[Lisa_seminaires] Christian Gagné and Jean-François Lalonde from U. of Laval.

Junyoung Chung elecegg at gmail.com
Ven 2 Juin 13:26:35 EDT 2017


Reminder: talks are starting in 20 minutes.

--Junyoung

On Fri, Jun 2, 2017 at 10:15 AM, Junyoung Chung <elecegg at gmail.com> wrote:

> Hi all,
>
> we have two tea-talks today. You can find the detail of the talks in my
> previous email.
>
> Best,
> --Junyoung
>
> On Mon, May 29, 2017 at 6:58 PM Junyoung Chung <elecegg at gmail.com> wrote:
>
>> Hi all,
>>
>> we have two tea-talks (each is a 30-min talk) this week!
>> Both speakers are from Univ. of Laval.
>> You will find more information below.
>>
>>
>> Talk by Christian Gagné
>>
>> Time: 13:45~14:15
>>
>> Title:
>> Alternating Direction Method of Multipliers for Sparse Convolutional
>> Neural Networks
>>
>> Abstract:
>> The storage and computation requirements of Convolutional Neural Networks
>> (CNNs) can be prohibitive for exploiting these models over low-power or
>> embedded devices. We are proposing to reduce the computational complexity
>> of the CNNs by minimizing an objective function, including the recognition
>> loss that is augmented with a sparsity-promoting penalty term. The sparsity
>> structure of the network is identified using the Alternating Direction
>> Method of Multipliers (ADMM), which is widely used in large optimization
>> problems. This method alternates between promoting the sparsity of the
>> network and optimizing the recognition performance, which allows us to
>> exploit the two-part structure of the corresponding objective functions. In
>> particular, we take advantage of the separability of the sparsity-inducing
>> penalty functions to decompose the minimization problem into sub-problems
>> that can be solved sequentially. Applying our method to a variety of
>> state-of-the-art CNN models, our proposed method is able to simplify the
>> original model, generating models with less computation and fewer
>> parameters, while maintaining and often improving generalization
>> performance. Accomplishments on a variety of models strongly verify that
>> our proposed ADMM-based method can be a very useful tool for simplifying
>> and improving deep CNNs.
>>
>> Short bio:
>> Christian Gagné is professor at the Electrical Engineering and Computer
>> Engineering Department of Université Laval since 2008. He is member of the
>> Computer Vision and Systems Laboratory, the REPARTI FRQNT Strategic
>> Cluster, and the Big Data Research Centre. He completed a Ph.D. in
>> Electrical Engineering (Université Laval) in 2005 before being postdoctoral
>> fellow at the INRIA Saclay - Île-de-France (France) and the University of
>> Lausanne (Switzerland) in 2005-2006. He was research associate for
>> Informatique WGZ Inc. (2006-2007) and MacDonald, Dettwiler, and Associates
>> Ltd. (2007-2008). His research interests are on the engineering of
>> intelligent systems, especially systems based on machine learning,
>> evolutionary computing, and their application to pattern recognition, smart
>> sensors, smart grids, and simulation of public health processes.
>>
>>
>>
>> Talk by Jean-François Lalonde
>>
>> Time: 14:15~14:45
>>
>> Title:
>> Learning to Predict Illumination from a Single Image
>>
>> Abstract:
>> We propose an automatic method to infer high dynamic range illumination
>> from a single, limited field-of-view, low dynamic range photograph of a
>> scene. In contrast to previous work that relies on specialized image
>> capture, user input, and/or simple scene models, we train an end-to-end
>> deep neural network that directly regresses a limited field-of-view photo
>> to HDR illumination, without strong assumptions on scene geometry, material
>> properties, or lighting. This allows us to automatically recover
>> high-quality HDR illumination estimates that significantly outperform
>> previous state-of-the-art methods. Consequently, using our illumination
>> estimates for applications like 3D object insertion, we can achieve results
>> that are photo-realistic.
>>
>> Bio:
>> Jean-François Lalonde is an Assistant Professor in Electrical and
>> Computer Engineering at Laval University, Quebec City, since
>> 2013. Previously, he was a Post-Doctoral Associate at Disney Research,
>> Pittsburgh. He received a B.Eng. degree in Computer Engineering with honors
>> from Laval University, Canada, in 2004. He earned his M.S at the Robotics
>> Institute at Carnegie Mellon University in 2006 under Prof. Martial Hebert
>> and received his Ph.D., also from Carnegie Mellon, in 2011 under the
>> supervision of Profs. Alexei A. Efros and Srinivasa G. Narasimhan. His
>> thesis, titled "Understanding and Recreating Appearance under Natural
>> Illumination," won the 2010-11 CMU School of Computer Science Distinguished
>> Dissertation Award. After graduation, he became a Computer Vision Scientist
>> at Tandent, Inc., where he helped develop LightBrush™, the first commercial
>> intrinsic imaging application. He also introduced intrinsic videos at
>> SIGGRAPH 2012 while at Tandent. His research focuses on lighting-aware
>> image understanding and synthesis by leveraging large amounts of data. More
>> at www.jflalonde.ca.
>>
>> Best,
>> --Junyoung
>>
> --
> --Junyoung
>
-------------- section suivante --------------
Une pièce jointe HTML a été nettoyée...
URL: http://webmail.iro.umontreal.ca/pipermail/lisa_seminaires/attachments/20170602/6c285979/attachment.html 


Plus d'informations sur la liste de diffusion Lisa_seminaires