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Ian J. Goodfellow
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- affiliation: Google Brain
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Books and Theses
- 2016
- [b1]Ian J. Goodfellow, Yoshua Bengio, Aaron C. Courville:
Deep Learning. Adaptive computation and machine learning, MIT Press 2016, ISBN 978-0-262-03561-3, pp. 1-775
Journal Articles
- 2020
- [j4]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): 139-144 (2020) - 2018
- [j3]Ian J. Goodfellow, Patrick D. McDaniel, Nicolas Papernot:
Making machine learning robust against adversarial inputs. Commun. ACM 61(7): 56-66 (2018) - 2015
- [j2]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: 59-63 (2015) - 2013
- [j1]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): 1902-1914 (2013)
Conference and Workshop Papers
- 2020
- [c48]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 - 2019
- [c47]Samaneh Azadi, Catherine Olsson, Trevor Darrell, Ian J. Goodfellow, Augustus Odena:
Discriminator Rejection Sampling. ICLR (Poster) 2019 - [c46]David Berthelot, Colin Raffel, Aurko Roy, Ian J. Goodfellow:
Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer. ICLR (Poster) 2019 - [c45]Gamaleldin F. Elsayed, Ian J. Goodfellow, Jascha Sohl-Dickstein:
Adversarial Reprogramming of Neural Networks. ICLR (Poster) 2019 - [c44]Augustus Odena, Catherine Olsson, David G. Andersen, Ian J. Goodfellow:
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing. ICML 2019: 4901-4911 - [c43]Yao Qin, Nicholas Carlini, Garrison W. Cottrell, Ian J. Goodfellow, Colin Raffel:
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition. ICML 2019: 5231-5240 - [c42]Han Zhang, Ian J. Goodfellow, Dimitris N. Metaxas, Augustus Odena:
Self-Attention Generative Adversarial Networks. ICML 2019: 7354-7363 - [c41]David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel:
MixMatch: A Holistic Approach to Semi-Supervised Learning. NeurIPS 2019: 5050-5060 - 2018
- [c40]Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, Been Kim:
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. ICLR (Workshop) 2018 - [c39]Jacob Buckman, Aurko Roy, Colin Raffel, Ian J. Goodfellow:
Thermometer Encoding: One Hot Way To Resist Adversarial Examples. ICLR (Poster) 2018 - [c38]William Fedus, Ian J. Goodfellow, Andrew M. Dai:
MaskGAN: Better Text Generation via Filling in the _______. ICLR (Poster) 2018 - [c37]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 - [c36]Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow:
Adversarial Spheres. ICLR (Workshop) 2018 - [c35]Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, Ian J. Goodfellow:
Realistic Evaluation of Semi-Supervised Learning Algorithms. ICLR (Workshop) 2018 - [c34]Florian Tramèr
, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, Patrick D. McDaniel:
Ensemble Adversarial Training: Attacks and Defenses. ICLR (Poster) 2018 - [c33]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: 3846-3855 - [c32]Avital Oliver, Augustus Odena, Colin Raffel, Ekin Dogus Cubuk, Ian J. Goodfellow:
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms. NeurIPS 2018: 3239-3250 - [c31]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: 3914-3924 - [c30]Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, Been Kim:
Sanity Checks for Saliency Maps. NeurIPS 2018: 9525-9536 - 2017
- [c29]Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami:
Practical Black-Box Attacks against Machine Learning. AsiaCCS 2017: 506-519 - [c28]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: 1-6 - [c27]Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, Pieter Abbeel:
Adversarial Attacks on Neural Network Policies. ICLR (Workshop) 2017 - [c26]Alexey Kurakin, Ian J. Goodfellow, Samy Bengio:
Adversarial Machine Learning at Scale. ICLR (Poster) 2017 - [c25]Alexey Kurakin, Ian J. Goodfellow, Samy Bengio:
Adversarial examples in the physical world. ICLR (Workshop) 2017 - [c24]Takeru Miyato, Andrew M. Dai, Ian J. Goodfellow:
Adversarial Training Methods for Semi-Supervised Text Classification. ICLR (Poster) 2017 - [c23]Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, Kunal Talwar:
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. ICLR 2017 - 2016
- [c22]Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov
, Kunal Talwar, Li Zhang:
Deep Learning with Differential Privacy. CCS 2016: 308-318 - [c21]Stephan Zheng, Yang Song, Thomas Leung, Ian J. Goodfellow:
Improving the Robustness of Deep Neural Networks via Stability Training. CVPR 2016: 4480-4488 - [c20]Chelsea Finn, Ian J. Goodfellow, Sergey Levine:
Unsupervised Learning for Physical Interaction through Video Prediction. NIPS 2016: 64-72 - [c19]Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen:
Improved Techniques for Training GANs. NIPS 2016: 2226-2234 - [c18]Tianqi Chen, Ian J. Goodfellow, Jonathon Shlens:
Net2Net: Accelerating Learning via Knowledge Transfer. ICLR 2016 - 2015
- [c17]Ian J. Goodfellow:
On distinguishability criteria for estimating generative models. ICLR (Workshop) 2015 - [c16]Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy:
Explaining and Harnessing Adversarial Examples. ICLR (Poster) 2015 - [c15]Ian J. Goodfellow, Oriol Vinyals:
Qualitatively characterizing neural network optimization problems. ICLR 2015 - 2014
- [c14]Vincent Dumoulin, Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
On the Challenges of Physical Implementations of RBMs. AAAI 2014: 1199-1205 - [c13]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: 2672-2680 - [c12]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 - [c11]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 - [c10]Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, Rob Fergus:
Intriguing properties of neural networks. ICLR (Poster) 2014 - [c9]David Warde-Farley, Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
An empirical analysis of dropout in piecewise linear networks. ICLR (Poster) 2014 - 2013
- [c8]Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron C. Courville, Yoshua Bengio:
Maxout Networks. ICML (3) 2013: 1319-1327 - [c7]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: 117-124 - [c6]Ian J. Goodfellow, Mehdi Mirza, Aaron C. Courville, Yoshua Bengio:
Multi-Prediction Deep Boltzmann Machines. NIPS 2013: 548-556 - [c5]Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
Joint Training Deep Boltzmann Machines for Classification. ICLR (Workshop) 2013 - 2012
- [c4]Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
Large-Scale Feature Learning With Spike-and-Slab Sparse Coding. ICML 2012 - [c3]Grégoire Mesnil, Yann N. Dauphin, Xavier Glorot
, Salah Rifai, Yoshua Bengio, Ian J. Goodfellow, Erick Lavoie, Xavier Muller, Guillaume Desjardins, David Warde-Farley, Pascal Vincent, Aaron C. Courville, James Bergstra:
Unsupervised and Transfer Learning Challenge: a Deep Learning Approach. ICML Unsupervised and Transfer Learning 2012: 97-110 - 2010
- [c2]Ian J. Goodfellow
, Nate Koenig, Marius Muja, Caroline Pantofaru, Alexander Sorokin, Leila Takayama:
Help me help you: interfaces for personal robots. HRI 2010: 187-188 - 2009
- [c1]Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak Lee, Andrew Y. Ng:
Measuring Invariances in Deep Networks. NIPS 2009: 646-654
Informal and Other Publications
- 2020
- [i55]David Berthelot, Peyman Milanfar, Ian J. Goodfellow:
Creating High Resolution Images with a Latent Adversarial Generator. CoRR abs/2003.02365 (2020) - [i54]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 abs/2010.11645 (2020) - 2019
- [i53]Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian J. Goodfellow, Aleksander Madry, Alexey Kurakin:
On Evaluating Adversarial Robustness. CoRR abs/1902.06705 (2019) - [i52]Ian J. Goodfellow:
A Research Agenda: Dynamic Models to Defend Against Correlated Attacks. CoRR abs/1903.06293 (2019) - [i51]Yao Qin, Nicholas Carlini, Ian J. Goodfellow, Garrison W. Cottrell, Colin Raffel:
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition. CoRR abs/1903.10346 (2019) - [i50]David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel:
MixMatch: A Holistic Approach to Semi-Supervised Learning. CoRR abs/1905.02249 (2019) - 2018
- [i49]Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow:
Adversarial Spheres. CoRR abs/1801.02774 (2018) - [i48]William Fedus, Ian J. Goodfellow, Andrew M. Dai:
MaskGAN: Better Text Generation via Filling in the ______. CoRR abs/1801.07736 (2018) - [i47]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 abs/1802.08195 (2018) - [i46]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 abs/1802.08768 (2018) - [i45]Harini Kannan, Alexey Kurakin, Ian J. Goodfellow:
Adversarial Logit Pairing. CoRR abs/1803.06373 (2018) - [i44]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 abs/1804.00097 (2018) - [i43]Ian J. Goodfellow:
Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size. CoRR abs/1804.07870 (2018) - [i42]Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, Ian J. Goodfellow:
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms. CoRR abs/1804.09170 (2018) - [i41]Han Zhang, Ian J. Goodfellow, Dimitris N. Metaxas, Augustus Odena:
Self-Attention Generative Adversarial Networks. CoRR abs/1805.08318 (2018) - [i40]Ian J. Goodfellow:
Defense Against the Dark Arts: An overview of adversarial example security research and future research directions. CoRR abs/1806.04169 (2018) - [i39]Gamaleldin F. Elsayed, Ian J. Goodfellow, Jascha Sohl-Dickstein:
Adversarial Reprogramming of Neural Networks. CoRR abs/1806.11146 (2018) - [i38]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 abs/1807.06732 (2018) - [i37]David Berthelot, Colin Raffel, Aurko Roy, Ian J. Goodfellow:
Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer. CoRR abs/1807.07543 (2018) - [i36]Augustus Odena, Ian J. Goodfellow:
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing. CoRR abs/1807.10875 (2018) - [i35]Catherine Olsson, Surya Bhupatiraju, Tom B. Brown, Augustus Odena, Ian J. Goodfellow:
Skill Rating for Generative Models. CoRR abs/1808.04888 (2018) - [i34]Tom B. Brown, Nicholas Carlini, Chiyuan Zhang, Catherine Olsson, Paul F. Christiano, Ian J. Goodfellow:
Unrestricted Adversarial Examples. CoRR abs/1809.08352 (2018) - [i33]Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, Been Kim:
Sanity Checks for Saliency Maps. CoRR abs/1810.03292 (2018) - [i32]Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, Been Kim:
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. CoRR abs/1810.03307 (2018) - [i31]Samaneh Azadi, Catherine Olsson, Trevor Darrell, Ian J. Goodfellow, Augustus Odena:
Discriminator Rejection Sampling. CoRR abs/1810.06758 (2018) - [i30]Ian J. Goodfellow:
New CleverHans Feature: Better Adversarial Robustness Evaluations with Attack Bundling. CoRR abs/1811.03685 (2018) - 2017
- [i29]Ian J. Goodfellow:
NIPS 2016 Tutorial: Generative Adversarial Networks. CoRR abs/1701.00160 (2017) - [i28]Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, Pieter Abbeel:
Adversarial Attacks on Neural Network Policies. CoRR abs/1702.02284 (2017) - [i27]Florian Tramèr, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, Patrick D. McDaniel:
The Space of Transferable Adversarial Examples. CoRR abs/1704.03453 (2017) - [i26]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 abs/1708.08022 (2017) - [i25]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 abs/1710.08446 (2017) - 2016
- [i24]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 abs/1602.02697 (2016) - [i23]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 abs/1603.04467 (2016) - [i22]Stephan Zheng, Yang Song, Thomas Leung, Ian J. Goodfellow:
Improving the Robustness of Deep Neural Networks via Stability Training. CoRR abs/1604.04326 (2016) - [i21]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 abs/1605.02688 (2016) - [i20]Chelsea Finn, Ian J. Goodfellow, Sergey Levine:
Unsupervised Learning for Physical Interaction through Video Prediction. CoRR abs/1605.07157 (2016) - [i19]Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow:
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples. CoRR abs/1605.07277 (2016) - [i18]Takeru Miyato, Andrew M. Dai, Ian J. Goodfellow:
Virtual Adversarial Training for Semi-Supervised Text Classification. CoRR abs/1605.07725 (2016) - [i17]Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen:
Improved Techniques for Training GANs. CoRR abs/1606.03498 (2016) - [i16]Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang:
Deep Learning with Differential Privacy. CoRR abs/1607.00133 (2016) - [i15]Alexey Kurakin, Ian J. Goodfellow, Samy Bengio:
Adversarial examples in the physical world. CoRR abs/1607.02533 (2016) - [i14]Ian J. Goodfellow, Nicolas Papernot, Patrick D. McDaniel:
cleverhans v0.1: an adversarial machine learning library. CoRR abs/1610.00768 (2016) - [i13]Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, Kunal Talwar:
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. CoRR abs/1610.05755 (2016) - [i12]Alexey Kurakin, Ian J. Goodfellow, Samy Bengio:
Adversarial Machine Learning at Scale. CoRR abs/1611.01236 (2016) - 2015
- [i11]Ian J. Goodfellow:
Efficient Per-Example Gradient Computations. CoRR abs/1510.01799 (2015) - [i10]Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian J. Goodfellow:
Adversarial Autoencoders. CoRR abs/1511.05644 (2015) - 2014
- [i9]Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, Yoshua Bengio:
Generative Adversarial Networks. CoRR abs/1406.2661 (2014) - 2013
- [i8]Ian J. Goodfellow:
Piecewise Linear Multilayer Perceptrons and Dropout. CoRR abs/1301.5088 (2013) - [i7]Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron C. Courville, Yoshua Bengio:
Maxout Networks. CoRR abs/1302.4389 (2013) - [i6]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 abs/1307.0414 (2013) - [i5]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 abs/1308.4214 (2013) - [i4]Vincent Dumoulin, Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
On the Challenges of Physical Implementations of RBMs. CoRR abs/1312.5258 (2013) - 2012
- [i3]Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
Spike-and-Slab Sparse Coding for Unsupervised Feature Discovery. CoRR abs/1201.3382 (2012) - [i2]Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, James Bergstra, Ian J. Goodfellow, Arnaud Bergeron, Nicolas Bouchard, David Warde-Farley, Yoshua Bengio:
Theano: new features and speed improvements. CoRR abs/1211.5590 (2012) - [i1]Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
Joint Training of Deep Boltzmann Machines. CoRR abs/1212.2686 (2012)
Coauthor Index
![](https://dblp.uni-trier.de/img/cog.dark.24x24.png)
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, and
to record detail pages.
load references from crossref.org and opencitations.net
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Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
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OpenAlex data
Load additional information about publications from .
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last updated on 2024-05-08 21:40 CEST by the dblp team
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