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andrewjaffe.net | ||
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dustintran.com
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| | | | | One aspect I always enjoy about machine learning is that questions often go back to the basics. The field essentially goes into an existential crisis every dozen years-rethinking our tools and asking foundational questions such as "why neural networks" or "why generative models".1 This was a theme in my conversations during NIPS 2016 last week, where a frequent topic was on the advantages of a Bayesian perspective to machine learning. Not surprisingly, this appeared as a big discussion point during the p... | |
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deepai.org
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| | | | | Bayesian inference refers to the application of Bayes' Theorem in determining the updated probability of a hypothesis given new information. | |
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alexanderetz.com
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| | | | | [This post has been updated and turned into a paper to be published in AMPPS] Much of the discussion in psychology surrounding Bayesian inference focuses on priors. Should we embrace priors, or should we be skeptical? When are Bayesian methods sensitive to specification of the prior, and when do the data effectively overwhelm it? Should... | |
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www.danieldjohnson.com
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| | | Writeup for my first major machine learning project. | ||
