136 citations · 243 across the 10 of their papers we have counts for
11 papers
Implicit Regularization with Polynomial Growth in Deep Tensor Factorization
Kais Hariz, Hachem Kadri, Stéphane Ayache +2
We study the implicit regularization effects of deep learning in tensor factorization. While implicit regularization in deep matrix and 'shallow' tensor factorization via linear an…
Implicit Regularization in Deep Tensor Factorization
Paolo Milanesi, Hachem Kadri, Stéphane Ayache +1
Attempts of studying implicit regularization associated to gradient descent (GD) have identified matrix completion as a suitable test-bed. Late findings suggest that this phenomeno…
Mapping individual differences in cortical architecture using multi-view representation learning
Akrem Sellami, François-Xavier Dupé, Bastien Cagna +4
In neuroscience, understanding inter-individual differences has recently emerged as a major challenge, for which functional magnetic resonance imaging (fMRI) has proven invaluable.…
Deep Networks with Adaptive Nyström Approximation
Luc Giffon, Stéphane Ayache, Thierry Artières +1
Recent work has focused on combining kernel methods and deep learning to exploit the best of the two approaches. Here, we introduce a new architecture of neural networks in which w…
Unsupervised Object Segmentation by Redrawing
Mickaël Chen, Thierry Artières, Ludovic Denoyer
Object segmentation is a crucial problem that is usually solved by using supervised learning approaches over very large datasets composed of both images and corresponding object ma…
Multi-View Data Generation Without View Supervision
Mickaël Chen, Ludovic Denoyer, Thierry Artières
The development of high-dimensional generative models has recently gained a great surge of interest with the introduction of variational auto-encoders and generative adversarial ne…
A Meta-Learning Approach to One-Step Active Learning
Gabriella Contardo, Ludovic Denoyer, Thierry Artieres
We consider the problem of learning when obtaining the training labels is costly, which is usually tackled in the literature using active-learning techniques. These approaches prov…
Sequential Cost-Sensitive Feature Acquisition
Gabriella Contardo, Ludovic Denoyer, Thierry Artières
We propose a reinforcement learning based approach to tackle the cost-sensitive learning problem where each input feature has a specific cost. The acquisition process is handled th…
LSHTC: A Benchmark for Large-Scale Text Classification
Ioannis Partalas, Aris Kosmopoulos, Nicolas Baskiotis +6
LSHTC is a series of challenges which aims to assess the performance of classification systems in large-scale classification in a a large number of classes (up to hundreds of thous…
Representation Learning for cold-start recommendation
Gabriella Contardo, Ludovic Denoyer, Thierry Artieres
A standard approach to Collaborative Filtering (CF), i.e. prediction of user ratings on items, relies on Matrix Factorization techniques. Representations for both users and items a…
Learning States Representations in POMDP
Gabriella Contardo, Ludovic Denoyer, Thierry Artieres +1
We propose to deal with sequential processes where only partial observations are available by learning a latent representation space on which policies may be accurately learned.