EDropout: Energy-Based Dropout and Pruning of Deep Neural Networks

7 June 2020

Papers citing "EDropout: Energy-Based Dropout and Pruning of Deep Neural Networks"

10 / 10 papers shown

Title
Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Giulia Fracastoro Sophie M. Fosson Andrea Migliorati G. Calafiore 50 1 0 19 Jan 2025
CALICO: Confident Active Learning with Integrated Calibration L. S. Querol Hajime Nagahara Hideaki Hayashi 33 0 0 02 Jul 2024
Magnificent Minified Models Richard E. Harang Hillary Sanders 19 0 0 16 Jun 2023
Joint Human Orientation-Activity Recognition Using WiFi Signals for Human-Machine Interaction Hojjat Salehinejad Navid Hasanzadeh Radomir Djogo S. Valaee 43 14 0 11 Oct 2022
Perturbation of Deep Autoencoder Weights for Model Compression and Classification of Tabular Data Manar D. Samad Sakib Abrar 38 12 0 17 May 2022
Compact Model Training by Low-Rank Projection with Energy Transfer K. Guo Zhenquan Lin Xiaofen Xing Fang Liu Xiangmin Xu 40 2 0 12 Apr 2022
A Survey on Dropout Methods and Experimental Verification in Recommendation Yongqian Li Weizhi Ma C. L. Philip Chen Hao Fei Yiqun Liu Shaoping Ma Yue Yang 40 10 0 05 Apr 2022
S-Rocket: Selective Random Convolution Kernels for Time Series Classification Hojjat Salehinejad Yang Wang Yuanhao Yu Jingshan Tang S. Valaee AI4TS 39 15 0 07 Mar 2022
LiteHAR: Lightweight Human Activity Recognition from WiFi Signals with Random Convolution Kernels Hojjat Salehinejad S. Valaee 27 30 0 23 Jan 2022
Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges Aritz D. Martinez Javier Del Ser Esther Villar-Rodriguez E. Osaba Javier Poyatos Siham Tabik Daniel Molina Francisco Herrera 40 26 0 09 Aug 2020