96 citations · 261 across the 9 of their papers we have counts for
9 papers
Only sparsity based loss function for learning representations
Vivek Bakaraju, Kishore Reddy Konda
We study the emergence of sparse representations in neural networks. We show that in unsupervised models with regularization, the emergence of sparsity is the result of the input d…
Building effective deep neural network architectures one feature at a time
Martin Mundt, Tobias Weis, Kishore Konda +1
Successful training of convolutional neural networks is often associated with sufficiently deep architectures composed of high amounts of features. These networks typically rely on…
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…
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…
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…
Modeling sequential data using higher-order relational features and predictive training
Vincent Michalski, Roland Memisevic, Kishore Konda
Bi-linear feature learning models, like the gated autoencoder, were proposed as a way to model relationships between frames in a video. By minimizing reconstruction error of one fr…
Zero-bias autoencoders and the benefits of co-adapting features
Kishore Konda, Roland Memisevic, David Krueger
Regularized training of an autoencoder typically results in hidden unit biases that take on large negative values. We show that negative biases are a natural result of using a hidd…
Unsupervised learning of depth and motion
Kishore Konda, Roland Memisevic
We present a model for the joint estimation of disparity and motion. The model is based on learning about the interrelations between images from multiple cameras, multiple frames i…
Learning to encode motion using spatio-temporal synchrony
Kishore Reddy Konda, Roland Memisevic, Vincent Michalski
We consider the task of learning to extract motion from videos. To this end, we show that the detection of spatial transformations can be viewed as the detection of synchrony betwe…