A Survey on Dropout Methods and Experimental Verification in
Recommendation

A Survey on Dropout Methods and Experimental Verification in Recommendation

5 April 2022

C. L. Philip Chen

Papers citing "A Survey on Dropout Methods and Experimental Verification in Recommendation"

11 / 11 papers shown

Title
R-Block: Regularized Block of Dropout for convolutional networks Liqi Wang Qiyang Hu 14 0 0 27 Jul 2023
Position: Tensor Networks are a Valuable Asset for Green AI Eva Memmel Clara Menzen Jetze T. Schuurmans Frederiek Wesel Kim Batselier 25 5 0 25 May 2022
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 305 7,434 0 11 Nov 2021
Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning Runxin Xu Fuli Luo Zhiyuan Zhang Chuanqi Tan Baobao Chang Songfang Huang Fei Huang LRM 145 178 0 13 Sep 2021
Imperceptible Adversarial Examples for Fake Image Detection Quanyu Liao Yuezun Li Xiaoqiang Guo Bin Kong Yingxin Zhu Jianlei Liu Zhuqing Jiang Qi Song Xi Wu AAML 97 33 0 03 Jun 2021
Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer Zhiwei Liu Ziwei Fan Yu Wang Philip S. Yu 109 145 0 02 May 2021
AutoDropout: Learning Dropout Patterns to Regularize Deep Networks Hieu H. Pham Quoc V. Le 66 56 0 05 Jan 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 270 5,660 0 05 Dec 2016
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference Y. Gal Zoubin Ghahramani UQCV BDL 197 745 0 06 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 279 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