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Seminars in 2017
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November 22, 2017 , Room: A-3600, time: 12:00
Jose Dolz
Topic: Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation.
November 8, 2017 , Room: A-3600, time: 12:00
Luiz Gustavo
Topic: adversarial examples in deep neural networks.(
Github Link
) (
Slides
)
October 11, 2017 , Room: A-3600, time: 12:00
Karthik Gopinath
Topic: A design for an automated Optical Coherence Tomography analysis system.(
Slides
)
September 12, 2017 , Room: A-1424, time: 14:00
Invited Talk
:
Prof. Marco Loog (Delft University)
Topic: Improvement Guarantees for Semi-Supervised Learning.
September 8, 2017 , Room: A-3600, time: 14:00
Invited Talk
:
Prof. Jing Yuan (Xidian University, China)
Topic: High-Performance Dual Decomposition and Optimization Methods with Applications to Image Analysis.
July 19, 2017 , Room: A-3600, time: 12:00
Prof. Kuldeep Kumar, PhD student (ETS)
Topic: Fiberprint: a subject fingerprint based on sparse code pooling for white matter fiber analysis.
June 26, 2017 (Monday) , Room: A-1424, time: 14:00
Invited Talk
:
Prof. Pierre-Marc Jodoin,(University of Sherbrooke)
Topic: Recent advances at the VITALab : solving video analytic and medical imaging problems trough deep learning methods.
June 7, 2017 (Wednesday) , Room: A-3600
Dr. Jose Dolz
(ETS)
Topic: Segmentation of Medical Images via Deep Learning Techniques: Current State-Of-The-Art and Perspectives. (
Slides
)
May 18, 2017 (Thursday) , Room: A-1170
Invited Talk
:
Prof. Yuri Boykov,
(University of Western Ontario)
Topic: Kernel Clustering meets Markov Random Fields.
May 18, 2017 (Thursday) , Room: A-1170
Invited Talk
:
Prof. Prof. Olga Veskler,
(University of Western Ontario)
Topic: Adaptive and Move Making Auxiliary Cuts for Binary Pairwise Energies.
May 10, 2017 (Wednesday) , Room: A-1424
Invited Talk
:
Dr. Albert Gordo,
(Computer Vision Group, Xerox Research Centre Europe)
Topic: Learning deep image representations for visual search.
April 19, 2017 (Wednesday) , Room: A-1424
Prof. Marco Pedersoli
(ETS)
Topic: Weakly Supervised Models for Visual Recognition. (
Slides
)