Quoting from this TEDx talk: Forgetting and disregarding is the basis of discerning.
After providing a brief history of bio-inspired computing, the speaker identifies what he thinks are the missing ingredients in state-of-the-art machines that must be supplied to close the gap with human performance.
Mixed feedback processors capable of implementing plastic localization are (at least) what it would take to 'win' the Imitation Game.
Do brains compute? : Rodolphe Sepulchre at TEDx Liege
#Cybernetics #TuringTest #biomimetics
Could I draw your kind attention to our new library of annotated synthetic indoor scenes we have been working on for a while? We think this could be potentially very useful for people interested in scene understanding for robotics and generating unlimited amount of training data from arbitrary view-points. This library emerged as a part of our joint-reconstruction-and-semantic-segmentation with conv-nets based approach. We have been trying to do segmentation of functional categories of objects purely from geometric cues.
Our semantic segmentation module builds on the work which kindly involved me in last year using his idea of saving pooling indices inspired from Marc'Aurelio Ranzato's unsupervised learning method. It is quite essential when do you semantic segmentation that you get your boundaries right and saving pooling indices in your conv-net whenever you use pooling helps quite a lot. You can find the relevant papers at the bottom of the webpage and nice little demo created together with 's caffe implementation.
The library webpage also has a small presentation in the publications section that I gave this CVPR in a workshop organised by Ian Reid. This is joint work with , , and and we are happy to share all the models and hope that you can use them and give us good feedback on expanding the library and the overall approach.
- FNRS (Aspirant)Ph.D. candidate, 2011 - 2014
- IAP DySCOPh.D. candidate, 2010 - 2011
The research deals with the application of manifold optimization techniques to large-scale convex optimization problems whose expected or desired solutions have low rank. We focus specifically on convex relaxations of large-scale rank-constrained problems encountered in machine learning, data reduction and bioinformatics.
- University of LiègeApplied Mathematics, 2010 - present
- Indian Institute of Technology BombayElectrical Engineering, 2005 - 2010
- Google scholar page (current)