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

Maxime Chevalier-Boisvert maximechevalierb at gmail.com
Ven 9 Juin 13:07:26 EDT 2017


Any chance that this will be recorded? I can't make it but the topic seems
really interesting.

- Maxime

On 9 June 2017 at 12:14, Joseph Paul Cohen <joseph at josephpcohen.com> wrote:

> Il y a etre snacks!
>
> On Jun 9, 2017 08:47, "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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