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Found 3 papers, 1 papers with code
Modular Deep Probabilistic Programming
A probabilistic module consists of a set of random variables with associated probabilistic distributions and dedicated inference methods.
Auto-Differentiating Linear Algebra
Development systems for deep learning (DL), such as Theano, Torch, TensorFlow, or MXNet, are easy-to-use tools for creating complex neural network models.
Interpretable Anomaly Detection with Mondrian P{ó}lya Forests on Data Streams
Anomaly detection at scale is an extremely challenging problem of great practicality.
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