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Probabilistic and Regularized Graph Convolutional Networks
This paper explores the recently proposed Graph Convolutional Network architecture proposed in (Kipf & Welling, 2016) The key points of their work is summarized and their results are reproduced.
Gradient Augmented Information Retrieval with Autoencoders and Semantic Hashing
This paper will summarize how to efficiently train an autoencoder in order to create meaningful and low-dimensional encodings of data.
Categorical Mixture Models on VGGNet activations
In order to do this, I extract layer activations from a pre-trained implementation of the popular VGGNet convolutional neural network.
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