A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks

7 May 2019

Erik Nijkamp

Papers citing "A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks"

26 / 26 papers shown

Title
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 393 10,591 0 17 Feb 2020
Neural Network Quine Oscar Chang Hod Lipson 43 23 0 15 Mar 2018
Essentially No Barriers in Neural Network Energy Landscape Felix Dräxler K. Veschgini M. Salmhofer Fred Hamprecht MoMe 111 432 0 02 Mar 2018
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs T. Garipov Pavel Izmailov Dmitrii Podoprikhin Dmitry Vetrov A. Wilson UQCV 83 750 0 27 Feb 2018
Stochastic Hyperparameter Optimization through Hypernetworks Jonathan Lorraine David Duvenaud 71 140 0 26 Feb 2018
Bayesian Hypernetworks David M. Krueger Chin-Wei Huang Riashat Islam Ryan Turner Alexandre Lacoste Aaron Courville UQCV BDL 59 139 0 13 Oct 2017
Stochastic Gradient Descent as Approximate Bayesian Inference Stephan Mandt Matthew D. Hoffman David M. Blei BDL 52 597 0 13 Apr 2017
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks Christos Louizos Max Welling BDL 144 460 0 06 Mar 2017
Diversified Texture Synthesis with Feed-forward Networks Yijun Li Chen Fang Jimei Yang Zhaowen Wang Xin Lu Ming-Hsuan Yang 44 268 0 05 Mar 2017
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 459 5,372 0 05 Nov 2016
Topology and Geometry of Half-Rectified Network Optimization C. Freeman Joan Bruna 192 235 0 04 Nov 2016
Learning feed-forward one-shot learners Luca Bertinetto João F. Henriques Jack Valmadre Philip Torr Andrea Vedaldi 62 471 0 16 Jun 2016
Learning to learn by gradient descent by gradient descent Marcin Andrychowicz Misha Denil Sergio Gomez Colmenarejo Matthew W. Hoffman David Pfau Tom Schaul Brendan Shillingford Nando de Freitas 108 2,006 0 14 Jun 2016
A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets Srikrishna Karanam Mengran Gou Ziyan Wu Angels Rates-Borras Mario Sznaier Richard J. Radke 90 58 0 31 May 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 821 9,306 0 06 Jun 2015
Variational Inference with Normalizing Flows Danilo Jimenez Rezende S. Mohamed DRL BDL 310 4,179 0 21 May 2015
Weight Uncertainty in Neural Networks Charles Blundell Julien Cornebise Koray Kavukcuoglu Daan Wierstra UQCV BDL 185 1,886 0 20 May 2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 463 43,289 0 11 Feb 2015
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification Kaiming He Xinming Zhang Shaoqing Ren Jian Sun VLM 323 18,613 0 06 Feb 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.8K 150,039 0 22 Dec 2014
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 277 19,049 0 20 Dec 2014
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 254 1,198 0 30 Nov 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.6K 100,348 0 04 Sep 2014
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization Yann N. Dauphin Razvan Pascanu Çağlar Gülçehre Kyunghyun Cho Surya Ganguli Yoshua Bengio ODL 126 1,385 0 10 Jun 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 270 14,918 1 21 Dec 2013
Predicting Parameters in Deep Learning Misha Denil B. Shakibi Laurent Dinh MarcÁurelio Ranzato Nando de Freitas OOD 195 1,318 0 03 Jun 2013