Survey of Dropout Methods for Deep Neural Networks

25 April 2019

Papers citing "Survey of Dropout Methods for Deep Neural Networks"

21 / 21 papers shown

Title
Hadamard product in deep learning: Introduction, Advances and Challenges Grigorios G. Chrysos Yongtao Wu Razvan Pascanu Philip Torr V. Cevher AAML 98 0 0 17 Apr 2025
Regularization can make diffusion models more efficient Mahsa Taheri Johannes Lederer 98 0 0 13 Feb 2025
Deep Learning on Hester Davis Scores for Inpatient Fall Prediction Hojjat Salehinejad Ricky Rojas Kingsley Iheasirim Mohammed Yousufuddin Bijan Borah 39 0 0 11 Jan 2025
Model-based feature selection for neural networks: A mixed-integer programming approach Shudian Zhao Calvin Tsay Jan Kronqvist 34 5 0 20 Feb 2023
Multilayer Perceptron Network Discriminates Larval Zebrafish Genotype using Behaviour Christopher Fusco Angel G Allen 11 0 0 06 Nov 2022
AA-Forecast: Anomaly-Aware Forecast for Extreme Events Ashkan Farhangi Jiang Bian Arthur Huang Haoyi Xiong Jun Wang Zhi-guo Guo AI4TS 19 4 0 21 Aug 2022
Information Geometry of Dropout Training Masanari Kimura H. Hino 9 2 0 22 Jun 2022
A Survey on Dropout Methods and Experimental Verification in Recommendation Y. Li Weizhi Ma C. L. Philip Chen M. Zhang Yiqun Liu Shaoping Ma Yue Yang 33 9 0 05 Apr 2022
Learning Robust Real-Time Cultural Transmission without Human Data Cultural General Intelligence Team Avishkar Bhoopchand Bethanie Brownfield Adrian Collister Agustin Dal Lago ... Alex Platonov Evan Senter Sukhdeep Singh Alexander Zacherl Lei M. Zhang VLM 40 11 0 01 Mar 2022
Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks C. F. G. Santos João Paulo Papa 22 211 0 10 Jan 2022
Neural Networks for Infectious Diseases Detection: Prospects and Challenges Muhammad Azeem Shumaila Javaid Hamza Fahim Nasir Saeed 22 6 0 07 Dec 2021
On the Convergence of Shallow Neural Network Training with Randomly Masked Neurons Fangshuo Liao Anastasios Kyrillidis 36 16 0 05 Dec 2021
HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization Vincent Dumont Casey Garner Anuradha Trivedi Chelsea Jones V. Ganapati Juliane Mueller T. Perciano Mariam Kiran Marcus Day 16 5 0 04 Oct 2021
Combining data assimilation and machine learning to estimate parameters of a convective-scale model Stefanie Legler T. Janjić 26 18 0 07 Sep 2021
R-Drop: Regularized Dropout for Neural Networks Xiaobo Liang Lijun Wu Juntao Li Yue Wang Qi Meng Tao Qin Wei Chen M. Zhang Tie-Yan Liu 41 424 0 28 Jun 2021
EventDrop: data augmentation for event-based learning Fuqiang Gu Weicong Sng Xuke Hu Fei Yu 20 37 0 07 Jun 2021
Review: Deep Learning in Electron Microscopy Jeffrey M. Ede 29 79 0 17 Sep 2020
A Framework for Neural Network Pruning Using Gibbs Distributions Alex Labach S. Valaee 9 5 0 08 Jun 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 273 5,660 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 266 7,634 0 03 Jul 2012