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

Junyoung Chung elecegg at gmail.com
Ven 2 Juin 10:15:25 EDT 2017


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
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