Portrait of Emmanuel Bengio

Emmanuel Bengio

Associate Industry Member
Staff Machine Learning Scientist, Recursion
Research Topics
Deep Learning
Generative Models
GFlowNets
Molecular Modeling
Reinforcement Learning

Biography

Emmanuel Bengio is an ML Scientist at Valence Labs/Recursion, working on the intersection of GFlowNets and drug discovery. He did his PhD under Joelle Pineau and Doina Precup at McGill/Mila - Quebec Artificial Intelligence Institute, focusing on understanding generalization in deep RL.

Publications

World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions
Teng Long
Emmanuel Bengio
Ryan Lowe
Jackie Cheung
Doina Precup
Humans interpret texts with respect to some background information, or world knowledge, and we would like to develop automatic reading compr… (see more)ehension systems that can do the same. In this paper, we introduce a task and several models to drive progress towards this goal. In particular, we propose the task of rare entity prediction: given a web document with several entities removed, models are tasked with predicting the correct missing entities conditioned on the document context and the lexical resources. This task is challenging due to the diversity of language styles and the extremely large number of rare entities. We propose two recurrent neural network architectures which make use of external knowledge in the form of entity descriptions. Our experiments show that our hierarchical LSTM model performs significantly better at the rare entity prediction task than those that do not make use of external resources.
2017-09-01
Conference on Empirical Methods in Natural Language Processing (published)
doi.org
A Closer Look at Memorization in Deep Networks
Devansh Arpit
Stanisław Jastrzębski
Nicolas Ballas
David Scott Krueger
Emmanuel Bengio
Maxinder S. Kanwal
Tegan Maharaj
Asja Fischer
Aaron Courville
Yoshua Bengio
Simon Lacoste-Julien
We examine the role of memorization in deep learning, drawing connections to capacity, generalization, and adversarial robustness. While dee… (see more)p networks are capable of memorizing noise data, our results suggest that they tend to prioritize learning simple patterns first. In our experiments, we expose qualitative differences in gradient-based optimization of deep neural networks (DNNs) on noise vs. real data. We also demonstrate that for appropriately tuned explicit regularization (e.g., dropout) we can degrade DNN training performance on noise datasets without compromising generalization on real data. Our analysis suggests that the notions of effective capacity which are dataset independent are unlikely to explain the generalization performance of deep networks when trained with gradient based methods because training data itself plays an important role in determining the degree of memorization.
2017-07-17
Proceedings of the 34th International Conference on Machine Learning (published)
proceedings.mlr.press
arxiv.org
Deep Nets Don't Learn via Memorization
David Scott Krueger
Nicolas Ballas
Stanisław Jastrzębski
Devansh Arpit
Maxinder S. Kanwal
Tegan Maharaj
Emmanuel Bengio
Asja Fischer
Aaron Courville
We use empirical methods to argue that deep neural networks (DNNs) do not achieve their performance by memorizing training data in spite of … (see more)overlyexpressive model architectures. Instead, they learn a simple available hypothesis that fits the finite data samples. In support of this view, we establish that there are qualitative differences when learning noise vs. natural datasets, showing: (1) more capacity is needed to fit noise, (2) time to convergence is longer for random labels, but shorter for random inputs, and (3) that DNNs trained on real data examples learn simpler functions than when trained with noise data, as measured by the sharpness of the loss function at convergence. Finally, we demonstrate that for appropriately tuned explicit regularization, e.g. dropout, we can degrade DNN training performance on noise datasets without compromising generalization on real data.
2017-02-17
International Conference on Learning Representations (published)
dblp.uni-trier.de