[Lisa_seminaires] [TeaTalk] Danny Tarlow, June 9, 13:45, AA5340

Dzmitry Bahdanau dimabgv at gmail.com
Ven 9 Juin 14:53:45 EDT 2017


Hi all,

Thanks for coming everyone, that was the record number of people! If you
want to talk to Danny, he will be around at 6th floor for some time.

Dima

On Fri, 9 Jun 2017 at 08:46 Dzmitry Bahdanau <dimabgv at gmail.com> wrote:

> Hi all,
>
> Just a kind reminder about the talk :)
>
> Dima
>
> On Tue, 6 Jun 2017 at 15:22 Dzmitry Bahdanau <dimabgv at gmail.com> wrote:
>
>> Hi all,
>>
>> You have just received an email about the tea-talk on June 13, but I have
>> good news for you: we will have one more before that!
>>
>> Our next (really next) speaker is *Danny Tarlow*, who is a Research
>> Scientist at Google Brain Montreal. He will present on *June 9, 13:45,
>> at AA5340* (our usual tea-talk slot). Please find detailed information
>> below.
>>
>> For those who are confused, the coming tea-talks will be given on June 9,
>> 13 and 15 (yet to be announced).
>>
>> *Title:* Learning to Code: Machine Learning for Program Induction
>>
>> *Abstract:* I'll present two of our recent works on using machine
>> learning ideas to induce computer programs from input-output examples. The
>> first system is TerpreT, which casts program synthesis as a continuous
>> optimization problem on which we perform gradient descent. It enables
>> comparison of gradient-based program synthesis techniques to discrete
>> search techniques that are popular in the programming languages community.
>> Based on our learnings from TerpreT, we develop the second system,
>> DeepCoder, which induces programs from input-output examples using a neural
>> network to guide discrete search techniques. DeepCoder achieves an order of
>> magnitude speedup over optimized search techniques, and it can solve
>> problems of difficulty comparable to the very simplest problems on
>> programming competition websites.
>>
>> *Bio:* Danny Tarlow is a Research Scientist at Google Brain Montreal.
>> His main research interests are in the application of machine learning to
>> problems involving structured data, with a specific interest in the
>> intersection of machine learning and programming languages. He is a
>> co-editor of the recent MIT Press book on Perturbations, Optimization, and
>> Statistics (2017). His work has won awards at UAI (Best Student Paper,
>> Runner Up), the ICML Workshop on Constructive Machine Learning (Best
>> Paper), the NIPS Workshop on Neural Abstract Machines and Program Induction
>> (Best Paper), and NIPS (Best Paper). He holds a Ph.D. from the Machine
>> Learning group at the University of Toronto (2013) and previously was a
>> Researcher at Microsoft Research Cambridge UK (2013 - 2017) with a Research
>> Fellowship at Darwin College, University of Cambridge (2013 - 2016).
>>
>> Dima
>>
>
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