Publications related to Deep Learning for Localized-Haptic Feedback in Tactile Surfaces

From Classical to Unsupervised-Deep-Learning Methods for Solving Inverse Problems in Imaging.

In this thesis, we propose new algorithms to solve inverse problems in the context of biomedical images. Due to ill-posedness, solving these problems require some prior knowledge of the statistics of the underlying images. The traditional algorithms, in th ...

EPFL2020

Crowding and the Architecture of the Visual System

Adrien Christophe Doerig

Classically, vision is seen as a cascade of local, feedforward computations. This framework has been tremendously successful, inspiring a wide range of ground-breaking findings in neuroscience and computer vision. Recently, feedforward Convolutional Neural ...

EPFL2020

Learning How to Smile: Expression Video Generation With Conditional Adversarial Recurrent Nets

Wei Wang

While several research studies have focused on analyzing human behavior and, in particular, emotional signals from visual data, the problem of synthesizing face video sequences with specific attributes (e.g. age, facial expressions) received much less atte ...

2020

Optimization for Reinforcement Learning: From a single agent to cooperative agents

Volkan Cevher

Fueled by recent advances in deep neural networks, reinforcement learning (RL) has been in the limelight because of many recent breakthroughs in artificial intelligence, including defeating humans in games (e.g., chess, Go, StarCraft), self-driving cars, s ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Understanding and Improving Fast Adversarial Training

Nicolas Henri Bernard Flammarion, Maksym Andriushchenko

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 ...

2020

Generative adversarial training of product of policies for robust and adaptive movement primitives

Sylvain Calinon, Emmanuel Pignat, Hakan Girgin

In learning from demonstrations, many generative models of trajectories make simplifying assumptions of independence. Correctness is sacrificed in the name of tractability and speed of the learning phase. The ignored dependencies, which are often the kinem ...

2020

Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems

Negar Kiyavash

Nonconvex minimax problems appear frequently in emerging machine learning applications, such as generative adversarial networks and adversarial learning. Simple algorithms such as the gradient descent ascent (GDA) are the common practice for solving these ...

2020

Deepfake detection: humans vs. machines

Sébastien Marcel

Deepfake videos, where a person’s face is automatically swapped with a face of someone else, are becoming easier to generate with more realistic results. In response to the threat such manipulations can pose to our trust in video evidence, several large da ...

Idiap2020

Square Attack: a query-efficient black-box adversarial attack via random search

Nicolas Henri Bernard Flammarion, Maksym Andriushchenko, Francesco Croce

We propose the Square Attack, a new score-based black-box

l_2

and

l_\infty

adversarial attack that does not rely on local gradient information and thus is not affected by gradient masking. The Square Attack is based on a randomized search scheme where ...

2020

Shape Reconstruction by Learning Differentiable Surface Representations

Pascal Fua, Mathieu Salzmann, Shaifali Parashar, Jan Bednarík, Erhan Gündogdu

Generative models that produce point clouds have emerged as a powerful tool to represent 3D surfaces, and the best current ones rely on learning an ensemble of parametric representations. Unfortunately, they offer no control over the deformations of the su ...

2020

Deep Learning for Localized-Haptic Feedback in Tactile Surfaces

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From Classical to Unsupervised-Deep-Learning Methods for Solving Inverse Problems in Imaging.

Crowding and the Architecture of the Visual System

Learning How to Smile: Expression Video Generation With Conditional Adversarial Recurrent Nets

Optimization for Reinforcement Learning: From a single agent to cooperative agents

Understanding and Improving Fast Adversarial Training

Generative adversarial training of product of policies for robust and adaptive movement primitives

Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems

Deepfake detection: humans vs. machines

Square Attack: a query-efficient black-box adversarial attack via random search

Shape Reconstruction by Learning Differentiable Surface Representations

Optimization for Reinforcement Learning: From a single agent to cooperative agents

Learning How to Smile: Expression Video Generation With Conditional Adversarial Recurrent Nets

Crowding and the Architecture of the Visual System

Generative adversarial training of product of policies for robust and adaptive movement primitives

From Classical to Unsupervised-Deep-Learning Methods for Solving Inverse Problems in Imaging.

Understanding and Improving Fast Adversarial Training

Square Attack: a query-efficient black-box adversarial attack via random search

Shape Reconstruction by Learning Differentiable Surface Representations

Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems

Deepfake detection: humans vs. machines