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ForestDSH: A Universal Hash Design for Discrete Probability Distributions
To solve our high dimensional classification problem, we introduce distribution sensitive hashes that map jointly generated pairs $(x, y)\sim P$ to the same bucket with probability higher than random pairs $x\sim P^A$ and $y\sim P^B$, where $P^A$ and $P^B$ are the marginal probability distributions of $P$.
From Information Bottleneck To Activation Norm Penalty
Recently, a regularization method has been proposed to optimize the variational lower bound of the Information Bottleneck Lagrangian.
On Random Weights for Texture Generation in One Layer Neural Networks
Recent work in the literature has shown experimentally that one can use the lower layers of a trained convolutional neural network (CNN) to model natural textures.
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