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

Dzmitry Bahdanau dimabgv at gmail.com
Ven 14 Juil 12:25:31 EDT 2017


Hi all,

Just a kind reminder - it's today!

Dima

On Wed, Jul 12, 2017, 15:26 Dzmitry Bahdanau <dimabgv at gmail.com> wrote:

> 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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