150 citations · 832 across the 25 of their papers we have counts for
8 papers · 1 filter
How far can we go without convolution: Improving fully-connected networks
Zhouhan Lin, Roland Memisevic, Kishore Konda
We propose ways to improve the performance of fully connected networks. We found that two approaches in particular have a strong effect on performance: linear bottleneck layers and…
Regularizing RNNs by Stabilizing Activations
David Krueger, Roland Memisevic
We stabilize the activations of Recurrent Neural Networks (RNNs) by penalizing the squared distance between successive hidden states' norms. This penalty term is an effective regul…
Denoising Criterion for Variational Auto-Encoding Framework
Daniel Jiwoong Im, Sungjin Ahn, Roland Memisevic +1
Denoising autoencoders (DAE) are trained to reconstruct their clean inputs with noise injected at the input level, while variational autoencoders (VAE) are trained with noise injec…
RATM: Recurrent Attentive Tracking Model
Samira Ebrahimi Kahou, Vincent Michalski, Roland Memisevic
We present an attention-based modular neural framework for computer vision. The framework uses a soft attention mechanism allowing models to be trained with gradient descent. It co…
Neural Networks with Few Multiplications
Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic +1
For most deep learning algorithms training is notoriously time consuming. Since most of the computation in training neural networks is typically spent on floating point multiplicat…
Dropout as data augmentation
Xavier Bouthillier, Kishore Konda, Pascal Vincent +1
Dropout is typically interpreted as bagging a large number of models sharing parameters. We show that using dropout in a network can also be interpreted as a kind of data augmentat…
Conservativeness of untied auto-encoders
Daniel Jiwoong Im, Mohamed Ishmael Diwan Belghazi, Roland Memisevic
We discuss necessary and sufficient conditions for an auto-encoder to define a conservative vector field, in which case it is associated with an energy function akin to the unnorma…
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…