no code implementations • 25 Apr 2024 • Nathanaël Perraudin, Adrien Teutrie, Cécile Hébert, Guillaume Obozinski
We consider the problem of regularized Poisson Non-negative Matrix Factorization (NMF) problem, encompassing various regularization terms such as Lipschitz and relatively smooth functions, alongside linear constraints.
no code implementations • 20 Feb 2023 • Stefania Russo, Nathanaël Perraudin, Steven Stalder, Fernando Perez-Cruz, Joao Paulo Leitao, Guillaume Obozinski, Jan Dirk Wegner
In this technical report we compare different deep learning models for prediction of water depth rasters at high spatial resolution.
1 code implementation • 11 Jan 2023 • Paola Malsot, Filipe Martins, Didier Trono, Guillaume Obozinski
On those real tasks, optirank performs at least as well as the vanilla logistic regression on classical ranks, while producing sparser solutions.
1 code implementation • 9 Dec 2022 • Enikő Székely, Sebastian Sippel, Nicolai Meinshausen, Guillaume Obozinski, Reto Knutti
Fingerprints are key tools in climate change detection and attribution (D&A) that are used to determine whether changes in observations are different from internal climate variability (detection), and whether observed changes can be assigned to specific external drivers (attribution).
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.
Ranked #13 on
Few-Shot Image Classification
on CIFAR-FS 5-way (1-shot)
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.
Ranked #1 on
Link Prediction
on YAGO15k
no code implementations • 25 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.
no code implementations • 20 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.
3 code implementations • ICML 2018 • Timothée Lacroix, Nicolas Usunier, Guillaume Obozinski
The problem of Knowledge Base Completion can be framed as a 3rd-order binary tensor completion problem.
Ranked #1 on
Link Prediction
on FB15k
no code implementations • CVPR 2015 • Mateusz Kozinski, Raghudeep Gadde, Sergey Zagoruyko, Guillaume Obozinski, Renaud Marlet
We present a new shape prior formalism for segmentation of rectified facade images.
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.
no code implementations • 14 Dec 2013 • Edouard Grave, Guillaume Obozinski, Francis Bach
Most natural language processing systems based on machine learning are not robust to domain shift.
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.
no code implementations • 8 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.