298 citations · 626 across the 17 of their papers we have counts for
20 papers
LRVS-Fashion: Extending Visual Search with Referring Instructions
Simon Lepage, Jérémie Mary, David Picard
This paper introduces a new challenge for image similarity search in the context of fashion, addressing the inherent ambiguity in this domain stemming from complex images. We prese…
Unveiling the Latent Space Geometry of Push-Forward Generative Models
Thibaut Issenhuth, Ugo Tanielian, Jérémie Mary +1
Many deep generative models are defined as a push-forward of a Gaussian measure by a continuous generator, such as Generative Adversarial Networks (GANs) or Variational Auto-Encode…
Lessons from the AdKDD'21 Privacy-Preserving ML Challenge
Eustache Diemert, Romain Fabre, Alexandre Gilotte +6
Designing data sharing mechanisms providing performance and strong privacy guarantees is a hot topic for the Online Advertising industry. Namely, a prominent proposal discussed und…
EdiBERT, a generative model for image editing
Thibaut Issenhuth, Ugo Tanielian, Jérémie Mary +1
Advances in computer vision are pushing the limits of im-age manipulation, with generative models sampling detailed images on various tasks. However, a specialized model is often d…
Latent reweighting, an almost free improvement for GANs
Thibaut Issenhuth, Ugo Tanielian, David Picard +1
Standard formulations of GANs, where a continuous function deforms a connected latent space, have been shown to be misspecified when fitting different classes of images. In particu…
Do Not Mask What You Do Not Need to Mask: a Parser-Free Virtual Try-On
Thibaut Issenhuth, Jérémie Mary, Clément Calauzènes
The 2D virtual try-on task has recently attracted a great interest from the research community, for its direct potential applications in online shopping as well as for its inherent…
Learning disconnected manifolds: a no GANs land
Ugo Tanielian, Thibaut Issenhuth, Elvis Dohmatob +1
Typical architectures of Generative AdversarialNetworks make use of a unimodal latent distribution transformed by a continuous generator. Consequently, the modeled distribution alw…
End-to-End Learning of Geometric Deformations of Feature Maps for Virtual Try-On
Thibaut Issenhuth, Jérémie Mary, Clément Calauzènes
The 2D virtual try-on task has recently attracted a lot of interest from the research community, for its direct potential applications in online shopping as well as for its inheren…
Distributionally Robust Reinforcement Learning
Elena Smirnova, Elvis Dohmatob, Jérémie Mary
Real-world applications require RL algorithms to act safely. During learning process, it is likely that the agent executes sub-optimal actions that may lead to unsafe/poor states o…
Visual Reasoning with Multi-hop Feature Modulation
Florian Strub, Mathieu Seurin, Ethan Perez +5
Recent breakthroughs in computer vision and natural language processing have spurred interest in challenging multi-modal tasks such as visual question-answering and visual dialogue…
Recurrent Neural Networks for Long and Short-Term Sequential Recommendation
Kiewan Villatel, Elena Smirnova, Jérémie Mary +1
Recommender systems objectives can be broadly characterized as modeling user preferences over short-or long-term time horizon. A large body of previous research studied long-term r…
Multi-Task Determinantal Point Processes for Recommendation
Romain Warlop, Jérémie Mary, Mike Gartrell
Determinantal point processes (DPPs) have received significant attention in the recent years as an elegant model for a variety of machine learning tasks, due to their ability to el…
Modulating early visual processing by language
Harm de Vries, Florian Strub, Jérémie Mary +3
It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected. This view dominates the current literature in comput…
End-to-end optimization of goal-driven and visually grounded dialogue systems
Florian Strub, Harm de Vries, Jeremie Mary +3
End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architectures for sequence-to-sequence learning.…
Hybrid Recommender System based on Autoencoders
Florian Strub, Romaric Gaudel, Jérémie Mary
A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratin…
Hybrid Collaborative Filtering with Autoencoders
Florian Strub, Jeremie Mary, Romaric Gaudel
Collaborative Filtering aims at exploiting the feedback of users to provide personalised recommendations. Such algorithms look for latent variables in a large sparse matrix of rati…
Bandits Warm-up Cold Recommender Systems
Jérémie Mary, Romaric Gaudel, Preux Philippe
We address the cold start problem in recommendation systems assuming no contextual information is available neither about users, nor items. We consider the case in which we only ha…
Cold-start Problems in Recommendation Systems via Contextual-bandit Algorithms
Hai Thanh Nguyen, Jérémie Mary, Philippe Preux
In this paper, we study a cold-start problem in recommendation systems where we have completely new users entered the systems. There is not any interaction or feedback of the new u…
Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques
Olivier Nicol, Jérémie Mary, Philippe Preux
In many recommendation applications such as news recommendation, the items that can be rec- ommended come and go at a very fast pace. This is a challenge for recommender systems (R…
Reducing statistical time-series problems to binary classification
Daniil Ryabko, Jérémie Mary
We show how binary classification methods developed to work on i.i.d. data can be used for solving statistical problems that are seemingly unrelated to classification and concern h…