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www.nowozin.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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www.johnmyleswhite.com
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| | | | | Introduction One of the things that set statistics apart from the rest of applied mathematics is an interest in the problems introduced by sampling: how can we learn about a model if we're given only a finite and potentially noisy sample of data? Although frequently important, the issues introduced by sampling can be a distraction when the core difficulties you face would persist even with access to an infinite supply of noiseless data. | |
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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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distill.pub
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| | | What we'd like to find out about GANs that we don't know yet. | ||
