banach Space Sparse Kernel Representer Theorem Sparse Learning Method

March 2, 2022

A density peaks clustering algorithm with sparse search and K-d tree
Yunxiao Shan, Shu Li, Fuxiang Li, Yuxin Cui, Shuai Li, Ming Zhou, Xiang Li
Practical Algorithm Sparse Matrix Density Peak Clustering Sparsity Search Density Peak Sparse Learning Method

Sparse Learning Method

Papers

Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control

Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes (with Appendix)

An Efficient Sparse Inference Software Accelerator for Transformer-based Language Models on CPUs

Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces

A density peaks clustering algorithm with sparse search and K-d tree