Ian J. Goodfellow

ORCID

Publication Activity (10 Years)

Years Active: 2009-2020
Publications (10 Years): 83

Top Venues

Publications

Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, Aditi Raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian J. Goodfellow, Percy Liang, Pushmeet Kohli
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming. CoRR (2020)
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, Yoshua Bengio
Generative adversarial networks. Commun. ACM 63 (11) (2020)
Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, Aditi Raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian J. Goodfellow, Percy Liang, Pushmeet Kohli
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming. NeurIPS (2020)
David Berthelot, Peyman Milanfar, Ian J. Goodfellow
Creating High Resolution Images with a Latent Adversarial Generator. CoRR (2020)
Yao Qin, Nicholas Carlini, Ian J. Goodfellow, Garrison W. Cottrell, Colin Raffel
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition. CoRR (2019)
David Berthelot, Colin Raffel, Aurko Roy, Ian J. Goodfellow
Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer. ICLR (Poster) (2019)
Ian J. Goodfellow
A Research Agenda: Dynamic Models to Defend Against Correlated Attacks. CoRR (2019)
Gamaleldin F. Elsayed, Ian J. Goodfellow, Jascha Sohl-Dickstein
Adversarial Reprogramming of Neural Networks. ICLR (Poster) (2019)
Augustus Odena, Catherine Olsson, David G. Andersen, Ian J. Goodfellow
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing. ICML (2019)
Han Zhang, Ian J. Goodfellow, Dimitris N. Metaxas, Augustus Odena
Self-Attention Generative Adversarial Networks. ICML (2019)
David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel
MixMatch: A Holistic Approach to Semi-Supervised Learning. CoRR (2019)
David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel
MixMatch: A Holistic Approach to Semi-Supervised Learning. NeurIPS (2019)
Yao Qin, Nicholas Carlini, Garrison W. Cottrell, Ian J. Goodfellow, Colin Raffel
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition. ICML (2019)
Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian J. Goodfellow, Aleksander Madry, Alexey Kurakin
On Evaluating Adversarial Robustness. CoRR (2019)
Samaneh Azadi, Catherine Olsson, Trevor Darrell, Ian J. Goodfellow, Augustus Odena
Discriminator Rejection Sampling. ICLR (Poster) (2019)
Samaneh Azadi, Catherine Olsson, Trevor Darrell, Ian J. Goodfellow, Augustus Odena
Discriminator Rejection Sampling. CoRR (2018)
Avital Oliver, Augustus Odena, Colin Raffel, Ekin Dogus Cubuk, Ian J. Goodfellow
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms. NeurIPS (2018)
Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alexey Kurakin, Ian J. Goodfellow, Jascha Sohl-Dickstein
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans. NeurIPS (2018)
Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian J. Goodfellow, Jascha Sohl-Dickstein
Adversarial Examples that Fool both Human and Computer Vision. CoRR (2018)
Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, Ian J. Goodfellow
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms. CoRR (2018)
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, Been Kim
Sanity Checks for Saliency Maps. NeurIPS (2018)
Augustus Odena, Jacob Buckman, Catherine Olsson, Tom B. Brown, Christopher Olah, Colin Raffel, Ian J. Goodfellow
Is Generator Conditioning Causally Related to GAN Performance? CoRR (2018)
David Berthelot, Colin Raffel, Aurko Roy, Ian J. Goodfellow
Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer. CoRR (2018)
William Fedus, Ian J. Goodfellow, Andrew M. Dai
MaskGAN: Better Text Generation via Filling in the ______. CoRR (2018)
Jacob Buckman, Aurko Roy, Colin Raffel, Ian J. Goodfellow
Thermometer Encoding: One Hot Way To Resist Adversarial Examples. ICLR (Poster) (2018)
Catherine Olsson, Surya Bhupatiraju, Tom B. Brown, Augustus Odena, Ian J. Goodfellow
Skill Rating for Generative Models. CoRR (2018)
Justin Gilmer, Ryan P. Adams, Ian J. Goodfellow, David G. Andersen, George E. Dahl
Motivating the Rules of the Game for Adversarial Example Research. CoRR (2018)
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, Patrick D. McDaniel
Ensemble Adversarial Training: Attacks and Defenses. ICLR (Poster) (2018)
Augustus Odena, Jacob Buckman, Catherine Olsson, Tom B. Brown, Christopher Olah, Colin Raffel, Ian J. Goodfellow
Is Generator Conditioning Causally Related to GAN Performance? ICML (2018)
Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow
Adversarial Spheres. CoRR (2018)
Augustus Odena, Ian J. Goodfellow
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing. CoRR (2018)
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian J. Goodfellow
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step. ICLR (Poster) (2018)
Ian J. Goodfellow
New CleverHans Feature: Better Adversarial Robustness Evaluations with Attack Bundling. CoRR (2018)
Harini Kannan, Alexey Kurakin, Ian J. Goodfellow
Adversarial Logit Pairing. CoRR (2018)
Ian J. Goodfellow
Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size. CoRR (2018)
Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow
Adversarial Spheres. ICLR (Workshop) (2018)
Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, Been Kim
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. ICLR (Workshop) (2018)
William Fedus, Ian J. Goodfellow, Andrew M. Dai
MaskGAN: Better Text Generation via Filling in the _______. ICLR (Poster) (2018)
Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, Been Kim
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. CoRR (2018)
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, Been Kim
Sanity Checks for Saliency Maps. CoRR (2018)
Gamaleldin F. Elsayed, Ian J. Goodfellow, Jascha Sohl-Dickstein
Adversarial Reprogramming of Neural Networks. CoRR (2018)
Ian J. Goodfellow
Defense Against the Dark Arts: An overview of adversarial example security research and future research directions. CoRR (2018)
Alexey Kurakin, Ian J. Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, Alan L. Yuille, Sangxia Huang, Yao Zhao, Yuzhe Zhao, Zhonglin Han, Junjiajia Long, Yerkebulan Berdibekov, Takuya Akiba, Seiya Tokui, Motoki Abe
Adversarial Attacks and Defences Competition. CoRR (2018)
Tom B. Brown, Nicholas Carlini, Chiyuan Zhang, Catherine Olsson, Paul F. Christiano, Ian J. Goodfellow
Unrestricted Adversarial Examples. CoRR (2018)
Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, Ian J. Goodfellow
Realistic Evaluation of Semi-Supervised Learning Algorithms. ICLR (Workshop) (2018)
Ian J. Goodfellow, Patrick D. McDaniel, Nicolas Papernot
Making machine learning robust against adversarial inputs. Commun. ACM 61 (7) (2018)
Han Zhang, Ian J. Goodfellow, Dimitris N. Metaxas, Augustus Odena
Self-Attention Generative Adversarial Networks. CoRR (2018)
Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang
On the Protection of Private Information in Machine Learning Systems: Two Recent Approches. CSF (2017)
Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami
Practical Black-Box Attacks against Machine Learning. AsiaCCS (2017)
Florian Tramèr, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, Patrick D. McDaniel
The Space of Transferable Adversarial Examples. CoRR (2017)
Alexey Kurakin, Ian J. Goodfellow, Samy Bengio
Adversarial Machine Learning at Scale. ICLR (Poster) (2017)
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian J. Goodfellow
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step. CoRR (2017)
Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, Pieter Abbeel
Adversarial Attacks on Neural Network Policies. ICLR (Workshop) (2017)
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, Kunal Talwar
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. ICLR (2017)
Alexey Kurakin, Ian J. Goodfellow, Samy Bengio
Adversarial examples in the physical world. ICLR (Workshop) (2017)
Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang
On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches. CoRR (2017)
Ian J. Goodfellow
NIPS 2016 Tutorial: Generative Adversarial Networks. CoRR (2017)
Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, Pieter Abbeel
Adversarial Attacks on Neural Network Policies. CoRR (2017)
Takeru Miyato, Andrew M. Dai, Ian J. Goodfellow
Adversarial Training Methods for Semi-Supervised Text Classification. ICLR (Poster) (2017)
Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen
Improved Techniques for Training GANs. CoRR (2016)
Ian J. Goodfellow, Nicolas Papernot, Patrick D. McDaniel
cleverhans v0.1: an adversarial machine learning library. CoRR (2016)
Alexey Kurakin, Ian J. Goodfellow, Samy Bengio
Adversarial examples in the physical world. CoRR (2016)
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Gregory S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian J. Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Józefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Gordon Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul A. Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda B. Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, Xiaoqiang Zheng
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. CoRR (2016)
Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang
Deep Learning with Differential Privacy. CoRR (2016)
Stephan Zheng, Yang Song, Thomas Leung, Ian J. Goodfellow
Improving the Robustness of Deep Neural Networks via Stability Training. CVPR (2016)
Tianqi Chen, Ian J. Goodfellow, Jonathon Shlens
Net2Net: Accelerating Learning via Knowledge Transfer. ICLR (2016)
Chelsea Finn, Ian J. Goodfellow, Sergey Levine
Unsupervised Learning for Physical Interaction through Video Prediction. NIPS (2016)
Takeru Miyato, Andrew M. Dai, Ian J. Goodfellow
Virtual Adversarial Training for Semi-Supervised Text Classification. CoRR (2016)
Chelsea Finn, Ian J. Goodfellow, Sergey Levine
Unsupervised Learning for Physical Interaction through Video Prediction. CoRR (2016)
Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami
Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples. CoRR (2016)
Stephan Zheng, Yang Song, Thomas Leung, Ian J. Goodfellow
Improving the Robustness of Deep Neural Networks via Stability Training. CoRR (2016)
Alexey Kurakin, Ian J. Goodfellow, Samy Bengio
Adversarial Machine Learning at Scale. CoRR (2016)
Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermüller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul F. Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron C. Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Melanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian J. Goodfellow, Matthew Graham, Çaglar Gülçehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrançois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Joseph Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph P. Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang
Theano: A Python framework for fast computation of mathematical expressions. CoRR (2016)
Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen
Improved Techniques for Training GANs. NIPS (2016)
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, Kunal Talwar
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. CoRR (2016)
Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples. CoRR (2016)
Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang
Deep Learning with Differential Privacy. CCS (2016)
Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy
Explaining and Harnessing Adversarial Examples. ICLR (Poster) (2015)
Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron C. Courville, Mehdi Mirza, Benjamin Hamner, William Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Tudor Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Zhang Chuang, Yoshua Bengio
Challenges in representation learning: A report on three machine learning contests. Neural Networks 64 (2015)
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian J. Goodfellow
Adversarial Autoencoders. CoRR (2015)
Ian J. Goodfellow
On distinguishability criteria for estimating generative models ICLR (Workshop) (2015)
Ian J. Goodfellow
Efficient Per-Example Gradient Computations. CoRR (2015)
Ian J. Goodfellow, Oriol Vinyals
Qualitatively characterizing neural network optimization problems. ICLR (2015)
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, Yoshua Bengio
Generative Adversarial Nets. NIPS (2014)
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, Yoshua Bengio
Generative Adversarial Networks. CoRR (2014)
Ian J. Goodfellow, Mehdi Mirza, Xia Da, Aaron C. Courville, Yoshua Bengio
An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks. ICLR (Poster) (2014)
Ian J. Goodfellow, Yaroslav Bulatov, Julian Ibarz, Sacha Arnoud, Vinay D. Shet
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks. ICLR (2014)
David Warde-Farley, Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio
An empirical analysis of dropout in piecewise linear networks. ICLR (Poster) (2014)
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, Rob Fergus
Intriguing properties of neural networks. ICLR (Poster) (2014)
Vincent Dumoulin, Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio
On the Challenges of Physical Implementations of RBMs. AAAI (2014)
Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron C. Courville, Mehdi Mirza, Benjamin Hamner, William Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Tudor Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Chuang Zhang, Yoshua Bengio
Challenges in Representation Learning: A report on three machine learning contests. CoRR (2013)
Vincent Dumoulin, Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio
On the Challenges of Physical Implementations of RBMs. CoRR (2013)
Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio
Scaling Up Spike-and-Slab Models for Unsupervised Feature Learning. IEEE Trans. Pattern Anal. Mach. Intell. 35 (8) (2013)
Ian J. Goodfellow
Piecewise Linear Multilayer Perceptrons and Dropout CoRR (2013)
Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio
Joint Training Deep Boltzmann Machines for Classification ICLR (Workshop) (2013)
Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron C. Courville, Mehdi Mirza, Benjamin Hamner, William Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Tudor Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Chuang Zhang, Yoshua Bengio
Challenges in Representation Learning: A Report on Three Machine Learning Contests. ICONIP (3) (2013)
Ian J. Goodfellow, Mehdi Mirza, Aaron C. Courville, Yoshua Bengio
Multi-Prediction Deep Boltzmann Machines. NIPS (2013)
Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron C. Courville, Yoshua Bengio
Maxout Networks. ICML (3) (2013)
Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron C. Courville, Yoshua Bengio
Maxout Networks CoRR (2013)
Ian J. Goodfellow, David Warde-Farley, Pascal Lamblin, Vincent Dumoulin, Mehdi Mirza, Razvan Pascanu, James Bergstra, Frédéric Bastien, Yoshua Bengio
Pylearn2: a machine learning research library. CoRR (2013)

Ian J. Goodfellow.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Ian J. Goodfellow