[Lisa_seminaires] [teatalk-orgs] Re: [TeaTalk] Yaniv Romano (Thursday, Aug 24) and Georgy Derevyanko (Friday, Aug 25)

Junyoung Chung elecegg at gmail.com
Jeu 24 Aou 13:16:47 EDT 2017


Hi everyone,

We have a talk in 15 minutes at Z209.

On Thu, Aug 24, 2017 at 10:19 AM Dzmitry Bahdanau <dimabgv at gmail.com> wrote:

> Just a kind reminder that Yaniv's talk is today.
>
> Dima
>
> On Mon, 21 Aug 2017 at 16:45 Dzmitry Bahdanau <dimabgv at gmail.com> wrote:
>
>> Hi all,
>>
>> We have two tea-talks this week!
>>
>> First, at *13:30 on Thursday, August 24, at Z-209*, we will have a
>> presentation by Yaniv Romano, who is a PhD student at Technion, Israel.
>>
>> Second, at *13:45 on Friday, August 25, at AA6214, Georgy Derevyanko*,
>> who is currently a post-doc at Concordia and MILA, will share his research
>> with us.
>>
>> Please find more details below, and hope to see you in great numbers!
>>
>> Dima
>>
>> *Speaker 1:* Yaniv Romano
>>
>> *Title: *A Quest for a Universal Model for Signals: From Sparsity to
>> ConvNets
>>
>> *Abstract: *The celebrated sparse representation model assumes that a
>> signal can be represented as a linear combination of a few columns, also
>> called atoms, taken from a matrix, termed a dictionary. When dealing with
>> high-dimensional signals, addressing the dictionary learning problem
>> becomes computationally infeasible due to the curse of dimensionality.
>> Traditionally, this problem was circumvented by learning a local sparse
>> model on small overlapping patches, extracted from the global signal, and
>> processing (e.g. denoising) these independently. We will start this talk by
>> proposing various approaches to bridge the gap between the efficient
>> independent local processing and the need to model the global signal at
>> hand. In particular, we will describe novel image restoration algorithms,
>> leading not only to state-of-the-art results, but also to a systematic and
>> generic way to boost the performance of many existing algorithms.
>>
>> A different approach to treat high dimensional signals is the
>> convolutional sparse coding (CSC). This global model assumes that a signal
>> can be represented as a superposition of a few local atoms, or small
>> filters, shifted to different positions. A recent work suggested a novel
>> theoretical analysis of this global model, which is based on the
>> observation that while being global, the CSC can be characterized and
>> analyzed locally. We will extend this local-global relation by showing how
>> one can efficiently solve the pursuit problem and train the filters
>> involved, while operating locally on image patches.
>>
>> Armed with these new insights, we proceed by proposing a multi-layer
>> extension of this model, ML-CSC, in which signals are assumed to emerge
>> from a cascade of CSC layers. This, in turn, is shown to be tightly
>> connected to Convolutional Neural Networks (CNN), so much so that the
>> forward-pass of the CNN is in fact the Thresholding pursuit serving the
>> ML-CSC model. This connection brings a fresh view to CNN, as we are able to
>> attribute to this architecture theoretical claims such as uniqueness of the
>> representations throughout the network, and their stable estimation, all
>> guaranteed under simple local sparsity conditions. Lastly, identifying the
>> weaknesses in the above scheme, we propose an alternative to the
>> forward-pass algorithm, which is both tightly connected to deconvolutional
>> and recurrent neural networks, and has better theoretical guarantees.
>>
>> *Bio: *Yaniv Romano received his B.Sc. degree from the Department of
>> Electrical Engineering, Technion – Israel Institute of Technology, in 2012,
>> where he is currently pursuing his Ph.D.. He received the 2015 Zeff
>> fellowship, the 2017 Andrew and Erna Finci Viterbi fellowship, and the 2017
>> Irwin and Joan Jacobs fellowship.
>> In parallel to his studies, he has been working in the industry since
>> 2011 as an Image Processing Algorithm Developer. The super-resolution
>> technology he invented as an intern in Google Research was launched in
>> 2017, leading to significant bandwidth savings of billions of images.
>>
>> *Speaker 2: *Georgy Derevyanko
>>
>> *Title:* Protein folding project
>>
>> *Abstract:* Protein folding and structure prediction is a 50 years old
>> problem.
>> The solution to it will immediately change the whole industry of drug
>> discovery and biology itself.
>> This talk is a call for collaboration to advance the field of protein
>> structure prediction using deep learning techniques.
>> I will give an overview of the state-of-art algorithms in the field and
>> their assessment procedures.
>> Also, I will present some of our publication-ready and preliminary
>> results.
>> Moreover, I will present the library of differentiable transformations of
>> a protein chemical
>> structure and pre-processed datasets in order to lower the problem entry
>> barrier*.*
>>
>> *Bio:* -2014 PhD in "Physics for life sciences" from Universite Joseph
>> Fourier (Grenoble, France)
>> -2016 PostDoc in experimental structural biology at Forshungszentrum
>> Juelich (Juelich, Germany)
>> -present PostDoc at Concordia CERMM & MILA
>>
>> --
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-- 
--Junyoung
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