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

Dzmitry Bahdanau dimabgv at gmail.com
Mar 6 Juin 15:22:59 EDT 2017


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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