125 citations · 179 across the 3 of their papers we have counts for
3 papers
Blocks and Fuel: Frameworks for deep learning
Bart van Merriënboer, Dzmitry Bahdanau, Vincent Dumoulin +4
We introduce two Python frameworks to train neural networks on large datasets: Blocks and Fuel. Blocks is based on Theano, a linear algebra compiler with CUDA-support. It facilitat…
EmoNets: Multimodal deep learning approaches for emotion recognition in video
Samira Ebrahimi Kahou, Xavier Bouthillier, Pascal Lamblin +15
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. The videos depict…
Self-informed neural network structure learning
David Warde-Farley, Andrew Rabinovich, Dragomir Anguelov
We study the problem of large scale, multi-label visual recognition with a large number of possible classes. We propose a method for augmenting a trained neural network classifier…