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Found 3 papers, 1 papers with code
Towards the Development of Entropy-Based Anomaly Detection in an Astrophysics Simulation
The use of AI and ML for scientific applications is currently a very exciting and dynamic field.
Challenges in Markov chain Monte Carlo for Bayesian neural networks
Nevertheless, this paper shows that a non-converged Markov chain, generated via MCMC sampling from the parameter space of a neural network, can yield via Bayesian marginalization a valuable posterior predictive distribution of the output of the neural network.
Exascale Deep Learning for Scientific Inverse Problems
We introduce novel communication strategies in synchronous distributed Deep Learning consisting of decentralized gradient reduction orchestration and computational graph-aware grouping of gradient tensors.
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