[Lisa_seminaires] Fwd: [Lisa_labo] Talk 21 Nov Fri @15.00 AA3195 by Dr. John Hershey

Frédéric Bastien frederic.bastien at gmail.com
Ven 21 Nov 11:51:42 EST 2014


I forward this as there is an email issue.

Fred
---------- Forwarded message ----------
From: Kyung Hyun Cho <cho.k.hyun at gmail.com>
Date: Fri, Nov 21, 2014 at 11:38 AM
Subject: Re: [Lisa_labo] Talk 21 Nov Fri @15.00 AA3195 by Dr. John Hershey
To: lisa_labo Labo <lisa_labo at iro.umontreal.ca>, "
lisa_teatalk at iro.umontreal.ca" <lisa_teatalk at iro.umontreal.ca>,
lisa_seminaires at iro.umontreal.ca
Cc: John Hershey <hershey at merl.com>


Dear all,

It's a reminder that we have a talk by Dr. John Hershey *today* at 15.00.

Hope to see many of you there!
- K

On Sun, Nov 16, 2014 at 6:00 PM, Kyung Hyun Cho <cho.k.hyun at gmail.com>
wrote:

> Dear all,
>
> Dr. John Hershey (Mistubishi Electric Research Labs, US) will tell us
> about the connection between model-free approaches (e.g. deep neural
> networks) and model-based approaches (e.g. probabilistic graphical models)
> and how we can utilize this connection. His talk will start from 15.00 next
> Friday (21 Nov) at the usual place AA3195.
>
> Hope to see many of you there!
> - Cho
>
> ===
> - Speaker: Dr. John Hershey (Mistubishi Electric Research Labs)
> - Date/Time: 15.00 - 16.00, 21 Nov
> - Place: AA3195
> - Title: Deep Unfolding: Infusing Deep Architectures with Generative
> Model Inference
> - Abstract:
>
> Model-based methods and deep neural networks have both been tremendously
> successful paradigms in machine learning. In model-based methods, problem
> domain knowledge can be built into the constraints of the model, typically
> at the expense of difficulties during inference. In contrast, deterministic
> deep neural networks are constructed in such a way that inference is
> straightforward, but their architectures are rather  generic and it can be
> unclear how to incorporate problem domain knowledge.  This work aims to
> obtain the advantages of both approaches. To do so, we start with a
> model-based approach and unfold the iterations of its inference method to
> form a layer-wise structure.   We then decouple the model parameters across
> layers to increase the network's learning capacity.  This results in novel
> neural-network-like architectures that incorporate our model-based
> constraints, but can be trained discriminatively to perform fast and
> accurate inference. We show how this framework can be applied to a
> non-negative matrix factorization model to obtain a new kind of
> non-negative deep neural network, that can be trained using a
> multiplicative backpropagation-style update algorithm. We present speech
> enhancement experiments showing that our approach is competitive with
> conventional neural networks despite using far fewer parameters.
>
> - Bio:
> John Hershey is a researcher at Mistubishi Electric Research Labs (MERL),
> in Cambridge, MA since 2010.  Prior to that, John spent 5 years as a
> researcher at IBM's Watson Research Center in New York, in the Speech
> Algorithms and Engines group, and one year as visiting researcher in the
> speech group at Microsoft Research, in Redmond, WA.   His obtained his
> Ph.D. at the University of California, San Diego.  He is currently working
> on machine learning for signal enhancement and separation, speech
> recognition, language processing, and adaptive user interfaces.
>
>
>
>
>

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