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

Junyoung Chung elecegg at gmail.com
Ven 2 Juin 13:30:21 EDT 2017


The room will be AA5340.
On Fri, Jun 2, 2017 at 1:26 PM Junyoung Chung <elecegg at gmail.com> wrote:

> 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
>>
>
> --
--Junyoung
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