Francesco Locatello

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Max Planck Society ∙ ETH Zurich

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. ∙ 3 ∙ 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

Anirudh Goyal

Stefan Bauer

Michael Tschannen

Gunnar Rätsch

Mario Lucic

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