Deep Learning Publication Navigator - subtopic: dropout

Year TitleAuthor
2017   Interpreting Neural Network Classifications with Variational Dropout Saliency Maps  CH Chang, E Creager, A Goldenberg, D Duvenaud
2017   Normalization and dropout for stochastic computing-based deep convolutional neural networks  J Li, Z Yuan, Z Li, A Ren, C Ding, J Draper, S Nazarian 
2017   Controlled dropout: A different approach to using dropout on deep neural network  BS Ko, HG Kim, KJ Oh, HJ Choi
2017   An improved dropout method and its application into DBN-based handwriting recognition  G Hu, H Li, L Luo, Y Xia
2016   Dropout Versus Weight Decay for Deep Networks  DP Helmbold, PM Long
2016   A Dropout Distribution Model on Deep Transfer Learning Networks  F Li, H Yang, J Wang
2016   Improved Dropout for Shallow and Deep Learning  Z Li, B Gong, T Yang
2016   A New Design Based-SVM of the CNN Classifier Architecture with Dropout for Offline Arabic Handwritten Recognition  M Elleuch, R Maalej, M Kherallah
2016   Selective Dropout for Deep Neural Networks  E Barrow, M Eastwood, C Jayne
2016   Group Dropout Inspired by Ensemble Learning  K Hara, D Saitoh, T Kondou, S Suzuki, H Shouno
2016   Improving quantitative structure–activity relationship models using Artificial Neural Networks trained with dropout  J Mendenhall, J Meiler
2016   Structured Dropout for Weak Label and Multi-Instance Learning and Its Application to Score-Informed Source Separation  S Ewert, MB Sandler
2016   NFLB dropout: Improve generalization ability by dropping out the best-A biologically inspired adaptive dropout method for unsupervised learning  P Yin, L Qi, X Xi, B Zhang, H Qiao
2016   Analysis of Dropout Learning Regarded as Ensemble Learning  K Hara, D Saitoh, H Shouno
2016   Learning deep feature representations with domain guided dropout for person re-identification  T Xiao, H Li, W Ouyang, X Wang
2016   Fundamental differences between Dropout and Weight Decay in Deep Networks  DP Helmbold, PM Long
2015   DropELM: Fast Neural Network Regularization with Dropout and DropConnect  A Iosifidis, A Tefas, I Pitas
2015   Efficient batchwise dropout training using submatrices  B Graham, J Reizenstein, L Robinson
2015   Dropout as a Bayesian Approximation: Appendix  Y Gal, Z Ghahramani
2015   Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning  Y Gal, Z Ghahramani
2015   Parallel Dither and Dropout for Regularising Deep Neural Networks  AJR Simpson
2015   Partitioning Large Scale Deep Belief Networks Using Dropout  Y Huang, S Zhang
2015   Flip-Rotate-Pooling Convolution and Split Dropout on Convolution Neural Networks for Image Classification  F Wu, P Hu, D Kong
2015   A Theoretically Grounded Application of Dropout in Recurrent Neural Networks  Y Gal
2015   Dropout as a Bayesian approximation: Insights and applications  Y Gal, Z Ghahramani
2015   QBDC: Query by dropout committee for training deep supervised architecture  M Ducoffe, F Precioso
2015   Stochastically Reducing Overfitting In Deep Neural Network Using Dropout  N Tripathi, A Jadeja
2015   Dither is Better than Dropout for Regularising Deep Neural Networks  AJR Simpson
2015   Deep Dropout Artificial Neural Networks for Recognising Digits and Characters in Natural Images  E Barrow, C Jayne, M Eastwood
2015   Hierarchical Feature Learning With Dropout K-Means for Hyperspectral Image Classification  F Zhang, B Du, L Zhang, L Zhang
2014   Learning Compact Convolutional Neural Networks with Nested Dropout  C Finn, LA Hendricks, T Darrell
2014   Temporal Dropout of Changes Approach to Convolutional Learning of Spatio-Temporal Features  D Culibrk, N Sebe
2014   On the Inductive Bias of Dropout  DP Helmbold, PM Long
2014   A Comparison of Random Forests and Dropout Nets for Sign Language Recognition with the Kinect  N Jaques, J Nutini