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lisa_seminaires@iro.umontreal.ca

September 2018

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[Tea Talk] Viral Shah (Julia Computing) Fri September 14 2018 10:30 Z315
by Michael Noukhovitch 10 Sep '18

10 Sep '18

This week we have *Viral Shah * from * Julia Computing * giving a talk on *Fri September 14 2018* at *10:30* in room *Z315 (in Pavilion Claire McNicoll)*. It's not our usual room but it'll be a regular room for the next two months :) Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? Yes And you can sign up to meet the speaker here: if there's a tea talk, you should always GOTO it Michael *TITLE* On Machine Learning and Programming Languages *KEYWORDS *ML/DL infrastructure, Programming Languages *ABSTRACT* We ask, what might the ideal ML language of the future look like? Our thoughts are published in this blog post: https://julialang.org/blog/2017/12/ml&pl As programming languages (PL) people, we have watched with great interest as machine learning (ML) has exploded -- and with it, the complexity of ML models and the frameworks people are using to build them. State-of-the-art models are increasingly programs, with support for programming constructs like loops and recursion, and this brings out many interesting issues in the tools we use to create them -- that is, programming languages. While machine learning does not yet have a dedicated language, several efforts are effectively creating hidden new languages underneath a Python API (like TensorFlow) while others are reusing Python as a modeling language (like PyTorch). We'd like to ask -- are new ML-tailored languages required, and if so, why? Now that Julia 1.0 is released, we will also discuss how Julia evolved to get where it is today, and how it might evolve to taking on some of the challenges posed by machine learning *BIO* Dr. Viral Shah is a co-creator of the Julia project and Co-founder and CEO of Julia Computing. He has had a long-term track record of building open-source software. Apart from Julia, he is also co-creator of Circuitscape, an open-source program which borrows algorithms from electronic circuit theory for ecological conservation. In the Government of India, he was an early member of the country’s national ID project - Aadhaar, where his work on re-architecting India’s social security systems led to a significant increase in social and financial inclusion, while simultaneously saving the exchequer over a billion dollars in slippage. The experiences of implementing technology at such scale for a billion people are collected in his book: Rebooting India. Viral has a Ph. D. from the University of California at Santa Barbara, in Computer Science.

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[Tea Talk] Geoff Gordon (MSR Montreal) Fri September 7 2018 10:30 AA3195
by Michael Noukhovitch 07 Sep '18

07 Sep '18

This week we are super lucky to have *Geoff Gordon*, Research Director of * MSR Montreal * giving a talk on *Fri September 7 2018* at *10:30* in room *AA3195* Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? *No* Geoff will be available to meet in the afternoon! Sign up here <https://calendar.google.com/calendar/selfsched?sstoken=UVBVTVF0X25Nd09LfGRl…> Knowing that this talk is likely awesome and that you probably come to talks given they're awesome, you're probably going to the talk! Good choice! Michael *TITLE* Neural Networks and Bayes Rule *KEYWORDS* Deep Graphical Models, Reasoning with NNs *ABSTRACT* Relational or structured reasoning is an important current research challenge. The classical approach to this challenge is a templated graphical model: highly expressive, with well-founded semantics, but (at least naively) difficult to scale up, and difficult to combine with the most effective supervised learning methods. More recently, researchers have designed many different deep network architectures for structured reasoning problems, with almost the flip set of advantages and disadvantages. Can we get the best of both worlds? That is, can we design deep nets that look more like graphical models, or graphical models that look more like deep nets, so that we get a framework that is both practical and "semantic"? This talk will take a look at some progress toward such a hybrid framework. *BIO* Dr. Gordon is the Research Director of Microsoft Research Montreal. He is on leave as a Professor in the Department of Machine Learning at Carnegie Mellon University, where he has also served as Interim Department Head and as Associate Department Head for Education. His research interests include artificial intelligence, statistical machine learning, game theory, multi-robot systems, and planning in probabilistic, adversarial, and general-sum domains. His previous appointments include Visiting Professor at the Stanford Computer Science Department and Principal Scientist at Burning Glass Technologies in San Diego. Dr. Gordon received his B.A. in Computer Science from Cornell University in 1991, and his Ph.D. in Computer Science from Carnegie Mellon University in 1999.

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