Concept

Variational autoencoder

Related publications (161)

Learning and leveraging shared domain semantics to counteract visual domain shifts

One of the main limitations of artificial intelligence today is its inability to adapt to unforeseen circumstances. Machine Learning (ML), due to its data-driven nature, is particularly susceptible to this. ML relies on observations in order to learn impli ...

EPFL2020

Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Networks

Emmanuel Abbé

We derive generalization and excess risk bounds for neural networks using a family of complexity measures based on a multilevel relative entropy. The bounds are obtained by introducing the notion of generated hierarchical coverings of neural networks and b ...

MICROTOME PUBL2020

Context Mover's Distance & Barycenters: Optimal Transport of Contexts for Building Representations

Martin Jaggi, Aymeric Daphnis Kévin Dieuleveut, Sidak Pal Singh, Andreas Hug

We present a framework for building unsupervised representations of entities and their compositions, where each entity is viewed as a probability distribution rather than a vector embedding. In particular, this distribution is supported over the contexts w ...

ADDISON-WESLEY PUBL CO2020

MATHICSE Technical Report : Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines

Fabio Nobile, Panagiotis Tsilifis

The challenges for non-intrusive methods for Polynomial Chaos modeling lie in the computational efficiency and accuracy under a limited number of model simulations. These challenges can be addressed by enforcing sparsity in the series representation throug ...

MATHICSE2020

Sparse Polynomial Chaos expansions using variational relevance vector machines

Fabio Nobile, Panagiotis Tsilifis

2020

Online Robust Regression via SGD on the l1 loss

Nicolas Henri Bernard Flammarion, Scott William Pesme

We consider the robust linear regression problem in the online setting where we have access to the data in a streaming manner, one data point after the other. More specifically, for a true parameter , we consider the corrupted Gaussian linear model $ y=\la ...

2020

Modurec: Recommender Systems with Feature and Time Modulation

Pascal Frossard, Clément Arthur Yvon Vignac, Javier Alejandro Maroto Morales

Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data. These algorithms unfortunately do not make effective use of other features, which c ...

2020

Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

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

We introduce a variational framework to learn the activation functions of deep neural networks. Our aim is to increase the capacity of the network while controlling an upper-bound of the actual Lipschitz constant of the input-output relation. To that end, ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Variance-Reduced Stochastic Learning by Networked Agents Under Random Reshuffling

Ali H. Sayed, Bicheng Ying, Kun Yuan

This paper develops a distributed variance-reduced strategy for a collection of interacting agents that are connected by a graph topology. The resulting diffusion-AVRG (where AVRG stands for "amortized variance-reduced gradient") algorithm is shown to have ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Coherent rendering of virtual smile previews with fast neural style transfer

Valentin Vasiliu

Coherent rendering in augmented reality deals with synthesizing virtual content that seamlessly blends in with the real content. Unfortunately, capturing or modeling every real aspect in the virtual rendering process is often unfeasible or too expensive. W ...

IEEE2019

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