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Found 21 papers, 6 papers with code
Are Labels Required for Improving Adversarial Robustness?
Recent work has uncovered the interesting (and somewhat surprising) finding that training models to be invariant to adversarial perturbations requires substantially larger datasets than those required for standard classification.
Discovering faster matrix multiplication algorithms with reinforcement learning
Particularly relevant is the case of 4 × 4 matrices in a finite field, where AlphaTensor’s algorithm improves on Strassen’s two-level algorithm for the first time, to our knowledge, since its discovery 50 years ago2.
DeepFool: a simple and accurate method to fool deep neural networks
State-of-the-art deep neural networks have achieved impressive results on many image classification tasks.
Universal adversarial perturbations
Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability.
Robustness via curvature regularization, and vice versa
State-of-the-art classifiers have been shown to be largely vulnerable to adversarial perturbations.
SaaS: Speed as a Supervisor for Semi-supervised Learning
We introduce the SaaS Algorithm for semi-supervised learning, which uses learning speed during stochastic gradient descent in a deep neural network to measure the quality of an iterative estimate of the posterior probability of unknown labels.
Adversarial vulnerability for any classifier
Despite achieving impressive performance, state-of-the-art classifiers remain highly vulnerable to small, imperceptible, adversarial perturbations.
Robustness of classifiers to uniform $\ell\_p$ and Gaussian noise
We study the robustness of classifiers to various kinds of random noise models.
Robustness of classifiers to universal perturbations: a geometric perspective
Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers.
Classification regions of deep neural networks
The goal of this paper is to analyze the geometric properties of deep neural network classifiers in the input space.
Robustness of classifiers: from adversarial to random noise
Moreover, we quantify the robustness of classifiers in terms of the subspace dimension in the semi-random noise regime, and show that our bounds remarkably interpolate between the worst-case and random noise regimes.
Analysis of classifiers' robustness to adversarial perturbations
To the best of our knowledge, our results provide the first theoretical work that addresses the phenomenon of adversarial instability recently observed for deep networks.
Manitest: Are classifiers really invariant?
Invariance to geometric transformations is a highly desirable property of automatic classifiers in many image recognition tasks.
Multi-task additive models with shared transfer functions based on dictionary learning
Additive models form a widely popular class of regression models which represent the relation between covariates and response variables as the sum of low-dimensional transfer functions.
Dictionary learning for fast classification based on soft-thresholding
The dictionary learning problem, which jointly learns the dictionary and linear classifier, is cast as a difference of convex (DC) program and solved efficiently with an iterative DC solver.
Image registration with sparse approximations in parametric dictionaries
We examine in this paper the problem of image registration from the new perspective where images are given by sparse approximations in parametric dictionaries of geometric functions.
Verification of deep probabilistic models
For example, a machine translation model should produce semantically equivalent outputs for innocuous changes in the input to the model.
Empirical Study of the Topology and Geometry of Deep Networks
We specifically study the topology of classification regions created by deep networks, as well as their associated decision boundary.
Learning dynamic polynomial proofs
In this work, we introduce a machine learning based method to search for a dynamic proof within these proof systems.
Adversarial Robustness through Local Linearization
Using this regularizer, we exceed current state of the art and achieve 47% adversarial accuracy for ImageNet with l-infinity adversarial perturbations of radius 4/255 under an untargeted, strong, white-box attack.
Quantum Circuit Optimization with AlphaTensor
A key challenge in realizing fault-tolerant quantum computers is circuit optimization.
