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

Joseph Paul Cohen joseph at josephpcohen.com
Ven 14 Juil 12:30:08 EDT 2017


There will be food!

On Fri, Jul 14, 2017 at 12:25 PM, Dzmitry Bahdanau <dimabgv at gmail.com> wrote:
> 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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