no code implementations • 1 Feb 2024 • David Emukpere, Bingbing Wu, Julien Perez, Jean-Michel Renders
As it requires impacting a possibly large set of degrees of freedom composing the environment, mutual information maximization fails alone in producing useful and safe manipulation behaviors.
no code implementations • 2 Aug 2023 • Michel Aractingi, Pierre-Alexandre Léziart, Thomas Flayols, Julien Perez, Tomi Silander, Philippe Souères
We detail the learning procedure and method for transfer on the real robot.
no code implementations • 19 Jun 2023 • Julien Perez, Denys Proux, Claude Roux, Michael Niemaz
To leverage reinforcement learning with text-based task descriptions, we need to produce reward functions associated with individual tasks in a scalable manner.
no code implementations • EACL 2021 • Quentin Grail, Julien Perez, Eric Gaussier
Fine-tuning a large language model on downstream tasks has become a commonly adopted process in the Natural Language Processing (NLP) (CITATION).
no code implementations • ECCV 2020 • Mert Bulent Sariyildiz, Julien Perez, Diane Larlus
Starting from the observation that captioned images are easily crawlable, we argue that this overlooked source of information can be exploited to supervise the training of visual representations.
no code implementations • ICLR 2020 • Michel Aractingi, Christopher Dance, Julien Perez, Tomi Silander
The results of this method, called invariance regularization, show an improvement in the generalization of policies to environments not seen during training.
no code implementations • 25 Sep 2019 • Quentin Grail, Julien Perez, Eric Gaussier
The purpose of the reading module is to produce a question-aware representation of the document.
no code implementations • 11 Jan 2019 • Koichiro Yoshino, Chiori Hori, Julien Perez, Luis Fernando D'Haro, Lazaros Polymenakos, Chulaka Gunasekara, Walter S. Lasecki, Jonathan K. Kummerfeld, Michel Galley, Chris Brockett, Jianfeng Gao, Bill Dolan, Xiang Gao, Huda Alamari, Tim K. Marks, Devi Parikh, Dhruv Batra
This paper introduces the Seventh Dialog System Technology Challenges (DSTC), which use shared datasets to explore the problem of building dialog systems.
no code implementations • 29 Oct 2018 • Quentin Grail, Julien Perez
To motivate this purpose, we present ReviewQA, a question-answering dataset based on hotel reviews.
no code implementations • 27 Sep 2018 • Morgan Funtowicz, Tomi Silander, Arnaud Sors, Julien Perez
More precisely, our forward model is trained to produce realistic observations of the future while a discriminator model is trained to distinguish between real images and the model’s prediction of the future.
no code implementations • ICLR 2018 • Quentin Grail, Julien Perez
In this paper we explore the paradigm of adversarial learning and self-play for the task of machine reading comprehension.
no code implementations • ICLR 2018 • julien perez, Tomi Silander
In this paper, we propose to introduce the paradigm of contextual bandits as framework for pro-active dialog systems.
no code implementations • 31 May 2017 • Julien Perez, Tomi Silander
In this paper, we explore the use of a recently proposed attention-based model, the Gated End-to-End Memory Network, for sequential control.
no code implementations • WS 2016 • Fei Liu, Julien Perez, Scott Nowson
Many methods have been used to recognise author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e. g. linear regression or Support Vector Machines.
no code implementations • EACL 2017 • Fei Liu, Julien Perez, Scott Nowson
Many methods have been used to recognize author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e. g. linear regression or Support Vector Machines.
1 code implementation • EACL 2017 • Julien Perez, Fei Liu
Our experiments show significant improvements on the most challenging tasks in the 20 bAbI dataset, without the use of any domain knowledge.
no code implementations • 16 Jun 2016 • Julien Perez
Finally, we show that the prediction schema is computationally efficient in comparison to the previous approaches.
no code implementations • EACL 2017 • Julien Perez, Fei Liu
In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate a compact representation of the current dialog status from a sequence of noisy observations produced by the speech recognition and the natural language understanding modules.
no code implementations • 30 Jun 2015 • Guillaume Bouchard, Théo Trouillon, Julien Perez, Adrien Gaidon
Stochastic Gradient Descent (SGD) is one of the most widely used techniques for online optimization in machine learning.