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Normalizing Flows Tutorial, Part 1: Distributions and Determinants I'm looking for help translate these posts into different languages! Please email me at <myfirstname><mylastname>2004<at>gmail.com if you are interested. Xiaoyi Yin (尹肖贻) has kindly translated this post into Chinese (中文). Jaeseong You has kindly translated this post into Korean (한국어) Kieran Didi has kindly translated this post into
The 30th annual Neural Information Processing Systems (NIPS) conference took place in Barcelona in early December. In this post, I share my thoughts on the conference. Let me know your thoughts in the comments section! Basic outline: State of AI Research and trends in 2016 Talks and papers that I liked Predictions for 2017 Miscellaneous notes Academic, industry, and public interest in Artificial I
Tutorial: Categorical Variational Autoencoders using Gumbel-Softmax In this post, I discuss our recent paper, Categorical Reparameterization with Gumbel-Softmax, which introduces a simple technique for training neural networks with discrete latent variables. I'm really excited to share this because (1) I believe it will be quite useful for a variety of Machine Learning research problems, (2) this
This post was first published on 12/29/15, and has since been migrated to Blogger. This is a tutorial on implementing Ian Goodfellow's Generative Adversarial Nets paper in TensorFlow. Adversarial Nets are a fun little Deep Learning exercise that can be done in ~80 lines of Python code, and exposes you (the reader) to an active area of deep learning research (as of 2015): Generative Modeling! Code
A Beginner's Guide to Variational Methods: Mean-Field Approximation Variational Bayeisan (VB) Methods are a family of techniques that are very popular in statistical Machine Learning. VB methods allow us to re-write statistical inference problems (i.e. infer the value of a random variable given the value of another random variable) as optimization problems (i.e. find the parameter values that mini
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