Search Results for author: Guillaume Obozinski

Found 12 papers, 3 papers with code

Empirical Bayes Transductive Meta-Learning with Synthetic Gradients

2 code implementations ICLR 2020 Shell Xu Hu, Pablo G. Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil D. Lawrence, Andreas Damianou

The evidence lower bound of the marginal log-likelihood of empirical Bayes decomposes as a sum of local KL divergences between the variational posterior and the true posterior on the query set of each task.

Few-Shot Image Classification Meta-Learning +3

Tensor Decompositions for temporal knowledge base completion

2 code implementations ICLR 2020 Timothée Lacroix, Guillaume Obozinski, Nicolas Usunier

Additionally, we propose a new dataset for knowledge base completion constructed from Wikidata, larger than previous benchmarks by an order of magnitude, as a new reference for evaluating temporal and non-temporal link prediction methods.

Knowledge Base Completion Link Prediction +2

Projected Canonical Decomposition for Knowledge Base Completion

no code implementations25 Sep 2019 Timothée Lacroix, Guillaume Obozinski, Joan Bruna, Nicolas Usunier

However, as we show in this paper through experiments on standard benchmarks of link prediction in knowledge bases, ComplEx, a variant of CP, achieves similar performances to recent approaches based on Tucker decomposition on all operating points in terms of number of parameters.

Knowledge Base Completion Link Prediction

Learning the effect of latent variables in Gaussian Graphical models with unobserved variables

no code implementations20 Jul 2018 Marina Vinyes, Guillaume Obozinski

In this work, we consider a family of latent variable Gaussian graphical models in which the graph of the joint distribution between observed and unobserved variables is sparse, and the unobserved variables are conditionally independent given the others.

Tight convex relaxations for sparse matrix factorization

no code implementations NeurIPS 2014 Emile Richard, Guillaume Obozinski, Jean-Philippe Vert

Based on a new atomic norm, we propose a new convex formulation for sparse matrix factorization problems in which the number of nonzero elements of the factors is assumed fixed and known.

Learning and Calibrating Per-Location Classifiers for Visual Place Recognition

no code implementations CVPR 2013 Petr Gronat, Guillaume Obozinski, Josef Sivic, Tomas Pajdla

The aim of this work is to localize a query photograph by finding other images depicting the same place in a large geotagged image database.

Object Recognition Two-sample testing +1

Structured Sparse Principal Component Analysis

no code implementations8 Sep 2009 Rodolphe Jenatton, Guillaume Obozinski, Francis Bach

We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespecified set of shapes.

Dictionary Learning Face Recognition

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