Variational Dropout and the Local Reparameterization Trick

8 June 2015

Papers citing "Variational Dropout and the Local Reparameterization Trick"

50 / 279 papers shown

Title
Ensemble Model Patching: A Parameter-Efficient Variational Bayesian Neural Network Oscar Chang Yuling Yao David Williams-King Hod Lipson BDL UQCV 32 8 0 23 May 2019
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression Simon Wiedemann H. Kirchhoffer Stefan Matlage Paul Haase Arturo Marbán ... Ahmed Osman D. Marpe H. Schwarz Thomas Wiegand Wojciech Samek MQ 19 21 0 15 May 2019
Survey of Dropout Methods for Deep Neural Networks Alex Labach Hojjat Salehinejad S. Valaee 27 149 0 25 Apr 2019
Relational Reasoning Network (RRN) for Anatomical Landmarking N. Torosdagli Syed Muhammad Anwar P. Verma D. Liberton Janice S. Lee Wade W. Han Ulas Bagci 33 3 0 08 Apr 2019
Effective and Efficient Dropout for Deep Convolutional Neural Networks Shaofeng Cai Jinyang Gao Gang Chen Beng Chin Ooi Wei Wang Meihui Zhang BDL 18 53 0 06 Apr 2019
Progressive Stochastic Binarization of Deep Networks David Hartmann Michael Wand MQ 17 1 0 03 Apr 2019
Correlated Parameters to Accurately Measure Uncertainty in Deep Neural Networks K. Posch J. Pilz UQCV BDL 19 28 0 02 Apr 2019
Focused Quantization for Sparse CNNs Yiren Zhao Xitong Gao Daniel Bates Robert D. Mullins Chengzhong Xu MQ 20 26 0 07 Mar 2019
Variational Inference to Measure Model Uncertainty in Deep Neural Networks K. Posch J. Steinbrener J. Pilz UQCV BDL 14 27 0 26 Feb 2019
The State of Sparsity in Deep Neural Networks Trevor Gale Erich Elsen Sara Hooker 16 743 0 25 Feb 2019
Task2Vec: Task Embedding for Meta-Learning Alessandro Achille Michael Lam Rahul Tewari Avinash Ravichandran Subhransu Maji Charless C. Fowlkes Stefano Soatto Pietro Perona SSL 28 309 0 10 Feb 2019
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data Yinhao Zhu N. Zabaras P. Koutsourelakis P. Perdikaris PINN AI4CE 46 854 0 18 Jan 2019
Entropy-Constrained Training of Deep Neural Networks Simon Wiedemann Arturo Marbán K. Müller Wojciech Samek 27 27 0 18 Dec 2018
Guided Dropout Rohit Keshari Richa Singh Mayank Vatsa BDL 26 37 0 10 Dec 2018
Knowing what you know in brain segmentation using Bayesian deep neural networks Patrick McClure Nao Rho J. Lee Jakub R. Kaczmarzyk C. Zheng Satrajit S. Ghosh D. Nielson Adam G. Thomas P. Bandettini Francisco Pereira UQCV 3DV BDL 24 52 0 03 Dec 2018
Evaluating Bayesian Deep Learning Methods for Semantic Segmentation Jishnu Mukhoti Y. Gal UQCV BDL 33 220 0 30 Nov 2018
Partitioned Variational Inference: A unified framework encompassing federated and continual learning T. Bui Cuong V Nguyen S. Swaroop Richard Turner FedML 24 55 0 27 Nov 2018
Structured Pruning of Neural Networks with Budget-Aware Regularization Carl Lemaire Andrew Achkar Pierre-Marc Jodoin 27 92 0 23 Nov 2018
Sequential Neural Methods for Likelihood-free Inference Conor Durkan George Papamakarios Iain Murray BDL 36 24 0 21 Nov 2018
Scalable agent alignment via reward modeling: a research direction Jan Leike David M. Krueger Tom Everitt Miljan Martic Vishal Maini Shane Legg 34 396 0 19 Nov 2018
Effect Handling for Composable Program Transformations in Edward2 Dave Moore Maria I. Gorinova 8 15 0 15 Nov 2018
A Methodology for Automatic Selection of Activation Functions to Design Hybrid Deep Neural Networks Alberto Marchisio Muhammad Abdullah Hanif Semeen Rehman Maurizio Martina Muhammad Shafique 27 11 0 27 Oct 2018
Good Initializations of Variational Bayes for Deep Models Simone Rossi Pietro Michiardi Maurizio Filippone BDL 17 21 0 18 Oct 2018
Metropolis-Hastings view on variational inference and adversarial training Kirill Neklyudov Evgenii Egorov Pavel Shvechikov Dmitry Vetrov GAN 29 13 0 16 Oct 2018
Rethinking the Value of Network Pruning Zhuang Liu Mingjie Sun Tinghui Zhou Gao Huang Trevor Darrell 10 1,449 0 11 Oct 2018
Understanding Priors in Bayesian Neural Networks at the Unit Level M. Vladimirova Jakob Verbeek Pablo Mesejo Julyan Arbel BDL UQCV 6 4 0 11 Oct 2018
Deterministic Variational Inference for Robust Bayesian Neural Networks Anqi Wu Sebastian Nowozin Edward Meeds Richard Turner José Miguel Hernández-Lobato Alexander L. Gaunt UQCV AAML BDL 29 16 0 09 Oct 2018
Probabilistic Meta-Representations Of Neural Networks Theofanis Karaletsos Peter Dayan Zoubin Ghahramani BDL 12 27 0 01 Oct 2018
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network Xuanqing Liu Yao Li Chongruo Wu Cho-Jui Hsieh AAML OOD 24 171 0 01 Oct 2018
Switching Isotropic and Directional Exploration with Parameter Space Noise in Deep Reinforcement Learning Izumi Karino Kazutoshi Tanaka Ryuma Niiyama Y. Kuniyoshi 14 3 0 18 Sep 2018
Extracting representations of cognition across neuroimaging studies improves brain decoding A. Mensch Julien Mairal B. Thirion Gaël Varoquaux AI4CE 29 15 0 17 Sep 2018
Probabilistic Binary Neural Networks Jorn W. T. Peters Max Welling BDL UQCV MQ 25 50 0 10 Sep 2018
A Neural Temporal Model for Human Motion Prediction Anand Gopalakrishnan A. Mali Daniel Kifer C. Lee Giles Alexander Ororbia 3DH 25 173 0 09 Sep 2018
Pyramidal Recurrent Unit for Language Modeling Sachin Mehta Rik Koncel-Kedziorski Mohammad Rastegari Hannaneh Hajishirzi 21 10 0 27 Aug 2018
Analyzing Inverse Problems with Invertible Neural Networks Lynton Ardizzone Jakob Kruse Sebastian J. Wirkert D. Rahner E. Pellegrini R. Klessen Lena Maier-Hein Carsten Rother Ullrich Kothe 21 483 0 14 Aug 2018
A Survey on Methods and Theories of Quantized Neural Networks Yunhui Guo MQ 29 231 0 13 Aug 2018
Understanding Dropout as an Optimization Trick Sangchul Hahn Heeyoul Choi ODL 13 34 0 26 Jun 2018
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors S. Ghosh Jiayu Yao Finale Doshi-Velez BDL UQCV 15 77 0 13 Jun 2018
Evidential Deep Learning to Quantify Classification Uncertainty Murat Sensoy Lance M. Kaplan M. Kandemir OOD UQCV EDL BDL 90 953 0 05 Jun 2018
Lightweight Probabilistic Deep Networks Jochen Gast Stefan Roth UQCV OOD BDL 33 180 0 29 May 2018
Meta-Learning Probabilistic Inference For Prediction Jonathan Gordon J. Bronskill Matthias Bauer Sebastian Nowozin Richard Turner BDL 45 263 0 24 May 2018
Towards Robust Evaluations of Continual Learning Sebastian Farquhar Y. Gal CLL 28 305 0 24 May 2018
Excitation Dropout: Encouraging Plasticity in Deep Neural Networks Andrea Zunino Sarah Adel Bargal Pietro Morerio Jianming Zhang Stan Sclaroff Vittorio Murino 21 23 0 23 May 2018
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation Manuel Haussmann Fred Hamprecht M. Kandemir BDL 26 6 0 19 May 2018
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows George Papamakarios D. Sterratt Iain Murray BDL 60 358 0 18 May 2018
Real-Time Prediction of the Duration of Distribution System Outages Aaron Jaech Baosen Zhang Mari Ostendorf D. Kirschen 11 74 0 03 Apr 2018
Neural Autoregressive Flows Chin-Wei Huang David M. Krueger Alexandre Lacoste Aaron Courville DRL AI4CE 22 433 0 03 Apr 2018
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches Yeming Wen Paul Vicol Jimmy Ba Dustin Tran Roger C. Grosse BDL 22 307 0 12 Mar 2018
Variance Networks: When Expectation Does Not Meet Your Expectations Kirill Neklyudov Dmitry Molchanov Arsenii Ashukha Dmitry Vetrov UQCV 18 23 0 10 Mar 2018
Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning J. A. G. Higuera D. Meger Gregory Dudek BDL 22 39 0 06 Mar 2018