most citedSelf-Supervised Prototypical Transfer Learning for Few-Shot Classification

27 citations · 40 across the 3 of their papers we have counts for

collaborators

6 papers

cs.LG202027 cited

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

Carlos Medina, Arnout Devos, Matthias Grossglauser

Most approaches in few-shot learning rely on costly annotated data related to the goal task domain during (pre-)training. Recently, unsupervised meta-learning methods have exchange…

stat.ML20191 cited

A User Study of Perceived Carbon Footprint

Victor Kristof, Valentin Quelquejay-Leclère, Robin Zbinden +3

We propose a statistical model to understand people's perception of their carbon footprint. Driven by the observation that few people think of CO2 impact in absolute terms, we desi…

cs.LG201912 cited

Learning Hawkes Processes from a Handful of Events

Farnood Salehi, William Trouleau, Matthias Grossglauser +1

Learning the causal-interaction network of multivariate Hawkes processes is a useful task in many applications. Maximum-likelihood estimation is the most common approach to solve t…

cs.LG2019

Regression Networks for Meta-Learning Few-Shot Classification

Arnout Devos, Matthias Grossglauser

We propose regression networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number…

stat.ML2019

Scalable and Efficient Comparison-based Search without Features

Daniyar Chumbalov, Lucas Maystre, Matthias Grossglauser

We consider the problem of finding a target object using pairwise comparisons, by asking an oracle questions of the form \emph{"Which object from the pair is more simil…

stat.ML2019

Pairwise Comparisons with Flexible Time-Dynamics

Lucas Maystre, Victor Kristof, Matthias Grossglauser

Inspired by applications in sports where the skill of players or teams competing against each other varies over time, we propose a probabilistic model of pairwise-comparison outcom…