A look at The Case for Bayesian Deep Learning | Bayesian Inference - Synced

Bayesian inference meanwhile leverages Bayes’ theorem to update the probability of a hypothesis as additional data becomes available. How can Bayesian inference benefit deep learning models? by Synced.

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Bayes’ theorem is one of the most important formulae in the field of mathematical statistics and probability, used to calculate the chances of a particular event occurring based on relevant existing information.

Bayesian inference meanwhile leverages Bayes’ theorem to update the probability of a hypothesis as additional data becomes available. How can Bayesian inference benefit deep learning models? New York University Assistant Professor Andrew Gordon Wilson addressed this question in his recent paper The Case for Bayesian Deep Learning...

Synced invited Dr. Hao Wang, a Postdoctoral Associate at the MIT Computer Science & Artificial Intelligence Lab (CSAIL) who works on statistical machine learning and deep learning, to share his thoughts on the paper The Case for Bayesian Deep Learning...

The paper The Case for Bayesian Deep Learning is on arXiv.
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Source: Synced