Ethan Perez

I'm a Ph.D. student in NLP at NYU, advised by Kyunghyun Cho and Douwe Kiela.

My research focuses on developing question-answering methods that generalize to harder questions than we have supervision for. Learning from human examples (supervised learning) won't scale to these kinds of questions, so I am investigating other paradigms that recursively break down harder questions into simpler ones.

Email  /  CV  /  Google Scholar

Research
FiLM: Visual Reasoning with a General Conditioning Layer
Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, Aaron Courville
AAAI, 2018  
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  

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 Workshop, 2018  

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

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