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10/22/2020
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
Convex relaxations have emerged as a promising approach for verifying de...
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03/04/2020
Creating High Resolution Images with a Latent Adversarial Generator
Generating realistic images is difficult, and many formulations for this...
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05/06/2019
MixMatch: A Holistic Approach to Semi-Supervised Learning
Semi-supervised learning has proven to be a powerful paradigm for levera...
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03/22/2019
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition
Adversarial examples are inputs to machine learning models designed by a...
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03/14/2019
A Research Agenda: Dynamic Models to Defend Against Correlated Attacks
In this article I describe a research agenda for securing machine learni...
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02/18/2019
On Evaluating Adversarial Robustness
Correctly evaluating defenses against adversarial examples has proven to...
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11/08/2018
New CleverHans Feature: Better Adversarial Robustness Evaluations with Attack Bundling
This technical report describes a new feature of the CleverHans library ...
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10/16/2018
Discriminator Rejection Sampling
We propose a rejection sampling scheme using the discriminator of a GAN ...
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10/08/2018
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values
Explaining the output of a complicated machine learning model like a dee...
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10/08/2018
Sanity Checks for Saliency Maps
Saliency methods have emerged as a popular tool to highlight features in...
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09/22/2018
Unrestricted Adversarial Examples
We introduce a two-player contest for evaluating the safety and robustne...
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08/14/2018
Skill Rating for Generative Models
We explore a new way to evaluate generative models using insights from e...
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07/28/2018
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
Machine learning models are notoriously difficult to interpret and debug...
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07/19/2018
Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer
Autoencoders provide a powerful framework for learning compressed repres...
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07/18/2018
Motivating the Rules of the Game for Adversarial Example Research
Advances in machine learning have led to broad deployment of systems wit...
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06/28/2018
Adversarial Reprogramming of Neural Networks
Deep neural networks are susceptible to adversarial attacks. In computer...
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06/11/2018
Defense Against the Dark Arts: An overview of adversarial example security research and future research directions
This article presents a summary of a keynote lecture at the Deep Learnin...
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05/21/2018
Self-Attention Generative Adversarial Networks
In this paper, we propose the Self-Attention Generative Adversarial Netw...
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04/21/2018
Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size
A key problem in research on adversarial examples is that vulnerability ...
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03/31/2018
Adversarial Attacks and Defences Competition
To accelerate research on adversarial examples and robustness of machine...
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03/16/2018
Adversarial Logit Pairing
In this paper, we develop improved techniques for defending against adve...
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02/23/2018
Is Generator Conditioning Causally Related to GAN Performance?
Recent work (Pennington et al, 2017) suggests that controlling the entir...
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02/22/2018
Adversarial Examples that Fool both Human and Computer Vision
Machine learning models are vulnerable to adversarial examples: small ch...
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01/23/2018
MaskGAN: Better Text Generation via Filling in the ______
Neural text generation models are often autoregressive language models o...
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01/09/2018
Adversarial Spheres
State of the art computer vision models have been shown to be vulnerable...
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10/23/2017
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
Generative adversarial networks (GANs) are a family of generative models...
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08/26/2017
On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches
The recent, remarkable growth of machine learning has led to intense int...
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04/11/2017
The Space of Transferable Adversarial Examples
Adversarial examples are maliciously perturbed inputs designed to mislea...
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02/08/2017
Adversarial Attacks on Neural Network Policies
Machine learning classifiers are known to be vulnerable to inputs malici...
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12/31/2016
NIPS 2016 Tutorial: Generative Adversarial Networks
This report summarizes the tutorial presented by the author at NIPS 2016...
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11/04/2016
Adversarial Machine Learning at Scale
Adversarial examples are malicious inputs designed to fool machine learn...
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10/18/2016
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
Some machine learning applications involve training data that is sensiti...
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10/03/2016
cleverhans v2.0.0: an adversarial machine learning library
cleverhans is a software library that provides standardized reference im...
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07/08/2016
Adversarial examples in the physical world
Most existing machine learning classifiers are highly vulnerable to adve...
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07/01/2016
Deep Learning with Differential Privacy
Machine learning techniques based on neural networks are achieving remar...
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06/10/2016
Improved Techniques for Training GANs
We present a variety of new architectural features and training procedur...
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05/25/2016
Adversarial Training Methods for Semi-Supervised Text Classification
Adversarial training provides a means of regularizing supervised learnin...
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05/24/2016
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Many machine learning models are vulnerable to adversarial examples: inp...
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05/23/2016
Unsupervised Learning for Physical Interaction through Video Prediction
A core challenge for an agent learning to interact with the world is to ...
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05/09/2016
Theano: A Python framework for fast computation of mathematical expressions
Theano is a Python library that allows to define, optimize, and evaluate...
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04/15/2016
Improving the Robustness of Deep Neural Networks via Stability Training
In this paper we address the issue of output instability of deep neural ...
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03/14/2016
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TensorFlow is an interface for expressing machine learning algorithms, a...
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02/08/2016
Practical Black-Box Attacks against Machine Learning
Machine learning (ML) models, e.g., deep neural networks (DNNs), are vul...
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11/18/2015
Adversarial Autoencoders
In this paper, we propose the "adversarial autoencoder" (AAE), which is ...
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11/18/2015
Net2Net: Accelerating Learning via Knowledge Transfer
We introduce techniques for rapidly transferring the information stored ...
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10/07/2015
Efficient Per-Example Gradient Computations
This technical report describes an efficient technique for computing the...
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12/21/2013
Intriguing properties of neural networks
Deep neural networks are highly expressive models that have recently ach...
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12/12/2012
Joint Training of Deep Boltzmann Machines
We introduce a new method for training deep Boltzmann machines jointly. ...
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11/23/2012
Theano: new features and speed improvements
Theano is a linear algebra compiler that optimizes a user's symbolically...
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06/27/2012
