Ethan Perez

I'm a second-year Ph.D. student doing research on Natural Language Processing at New York University, advised by Kyunghyun Cho and Douwe Kiela.

My research focuses on developing learning algorithms that have the long-term potential to answer questions that people don't know the answers to. Supervised learning cannot answer such questions, even in principle, so I am investigating other learning paradigms for generalizing beyond the available supervision.

I earned a Bachelor’s from Rice University as the Engineering department’s Outstanding Senior. Previously, I've spent time at Facebook AI Research and Google, and I had the great pleasure of working at the Montreal Institute for Learning Algorithms with Aaron Courville and Hugo Larochelle.

Email  /  CV  /  Google Scholar  /  Twitter

Research
Unsupervised Question Decomposition for Question Answering
Ethan Perez, Patrick Lewis, Scott Wen-tau Yih, Kyunghyun Cho, Douwe Kiela,
Reasoning for Complex Question Answering Workshop, AAAI 2020   (Oral Presentation)
[Code] [Blog Post]  

We decompose a hard question into several, easier questions with unsupervised learning, improving multi-hop question answering without extra supervision.

Finding Generalizable Evidence by Learning to Convince Q&A Models
Ethan Perez, Siddharth Karamcheti, Rob Fergus, Jason Weston, Douwe Kiela, Kyunghyun Cho,
EMNLP 2019. [Code] [Blog Post] [Press]  

We find text evidence for an answer to a question by finding text that convinces Q&A models to pick that answer.

ELI5: Long Form Question Answering
Angela Fan, Yacine Jernite*, Ethan Perez*, David Grangier, Jason Weston, Michael Auli
ACL 2019. [Code] [Blog Post] [Website]  

We introduce a dataset for abstractive question-answering where answers are 100+ words long (many "how" and "why" questions).

FiLM: Visual Reasoning with a General Conditioning Layer
Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, Aaron Courville
AAAI 2018. [Code] [Presentation]  

A general-purpose neural network layer can be used to integrate multimodal input to answer reasoning questions about images.

Feature-wise transformations
Vincent Dumoulin, Ethan Perez, Nathan Schucher, Florian Strub, Harm de Vries, Aaron Courville, Yoshua Bengio
Distill 2018.  

A review of a simple and surprisingly effective class of neural conditioning mechanisms.

Visual Reasoning with Multi-hop Feature Modulation
Florian Strub, Mathieu Seurin, Ethan Perez, Harm de Vries, Jeremie Mary, Aaron Courville, Olivier Pietquin
ECCV 2018. [Code]  

Decoding FiLM conditioning parameters in multiple hops helps for more advanced vision-and-language tasks such as visual dialogue.

HoME: a Household Multimodal Environment
Simon Brodeur, Ethan Perez*, Ankesh Anand*, Florian Golemo*, Luca Celotti, Florian Strub, Hugo Larochelle, Aaron Courville
ICLR 2018 Workshop. [Code]  

We introduce a simulated environment for agents to learn from vision, audio, semantics, physics, and object-interaction within a realistic, household context.

Semi-supervised learning with the deep rendering mixture model
Tan Nguyen, Wanjia Liu, Ethan Perez, Richard G. Baraniuk, Ankit B. Patel
arXiv 2018.  

A probabilistic graphical model underlying CNNs achieves state-of-the-art semi-supervised image classification.


Design courtesy of Jon Barron