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Found 9 papers, 1 papers with code
Building effective deep neural networks one feature at a time
Successful training of convolutional neural networks is often associated with suffi- ciently deep architectures composed of high amounts of features.
Building effective deep neural network architectures one feature at a time
Successful training of convolutional neural networks is often associated with sufficiently deep architectures composed of high amounts of features.
How far can we go without convolution: Improving fully-connected networks
We propose ways to improve the performance of fully connected networks.
Dropout as data augmentation
Dropout is typically interpreted as bagging a large number of models sharing parameters.
EmoNets: Multimodal deep learning approaches for emotion recognition in video
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.
Modeling Deep Temporal Dependencies with Recurrent Grammar Cells""
We propose modeling time series by representing the transformations that take a frame at time t to a frame at time t+1.
Zero-bias autoencoders and the benefits of co-adapting features
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.
Modeling sequential data using higher-order relational features and predictive training
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.
Unsupervised learning of depth and motion
We present a model for the joint estimation of disparity and motion.
