Wasserstein Adversarial Regularization for learning with label noise

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This paper introduces a new extragradient-type algorithm for a class of nonconvex-nonconcave minimax problems. It is well-known that finding a local solution for general minimax problems is computationally intractable. This observation has recently motivat ...

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A recent line of work focused on making adversarial training computationally efficient for deep learning models. In particular, Wong et al. (2020) showed that ℓ∞-adversarial training with fast gradient sign method (FGSM) can fail due to a phenomenon called ...

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Optical diffraction tomography is an effective tool to estimate the refractive indices of unknown objects. It proceeds by solving an ill-posed inverse problem for which the wave equation governs the scattering events. The solution has traditionally been de ...

Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds. Despite promising progress, existing representations learned with neural networks sti ...

The large capacity of neural networks enables them to learn complex functions. To avoid overfitting, networks however require a lot of training data that can be expensive and time-consuming to collect. A common practical approach to attenuate overfitting i ...

Wasserstein Adversarial Regularization for learning with label noise

Chattez avec Graph Search

Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems

On the robustness of randomized classifiers to adversarial examples

Deep learning-based galaxy image deconvolution

Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides

Adversarial training with informed data selection

Large Steps in Inverse Rendering of Geometry

Understanding and Improving Fast Adversarial Training

Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search

Disparity Between Batches as a Signal for Early Stopping

Deep-learning projector for optical diffraction tomography

Social NCE: Contrastive Learning of Socially-aware Motion Representations

AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks