Publications associées à Théorème d'approximation universelle

Multilayer Graph Clustering With Optimized Node Embedding

We are interested in multilayer graph clustering, which aims at dividing the graph nodes into categories or communities. To do so, we propose to learn a clustering-friendly embedding of the graph nodes by solving an optimization problem that involves a fid ...

IEEE2021

ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

Deep neural networks (DNNs) have surpassed human-level accuracy in a variety of cognitive tasks but at the cost of significant memory/time requirements in DNN training. This limits their deployment in energy and memory limited applications that require rea ...

IEEE2020

Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures

Florent Gérard Krzakala, Julien Marcel Daniel Emmanuel Launay

Despite being the workhorse of deep learning, the backpropagation algorithm is no panacea. It enforces sequential layer updates, thus preventing efficient parallelization of the training process. Furthermore, its biological plausibility is being challenged ...

Curran Associates, Inc.2020

Learning Activation Functions in Deep (Spline) Neural Networks

Michaël Unser, Pakshal Narendra Bohra, Shayan Aziznejad, Harshit Gupta, Joaquim Gonçalves Garcia Barreto Campos

We develop an efficient computational solution to train deep neural networks (DNN) with free-form activation functions. To make the problem well-posed, we augment the cost functional of the DNN by adding an appropriate shape regularization: the sum of the ...

2020

Rapid Recalibration of Peri-Personal Space: Psychophysical, Electrophysiological, and Neural Network Modeling Evidence

Olaf Blanke, Bruno Herbelin, Andrea Serino, Jean-Paul Noel, Elisa Pellencin, Tommaso Enrico Bertoni

Interactions between individuals and the environment occur within the peri-personal space (PPS). The encoding of this space plastically adapts to bodily constraints and stimuli features. However, these remapping effects have not been demonstrated on an ada ...

2020

Fast and Provable ADMM for Learning with Generative Priors

Volkan Cevher, Armin Eftekhari, Fabian Ricardo Latorre Gomez

In this work, we propose a (linearized) Alternating Direction Method-of-Multipliers (ADMM) algorithm for minimizing a convex function subject to a nonconvex constraint. We focus on the special case where such constraint arises from the specification that a ...

2019

Deep Spline Networks With Control Of Lipschitz Regularity

Michaël Unser, Shayan Aziznejad

The motivation for this work is to improve the performance of deep neural networks through the optimization of the individual activation functions. Since the latter results in an infinite-dimensional optimization problem, we resolve the ambiguity by search ...

IEEE2019

Learning Trajectory Dependencies for Human Motion Prediction

Mathieu Salzmann, Hongwei Li, Wei Mao

Human motion prediction, i.e., forecasting future body poses given observed pose sequence, has typically been tackled with recurrent neural networks (RNNs). However, as evidenced by prior work, the resulted RNN models suffer from prediction errors accumula ...

IEEE2019

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Théorème d'approximation universelle

Deep Boltzmann Machines: Rigorous Results at Arbitrary Depth

Multilayer Graph Clustering With Optimized Node Embedding

ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning

The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models

Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures

Learning Activation Functions in Deep (Spline) Neural Networks

Rapid Recalibration of Peri-Personal Space: Psychophysical, Electrophysiological, and Neural Network Modeling Evidence

Fast and Provable ADMM for Learning with Generative Priors

Deep Spline Networks With Control Of Lipschitz Regularity

Learning Trajectory Dependencies for Human Motion Prediction

Graph Chatbot

Chattez avec Graph Search

Rapid Recalibration of Peri-Personal Space: Psychophysical, Electrophysiological, and Neural Network Modeling Evidence

ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning

Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures

Multilayer Graph Clustering With Optimized Node Embedding

Deep Boltzmann Machines: Rigorous Results at Arbitrary Depth

Learning Trajectory Dependencies for Human Motion Prediction

Fast and Provable ADMM for Learning with Generative Priors

The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models

Deep Spline Networks With Control Of Lipschitz Regularity

Learning Activation Functions in Deep (Spline) Neural Networks