Search Results for author: Ian Goodfellow

Found 56 papers, 37 papers with code

Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming

2 code implementations NeurIPS 2020 Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, aditi raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian Goodfellow, Percy Liang, Pushmeet Kohli

In this work, we propose a first-order dual SDP algorithm that (1) requires memory only linear in the total number of network activations, (2) only requires a fixed number of forward/backward passes through the network per iteration.

Creating High Resolution Images with a Latent Adversarial Generator

1 code implementation4 Mar 2020 David Berthelot, Peyman Milanfar, Ian Goodfellow

That is to say, instead of generating an arbitrary image as a sample from the manifold of natural images, we propose to sample images from a particular "subspace" of natural images, directed by a low-resolution image from the same subspace.

Image Super-Resolution

Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition

1 code implementation22 Mar 2019 Yao Qin, Nicholas Carlini, Ian Goodfellow, Garrison Cottrell, Colin Raffel

Adversarial examples are inputs to machine learning models designed by an adversary to cause an incorrect output.

Speech Recognition

A Research Agenda: Dynamic Models to Defend Against Correlated Attacks

no code implementations14 Mar 2019 Ian Goodfellow

When machine learning is used in contexts where security is a concern, it is desirable to design models that perform well even when the input is designed by a malicious adversary.

A domain agnostic measure for monitoring and evaluating GANs

1 code implementation NeurIPS 2019 Paulina Grnarova, Kfir. Y. Levy, Aurelien Lucchi, Nathanael Perraudin, Ian Goodfellow, Thomas Hofmann, Andreas Krause

Evaluations are essential for: (i) relative assessment of different models and (ii) monitoring the progress of a single model throughout training.

New CleverHans Feature: Better Adversarial Robustness Evaluations with Attack Bundling

no code implementations8 Nov 2018 Ian Goodfellow

This technical report describes a new feature of the CleverHans library called "attack bundling".

Adversarial Robustness

Discriminator Rejection Sampling

1 code implementation ICLR 2019 Samaneh Azadi, Catherine Olsson, Trevor Darrell, Ian Goodfellow, Augustus Odena

We propose a rejection sampling scheme using the discriminator of a GAN to approximately correct errors in the GAN generator distribution.

Image Generation

Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values

no code implementations8 Oct 2018 Julius Adebayo, Justin Gilmer, Ian Goodfellow, Been Kim

Explaining the output of a complicated machine learning model like a deep neural network (DNN) is a central challenge in machine learning.

Unrestricted Adversarial Examples

1 code implementation22 Sep 2018 Tom B. Brown, Nicholas Carlini, Chiyuan Zhang, Catherine Olsson, Paul Christiano, Ian Goodfellow

We introduce a two-player contest for evaluating the safety and robustness of machine learning systems, with a large prize pool.

Skill Rating for Generative Models

no code implementations14 Aug 2018 Catherine Olsson, Surya Bhupatiraju, Tom Brown, Augustus Odena, Ian Goodfellow

We explore a new way to evaluate generative models using insights from evaluation of competitive games between human players.

TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing

3 code implementations28 Jul 2018 Augustus Odena, Ian Goodfellow

We then discuss the application of CGF to the following goals: finding numerical errors in trained neural networks, generating disagreements between neural networks and quantized versions of those networks, and surfacing undesirable behavior in character level language models.

Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer

7 code implementations ICLR 2019 David Berthelot, Colin Raffel, Aurko Roy, Ian Goodfellow

Autoencoders provide a powerful framework for learning compressed representations by encoding all of the information needed to reconstruct a data point in a latent code.

Motivating the Rules of the Game for Adversarial Example Research

no code implementations18 Jul 2018 Justin Gilmer, Ryan P. Adams, Ian Goodfellow, David Andersen, George E. Dahl

Advances in machine learning have led to broad deployment of systems with impressive performance on important problems.

Adversarial Reprogramming of Neural Networks

5 code implementations ICLR 2019 Gamaleldin F. Elsayed, Ian Goodfellow, Jascha Sohl-Dickstein

Previous adversarial attacks have been designed to degrade performance of models or cause machine learning models to produce specific outputs chosen ahead of time by the attacker.

General Classification

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

no code implementations11 Jun 2018 Ian Goodfellow

This article presents a summary of a keynote lecture at the Deep Learning Security workshop at IEEE Security and Privacy 2018.

Self-Attention Generative Adversarial Networks

44 code implementations arXiv 2018 Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena

In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks.

Conditional Image Generation

Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size

2 code implementations21 Apr 2018 Ian Goodfellow

In other words, the attack-based methodology provides an upper-bound on the size of a perturbation that will fool the model, but security guarantees require a lower bound.

Adversarial Attacks and Defences Competition

1 code implementation31 Mar 2018 Alexey Kurakin, Ian Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, Jian-Yu Wang, Zhishuai Zhang, Zhou Ren, Alan Yuille, Sangxia Huang, Yao Zhao, Yuzhe Zhao, Zhonglin Han, Junjiajia Long, Yerkebulan Berdibekov, Takuya Akiba, Seiya Tokui, Motoki Abe

To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them.

Adversarial Logit Pairing

3 code implementations NeurIPS 2018 Harini Kannan, Alexey Kurakin, Ian Goodfellow

In this paper, we develop improved techniques for defending against adversarial examples at scale.

Is Generator Conditioning Causally Related to GAN Performance?

no code implementations ICML 2018 Augustus Odena, Jacob Buckman, Catherine Olsson, Tom B. Brown, Christopher Olah, Colin Raffel, Ian Goodfellow

Motivated by this, we study the distribution of singular values of the Jacobian of the generator in Generative Adversarial Networks (GANs).

Adversarial Examples that Fool both Computer Vision and Time-Limited Humans

no code implementations NeurIPS 2018 Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian Goodfellow, Jascha Sohl-Dickstein

Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich.

Adversarial Spheres

2 code implementations ICLR 2018 Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian Goodfellow

We hypothesize that this counter intuitive behavior is a naturally occurring result of the high dimensional geometry of the data manifold.

Thermometer Encoding: One Hot Way To Resist Adversarial Examples

no code implementations ICLR 2018 Jacob Buckman, Aurko Roy, Colin Raffel, Ian Goodfellow

It is well known that it is possible to construct "adversarial examples" for neural networks: inputs which are misclassified by the network yet indistinguishable from true data.

MaskGAN: Better Text Generation via Filling in the _______

no code implementations ICLR 2018 William Fedus, Ian Goodfellow, Andrew M. Dai

Neural autoregressive and seq2seq models that generate text by sampling words sequentially, with each word conditioned on the previous model, are state-of-the-art for several machine translation and summarization benchmarks.

Image Generation Machine Translation +3

Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step

1 code implementation ICLR 2018 William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian Goodfellow

Unlike other generative models, the data distribution is learned via a game between a generator (the generative model) and a discriminator (a teacher providing training signal) that each minimize their own cost.

On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches

no code implementations26 Aug 2017 Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang

The recent, remarkable growth of machine learning has led to intense interest in the privacy of the data on which machine learning relies, and to new techniques for preserving privacy.

Ensemble Adversarial Training: Attacks and Defenses

11 code implementations ICLR 2018 Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel

We show that this form of adversarial training converges to a degenerate global minimum, wherein small curvature artifacts near the data points obfuscate a linear approximation of the loss.

The Space of Transferable Adversarial Examples

1 code implementation11 Apr 2017 Florian Tramèr, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel

Adversarial examples are maliciously perturbed inputs designed to mislead machine learning (ML) models at test-time.

Adversarial Attacks on Neural Network Policies

no code implementations8 Feb 2017 Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, Pieter Abbeel

Machine learning classifiers are known to be vulnerable to inputs maliciously constructed by adversaries to force misclassification.

NIPS 2016 Tutorial: Generative Adversarial Networks

22 code implementations31 Dec 2016 Ian Goodfellow

This report summarizes the tutorial presented by the author at NIPS 2016 on generative adversarial networks (GANs).

Adversarial Machine Learning at Scale

7 code implementations4 Nov 2016 Alexey Kurakin, Ian Goodfellow, Samy Bengio

Adversarial examples are malicious inputs designed to fool machine learning models.

Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

7 code implementations18 Oct 2016 Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, Kunal Talwar

The approach combines, in a black-box fashion, multiple models trained with disjoint datasets, such as records from different subsets of users.

Transfer Learning

Adversarial examples in the physical world

4 code implementations8 Jul 2016 Alexey Kurakin, Ian Goodfellow, Samy Bengio

Up to now, all previous work have assumed a threat model in which the adversary can feed data directly into the machine learning classifier.

Deep Learning with Differential Privacy

18 code implementations1 Jul 2016 Martín Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang

Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains.

Adversarial Training Methods for Semi-Supervised Text Classification

4 code implementations25 May 2016 Takeru Miyato, Andrew M. Dai, Ian Goodfellow

We extend adversarial and virtual adversarial training to the text domain by applying perturbations to the word embeddings in a recurrent neural network rather than to the original input itself.

General Classification Semi Supervised Text Classification +3

Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

no code implementations24 May 2016 Nicolas Papernot, Patrick McDaniel, Ian Goodfellow

We demonstrate our attacks on two commercial machine learning classification systems from Amazon (96. 19% misclassification rate) and Google (88. 94%) using only 800 queries of the victim model, thereby showing that existing machine learning approaches are in general vulnerable to systematic black-box attacks regardless of their structure.

Unsupervised Learning for Physical Interaction through Video Prediction

3 code implementations NeurIPS 2016 Chelsea Finn, Ian Goodfellow, Sergey Levine

A core challenge for an agent learning to interact with the world is to predict how its actions affect objects in its environment.

Video Prediction

Theano: A Python framework for fast computation of mathematical expressions

1 code implementation9 May 2016 The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, 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 Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, 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 Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher 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 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

Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.

Dimensionality Reduction General Classification

Improving the Robustness of Deep Neural Networks via Stability Training

no code implementations CVPR 2016 Stephan Zheng, Yang song, Thomas Leung, Ian Goodfellow

In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network.

General Classification

Practical Black-Box Attacks against Machine Learning

16 code implementations8 Feb 2016 Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami

Our attack strategy consists in training a local model to substitute for the target DNN, using inputs synthetically generated by an adversary and labeled by the target DNN.

Net2Net: Accelerating Learning via Knowledge Transfer

3 code implementations18 Nov 2015 Tianqi Chen, Ian Goodfellow, Jonathon Shlens

Our Net2Net technique accelerates the experimentation process by instantaneously transferring the knowledge from a previous network to each new deeper or wider network.

Transfer Learning

Adversarial Autoencoders

25 code implementations18 Nov 2015 Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, Brendan Frey

In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution.

Data Visualization Dimensionality Reduction +4

Efficient Per-Example Gradient Computations

3 code implementations7 Oct 2015 Ian Goodfellow

This technical report describes an efficient technique for computing the norm of the gradient of the loss function for a neural network with respect to its parameters.

Generative Adversarial Nets

1 code implementation NeurIPS 2014 Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio

We propose a new framework for estimating generative models via adversarial nets, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.

Intriguing properties of neural networks

10 code implementations21 Dec 2013 Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus

Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks.

Theano: new features and speed improvements

no code implementations23 Nov 2012 Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, James Bergstra, Ian Goodfellow, Arnaud Bergeron, Nicolas Bouchard, David Warde-Farley, Yoshua Bengio

Theano is a linear algebra compiler that optimizes a user's symbolically-specified mathematical computations to produce efficient low-level implementations.

Measuring Invariances in Deep Networks

no code implementations NeurIPS 2009 Ian Goodfellow, Honglak Lee, Quoc V. Le, Andrew Saxe, Andrew Y. Ng

Our evaluation metrics can also be used to evaluate future work in unsupervised deep learning, and thus help the development of future algorithms.

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