[Lisa_seminaires] [TeaTalk] Yaniv Romano (Thursday, Aug 24) and Georgy Derevyanko (Friday, Aug 25)

Dzmitry Bahdanau dimabgv at gmail.com
Jeu 24 Aou 10:19:06 EDT 2017


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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