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Eric Jang

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Eric Jang | blog.evjang.com Reviews
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Technology, A.I., Careers
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1 eric jang
2 basic outline
3 miscellaneous notes
4 superhuman speech transcription
5 superhuman translation
6 superhuman lip reading
7 gans
8 deep rl
9 bayesian deep learning
10 symmetrized variational inference
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Eric Jang | blog.evjang.com Reviews

https://blog.evjang.com

Technology, A.I., Careers

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1

Eric Jang: September 2016

http://blog.evjang.com/2016_09_01_archive.html

Technology, A.I., Careers. Tuesday, September 6, 2016. Riemann Summation and Physics Simulation are Statistically Biased. Riemann summation is the simplest method for numerically approximating integrals. in which you chop up the domain into equally sized segments and add a bunch of rectangles together. Int f(x) dx sim sum i k f(x) Delta x$. This technique also comes up in Euler integration for simulating physics:. Delta x and = int dot{x}(t) dt. Sim dot{x}(t) Delta t. The good news is that $V i = 0$, and...

2

Eric Jang: June 2016

http://blog.evjang.com/2016_06_01_archive.html

Technology, A.I., Careers. Saturday, June 25, 2016. Adversarial Exploration Policies for Robust Model Learning. This post was first published on 05/14/16, and has since been migrated to Blogger. Brown University requires S.c.B students to take a capstone. Course that "studies a current topic in depth to produce a culminating artifact such as a paper of software project". Robustness of $ hat{f} tau$ is crucial. If the robot (such as an autonomous vehicle) spends most of its exploration time in a small...

3

Eric Jang: Riemann Summation and Physics Simulation are Statistically Biased

http://blog.evjang.com/2016/09/riemann-bias.html

Technology, A.I., Careers. Tuesday, September 6, 2016. Riemann Summation and Physics Simulation are Statistically Biased. Riemann summation is the simplest method for numerically approximating integrals. in which you chop up the domain into equally sized segments and add a bunch of rectangles together. Int f(x) dx sim sum i k f(x) Delta x$. This technique also comes up in Euler integration for simulating physics:. Delta x and = int dot{x}(t) dt. Sim dot{x}(t) Delta t. The good news is that $V i = 0$, and...

4

Eric Jang: November 2016

http://blog.evjang.com/2016_11_01_archive.html

Technology, A.I., Careers. Tuesday, November 8, 2016. Tutorial: Categorical Variational Autoencoders using Gumbel-Softmax. Not supported by viewer]. Not supported by viewer]. Not supported by viewer]. Not supported by viewer]. In this post, I discuss our recent paper, Categorical Reparameterization with Gumbel-Softmax. If you want categorical features in your neural nets, just let. Sample = softmax( logits gumbel noise)/temperature). You can find the code for this article here. The workhorse of Deep Lear...

5

Eric Jang: A Beginner's Guide to Variational Methods: Mean-Field Approximation

http://blog.evjang.com/2016/08/variational-bayes.html

Technology, A.I., Careers. Sunday, August 7, 2016. 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 minimize some objective function). If you forgot some stuff...

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Eric Jang

Technology, A.I., Careers. Monday, January 2, 2017. Summary of NIPS 2016. 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! State of AI Research and trends in 2016. Talks and papers that I liked. State of Artificial Intelligence Research: 2016 Trends. There was a 50% percent increase in NIPS registrations from last year:. The research top...

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