no code implementations • ICLR 2018 • Martin Mundt, Tobias Weis, Kishore Konda, Visvanathan Ramesh
Successful training of convolutional neural networks is often associated with suffi- ciently deep architectures composed of high amounts of features.
no code implementations • 18 May 2017 • Martin Mundt, Tobias Weis, Kishore Konda, Visvanathan Ramesh
Successful training of convolutional neural networks is often associated with sufficiently deep architectures composed of high amounts of features.
5 code implementations • 9 Nov 2015 • Zhouhan Lin, Roland Memisevic, Kishore Konda
We propose ways to improve the performance of fully connected networks.
no code implementations • 29 Jun 2015 • Xavier Bouthillier, Kishore Konda, Pascal Vincent, Roland Memisevic
Dropout is typically interpreted as bagging a large number of models sharing parameters.
no code implementations • 5 Mar 2015 • Samira Ebrahimi Kahou, Xavier Bouthillier, Pascal Lamblin, Caglar Gulcehre, Vincent Michalski, Kishore Konda, Sébastien Jean, Pierre Froumenty, Yann Dauphin, Nicolas Boulanger-Lewandowski, Raul Chandias Ferrari, Mehdi Mirza, David Warde-Farley, Aaron Courville, Pascal Vincent, Roland Memisevic, Christopher Pal, Yoshua Bengio
The task of the emotion recognition in the wild (EmotiW) Challenge is to assign one of seven emotions to short video clips extracted from Hollywood style movies.
no code implementations • NeurIPS 2014 • Vincent Michalski, Roland Memisevic, Kishore Konda
We propose modeling time series by representing the transformations that take a frame at time t to a frame at time t+1.
no code implementations • 13 Feb 2014 • Kishore Konda, Roland Memisevic, David Krueger
We show that negative biases are a natural result of using a hidden layer whose responsibility is to both represent the input data and act as a selection mechanism that ensures sparsity of the representation.
no code implementations • 10 Feb 2014 • Vincent Michalski, Roland Memisevic, Kishore Konda
In this work we extend bi-linear models by introducing "higher-order mapping units" that allow us to encode transformations between frames and transformations between transformations.
no code implementations • 12 Dec 2013 • Kishore Konda, Roland Memisevic
We present a model for the joint estimation of disparity and motion.