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Probabilistic Machine Learning in the Age of Deep Learning: New Perspectives for Gaussian Processes, Bayesian Optimization an...
| | dustintran.com
Two papers released on arXiv, "Operator Variational Inference" and "Model Criticism for Bayesian Causal Inference" | Dustin T...
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| | Two papers of mine were released today on arXiv. Operator variational inference, in collaboration with Rajesh Ranganath, Jaan Altosaar, and David Blei. Model criticism for Bayesian causal inference, in collaboration with Francisco Ruiz, Susan Athey, and David Blei. Last week, I gave a talk at OpenAI on operator variational inference and Edward. I can now release those slides online. Operator variational inference Operator VI is a paper I'm really excited about. It is at NIPS this year. Most directly, it'...
| | christopher-beckham.github.io
EDM diffusion models - a Jupyter implementation, and how they are implemented in practice | Christopher Beckham, PhD
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| | I wrote a self-contained implementation of NVIDIA's EDM diffusion model in a Jupyter notebook, as well as its associated sampling algorithms. I also discuss the rather confusing names used for real-world implementations of those algorithms.
| | louiskirsch.com
Theories of Intelligence (2/2): Active Inference | Louis Kirsch
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| | We look at Active Inference, a theoretical formulation of perception and action from neuroscience that can explain many phenomena in the brain. It aims to explain the behavior of cells, organs, animals, humans, and entire species. This article is geared towards machine learning researchers familiar with probabilistic modeling and reinforcement learning.
| | dennybritz.com
Recurrent Neural Networks Tutorial, Part 3 - Backpropagation Through Time and Vanishing Gradients ยท Denny's Blog
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| This the thirdpart of the Recurrent Neural Network Tutorial.