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ericjang11's profile
Eric Jang 🇺🇸🇹🇼
Eric Jang 🇺🇸🇹🇼
Eric Jang  🇺🇸 🇹🇼
@ericjang11

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Eric Jang  🇺🇸 🇹🇼

@ericjang11

Research scientist at Robotics @ Google. Formerly at Pixar Animation Studios and Two Sigma Investments. Brown '16. I love science. All opinions are my own.

Joined January 2014

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    Eric Jang  🇺🇸 🇹🇼‏ @ericjang11 29 Nov 2019
    • Report Tweet

    Anything that can be implemented in JAX *will* be implemented in JAX. Here's a differentiable path tracer (and a tutorial!) Blog Post: https://blog.evjang.com/2019/11/jaxpt.html … Code: https://github.com/ericjang/pt-jax pic.twitter.com/KBHnCqka2e

    7:31 AM - 29 Nov 2019
    • 77 Retweets
    • 346 Likes
    • cloudpeterson rkchif Andrej Godina Swapneel Mehta Mike Ross Simon Osindero Isak Westerlund Syoyo Fujita 🌸  RayWa(Ray and Peace) yovas
    9 replies 77 retweets 346 likes
      1. Eric Jang  🇺🇸 🇹🇼‏ @ericjang11 29 Nov 2019
        • Report Tweet

        @tunabrain @sschoenholz @SingularMattrix

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      1. Eric Jang  🇺🇸 🇹🇼‏ @ericjang11 29 Nov 2019
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        Edit: Thanks to Stephan Hoyer, who pointed out that my benchmark only ran on 1 TPU core, whereas a Cloud TPU has 8 available. Updated post, chart and code to reflect that using the whole TPU does speed things up. My implementation could do a better job of parallelizing though.pic.twitter.com/7OZPfA4cSe

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      2. Akihiro Matsukawa‏ @amatsukawa 6 Dec 2019
        • Report Tweet
        Replying to @ericjang11

        So say I don’t care about running on TPUs (I know the pains of TF + TPUs all to well) what are some “killer features” of JAX over PyTorch?

        1 reply 0 retweets 1 like
      3. Eric Jang  🇺🇸 🇹🇼‏ @ericjang11 6 Dec 2019
        • Report Tweet
        Replying to @amatsukawa

        Vmap and pmap are the biggest ones that come to mind. I’ve heard anecdotally that JAX can be much faster on some workloads (don’t quote me). I expect pytorch devs to catch up eventually though.

        1 reply 0 retweets 3 likes
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      2. Alex Mordvintsev‏ @zzznah 29 Nov 2019
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        Replying to @ericjang11

        Wdyt about http://taichi.graphics  ?

        1 reply 0 retweets 8 likes
      3. Eric Jang  🇺🇸 🇹🇼‏ @ericjang11 29 Nov 2019
        • Report Tweet
        Replying to @zzznah

        Taichi is amazing. It is a few years ahead of existing DL frameworks in understanding that optimizing data structures is 90% of the battle. I’m playing with it on the side.

        0 replies 0 retweets 16 likes
      4. End of conversation
      1. New conversation
      2. mat kelcey‏ @mat_kelcey 29 Nov 2019
        • Report Tweet
        Replying to @ericjang11

        ( Jax gotchas link is a 404 )

        1 reply 0 retweets 0 likes
      3. 𝔊𝔴𝔢𝔯𝔫‏ @gwern 30 Nov 2019
        • Report Tweet
        Replying to @mat_kelcey @ericjang11

        (And typo in title.)

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      2. Ian Williamson‏ @ianadwilliamson 29 Nov 2019
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        Replying to @ericjang11

        Looking at flow control with JAX, do you know if there's eventually going to be support for native Python flow control? Or will it always be necessary to do things like np.where(condition, a, b)?

        1 reply 0 retweets 1 like
      3. Stephan Hoyer‏ @shoyer 29 Nov 2019
        • Report Tweet
        Replying to @ianadwilliamson @ericjang11

        You can use Python's native control flow (if you don't jit), lax.cond for control flow within jit, and also see jax.experimental.loops: https://jax.readthedocs.io/en/latest/jax.experimental.loops.html … As for auto-magically compiling Python control flow like Numba, I'm not sure that's really a good idea.

        1 reply 0 retweets 4 likes
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