Ian J. Goodfellow

According to our database1, Ian J. Goodfellow authored at least 76 papers between 2009 and 2018.

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Bibliography

2018
Discriminator Rejection Sampling.
CoRR, 2018

Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values.
CoRR, 2018

Sanity Checks for Saliency Maps.
CoRR, 2018

Unrestricted Adversarial Examples.
CoRR, 2018

Skill Rating for Generative Models.
CoRR, 2018

TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing.
CoRR, 2018

Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer.
CoRR, 2018

Motivating the Rules of the Game for Adversarial Example Research.
CoRR, 2018

Adversarial Reprogramming of Neural Networks.
CoRR, 2018

Defense Against the Dark Arts: An overview of adversarial example security research and future research directions.
CoRR, 2018

Self-Attention Generative Adversarial Networks.
CoRR, 2018

Realistic Evaluation of Deep Semi-Supervised Learning Algorithms.
CoRR, 2018

Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size.
CoRR, 2018

Adversarial Attacks and Defences Competition.
CoRR, 2018

Adversarial Logit Pairing.
CoRR, 2018

Is Generator Conditioning Causally Related to GAN Performance?
CoRR, 2018

Adversarial Examples that Fool both Human and Computer Vision.
CoRR, 2018

MaskGAN: Better Text Generation via Filling in the ______.
CoRR, 2018

Adversarial Spheres.
CoRR, 2018

Making machine learning robust against adversarial inputs.
Commun. ACM, 2018

Is Generator Conditioning Causally Related to GAN Performance?
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step.
CoRR, 2017

On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches.
CoRR, 2017

The Space of Transferable Adversarial Examples.
CoRR, 2017

Adversarial Attacks on Neural Network Policies.
CoRR, 2017

NIPS 2016 Tutorial: Generative Adversarial Networks.
CoRR, 2017

On the Protection of Private Information in Machine Learning Systems: Two Recent Approches.
Proceedings of the 30th IEEE Computer Security Foundations Symposium, 2017

Practical Black-Box Attacks against Machine Learning.
Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, 2017

2016
Improving the Robustness of Deep Neural Networks via Stability Training.
CoRR, 2016

Improved Techniques for Training GANs.
CoRR, 2016

Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples.
CoRR, 2016

Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples.
CoRR, 2016

Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data.
CoRR, 2016

Virtual Adversarial Training for Semi-Supervised Text Classification.
CoRR, 2016

Adversarial Machine Learning at Scale.
CoRR, 2016

Adversarial examples in the physical world.
CoRR, 2016

cleverhans v0.1: an adversarial machine learning library.
CoRR, 2016

Unsupervised Learning for Physical Interaction through Video Prediction.
CoRR, 2016

Theano: A Python framework for fast computation of mathematical expressions.
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CoRR, 2016

Deep Learning with Differential Privacy.
CoRR, 2016

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.
CoRR, 2016

Improved Techniques for Training GANs.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Unsupervised Learning for Physical Interaction through Video Prediction.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Improving the Robustness of Deep Neural Networks via Stability Training.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

Deep Learning with Differential Privacy.
Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 2016

Deep Learning.
Adaptive computation and machine learning, MIT Press, ISBN: 978-0-262-03561-3, 2016

2015
Challenges in representation learning: A report on three machine learning contests.
Neural Networks, 2015

Adversarial Autoencoders.
CoRR, 2015

Efficient Per-Example Gradient Computations.
CoRR, 2015

Net2Net: Accelerating Learning via Knowledge Transfer.
CoRR, 2015

2014
Qualitatively characterizing neural network optimization problems.
CoRR, 2014

Explaining and Harnessing Adversarial Examples.
CoRR, 2014

Generative Adversarial Networks.
CoRR, 2014

Generative Adversarial Nets.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

On the Challenges of Physical Implementations of RBMs.
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013
Scaling Up Spike-and-Slab Models for Unsupervised Feature Learning.
IEEE Trans. Pattern Anal. Mach. Intell., 2013

Maxout Networks
CoRR, 2013

Piecewise Linear Multilayer Perceptrons and Dropout
CoRR, 2013

Joint Training Deep Boltzmann Machines for Classification
CoRR, 2013

An empirical analysis of dropout in piecewise linear networks.
CoRR, 2013

Intriguing properties of neural networks.
CoRR, 2013

Pylearn2: a machine learning research library.
CoRR, 2013

An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks.
CoRR, 2013

Challenges in Representation Learning: A report on three machine learning contests.
CoRR, 2013

Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks.
CoRR, 2013

On the Challenges of Physical Implementations of RBMs.
CoRR, 2013

Multi-Prediction Deep Boltzmann Machines.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013


Maxout Networks.
Proceedings of the 30th International Conference on Machine Learning, 2013

2012
Unsupervised and Transfer Learning Challenge: a Deep Learning Approach.
Proceedings of the Unsupervised and Transfer Learning, 2012

Joint Training of Deep Boltzmann Machines
CoRR, 2012

Theano: new features and speed improvements
CoRR, 2012

Spike-and-Slab Sparse Coding for Unsupervised Feature Discovery
CoRR, 2012

Large-Scale Feature Learning With Spike-and-Slab Sparse Coding.
Proceedings of the 29th International Conference on Machine Learning, 2012

2010
Help me help you: interfaces for personal robots.
Proceedings of the 5th ACM/IEEE International Conference on Human Robot Interaction, 2010

2009
Measuring Invariances in Deep Networks.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009


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