[Lisa_seminaires] [TeaTalk] Ethan Perez, July 14, AA6214, 13:45

Dzmitry Bahdanau dimabgv at gmail.com
Mer 12 Juil 15:26:37 EDT 2017


Hi all,

Our next speaker is Ethan Perez, who is currently interning at MILA with
Aaron Courville. Please come to AA6214 on July 14 at 13:45!

*Title:* Learning Visual Reasoning Without Strong Priors

*Abstract:*
Achieving artificial visual reasoning - the ability to answer image-related
questions which require a multi-step, high-level process - is an important
step towards artificial general intelligence. This multi-modal task
requires learning a question-dependent, structured reasoning process over
images from language. Standard deep learning approaches tend to exploit
biases in the data rather than learn this underlying structure, while
leading methods learn to visually reason successfully but are hand-crafted
for reasoning. We show that a general-purpose, Conditional Batch
Normalization method achieves state-of-the-art results on the Compositional
Language and Elementary Visual Reasoning (CLEVR) task with a 2.4% error
rate. We outperform the next best end-to-end method (4.5%) which uses data
augmentation and even methods that use extra supervision (3.1%). We probe
our model to shed light on how it reasons, showing it has learned a
question-dependent, multi-step process. Previous work has operated under
the assumption that visual reasoning calls for a specialized architecture,
but we show that a general architecture with proper conditioning can learn
to visually reason effectively.

*Bio:*
Ethan Perez is a rising 4th year computer science undergrad at Rice
University. He is currently interning at MILA working with Aaron Courville
on Visual Reasoning. Previously, he has researched on deep semi-supervised
learning methods and built machine learning models for location detection
at Google Maps and fraud detection at Uber.

Dima
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