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Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry
Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape.
Investigating Efficient Learning and Compositionality in Generative LSTM Networks
In the past, the character challenge was only met by complex algorithms that were provided with stochastic primitives.
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