95 citations · 265 across the 6 of their papers we have counts for
8 papers
DiPaCo: Distributed Path Composition
Arthur Douillard, Qixuan Feng, Andrei A. Rusu +7
Progress in machine learning (ML) has been fueled by scaling neural network models. This scaling has been enabled by ever more heroic feats of engineering, necessary for accommodat…
Towards Robust and Efficient Continual Language Learning
Adam Fisch, Amal Rannen-Triki, Razvan Pascanu +4
As the application space of language models continues to evolve, a natural question to ask is how we can quickly adapt models to new tasks. We approach this classic question from a…
Towards Compute-Optimal Transfer Learning
Massimo Caccia, Alexandre Galashov, Arthur Douillard +6
The field of transfer learning is undergoing a significant shift with the introduction of large pretrained models which have demonstrated strong adaptability to a variety of downst…
Learning through Dialogue Interactions by Asking Questions
Jiwei Li, Alexander H. Miller, Sumit Chopra +2
A good dialogue agent should have the ability to interact with users by both responding to questions and by asking questions, and importantly to learn from both types of interactio…
Dialogue Learning With Human-In-The-Loop
Jiwei Li, Alexander H. Miller, Sumit Chopra +2
An important aspect of developing conversational agents is to give a bot the ability to improve through communicating with humans and to learn from the mistakes that it makes. Most…
Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews
Grégoire Mesnil, Tomas Mikolov, Marc'Aurelio Ranzato +1
Sentiment analysis is a common task in natural language processing that aims to detect polarity of a text document (typically a consumer review). In the simplest settings, we discr…