Francesco Locatello

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Max Planck Society ∙ ETH Zurich

On the Transfer of Disentangled Representations in Realistic Settings

Learning meaningful representations that disentangle the underlying stru...
10/27/2020 ∙ by Andrea Dittadi, et al. ∙ 24 ∙ share

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A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation

The idea behind the unsupervised learning of disentangled representation...
10/27/2020 ∙ by Francesco Locatello, et al. ∙ 27 ∙ share

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A Commentary on the Unsupervised Learning of Disentangled Representations

The goal of the unsupervised learning of disentangled representations is...
07/28/2020 ∙ by Francesco Locatello, et al. ∙ 15 ∙ share

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Is Independence all you need? On the Generalization of Representations Learned from Correlated Data

Despite impressive progress in the last decade, it still remains an open...
06/14/2020 ∙ by Frederik Träuble, et al. ∙ 14 ∙ share

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Weakly-Supervised Disentanglement Without Compromises

Intelligent agents should be able to learn useful representations by obs...
02/07/2020 ∙ by Francesco Locatello, et al. ∙ 20 ∙ share

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On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

Learning meaningful and compact representations with structurally disent...
06/07/2019 ∙ by Muhammad Waleed Gondal, et al. ∙ 4 ∙ share

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On the Fairness of Disentangled Representations

Recently there has been a significant interest in learning disentangled ...
05/31/2019 ∙ by Francesco Locatello, et al. ∙ 6 ∙ share

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Are Disentangled Representations Helpful for Abstract Visual Reasoning?

A disentangled representation encodes information about the salient fact...
05/29/2019 ∙ by Sjoerd van Steenkiste, et al. ∙ 3 ∙ share

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The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA

We consider the problem of recovering a common latent source with indepe...
05/16/2019 ∙ by Luigi Gresele, et al. ∙ 9 ∙ share

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Disentangling Factors of Variation Using Few Labels

Learning disentangled representations is considered a cornerstone proble...
05/03/2019 ∙ by Francesco Locatello, et al. ∙ 12 ∙ share

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Stochastic Conditional Gradient Method for Composite Convex Minimization

In this paper, we propose the first practical algorithm to minimize stoc...
01/29/2019 ∙ by Francesco Locatello, et al. ∙ 0 ∙ share

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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

In recent years, the interest in unsupervised learning of disentangled r...
11/29/2018 ∙ by Francesco Locatello, et al. ∙ 4 ∙ share

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Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series

Human professionals are often required to make decisions based on comple...
06/06/2018 ∙ by Vincent Fortuin, et al. ∙ 0 ∙ share

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Boosting Black Box Variational Inference

Approximating a probability density in a tractable manner is a central t...
06/06/2018 ∙ by Francesco Locatello, et al. ∙ 0 ∙ share

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Clustering Meets Implicit Generative Models

Clustering is a cornerstone of unsupervised learning which can be though...
04/30/2018 ∙ by Francesco Locatello, et al. ∙ 0 ∙ share

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Revisiting First-Order Convex Optimization Over Linear Spaces

Two popular examples of first-order optimization methods over linear spa...
03/26/2018 ∙ by Francesco Locatello, et al. ∙ 0 ∙ share

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Boosting Variational Inference: an Optimization Perspective

Variational Inference is a popular technique to approximate a possibly i...
08/05/2017 ∙ by Francesco Locatello, et al. ∙ 0 ∙ share

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Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees

Greedy optimization methods such as Matching Pursuit (MP) and Frank-Wolf...
05/31/2017 ∙ by Francesco Locatello, et al. ∙ 0 ∙ share

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A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe

Two of the most fundamental prototypes of greedy optimization are the ma...
02/21/2017 ∙ by Francesco Locatello, et al. ∙ 0 ∙ share

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Francesco Locatello

Featured Co-authors

Bernhard Schölkopf

Volkan Cevher

Martin Jaggi

Jürgen Schmidhuber

Joydeep Ghosh

Sylvain Gelly

Stefan Bauer

Anirudh Goyal

Mario Lucic

Gunnar Rätsch

Michael Tschannen

On the Transfer of Disentangled Representations in Realistic Settings

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation

A Commentary on the Unsupervised Learning of Disentangled Representations

Is Independence all you need? On the Generalization of Representations Learned from Correlated Data

Weakly-Supervised Disentanglement Without Compromises

On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

On the Fairness of Disentangled Representations

Are Disentangled Representations Helpful for Abstract Visual Reasoning?

The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA

Disentangling Factors of Variation Using Few Labels

Stochastic Conditional Gradient Method for Composite Convex Minimization

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series

Boosting Black Box Variational Inference

Clustering Meets Implicit Generative Models

Revisiting First-Order Convex Optimization Over Linear Spaces

Boosting Variational Inference: an Optimization Perspective

Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees

A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe