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[4/4] We hope that this class of estimators will find exciting machine applications! The paper is available online at http://bayesiandeeplearning.org/2019/papers/76.pdf …
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[3/4] It has low variance, similar to the reparameterization gradients, and works with non-differentiable functions and discrete distributions, just like REINFORCE. The downside is the higher computational complexity that grows with the number of parameters.
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[2/4] Measure valued derivatives are a class of Monte Carlo gradient estimators that has been introduced 30 years ago by Georg Pflug, but is almost unknown in the machine learning community.
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[1/4] I will be talking about Measure Valued Derivatives for Approximate Bayesian Inference, our joint work with @elaClaudia
@shakir_za@AndriyMnih, at the Bayesian Deep Learning workshop at 16:05 tomorrow.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
I’m at
#NeurIPS2019 this week. Let me know if you’d like to catch up!Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Cool paper from
@artygadetsky@k_struminsky: REBAR-like control variates for Plackett-Luce, a distribution over permutations, with application to learning of causal graphs. Check it out!https://twitter.com/bayesgroup/status/1199023536653950976 …
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Michael Figurnov proslijedio/la je Tweet
The code reproducing the experiments is this paper is now available at: https://github.com/deepmind/mc_gradients …https://twitter.com/shakir_za/status/1143802522299244545 …
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Michael Figurnov proslijedio/la je Tweet
#AlphaStar@nature: Grandmaster level as all 3@StarCraft races on http://Battle.net , w/ a pro approved interface (camera & APM limits). 2 years ago I thought this was impossible! How? Imitation learning (Diamond) -> multiagent League (Grandmaster) https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning …pic.twitter.com/rcOrRsZ838Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Michael Figurnov proslijedio/la je Tweet
A new paper on tweaking SPIRAL (http://proceedings.mlr.press/v80/ganin18a.html …). What's new: • Spectral normalization of discriminator (Miyato, 18) ⇒ sharper images • Reward shaping by (Ng, 99) ⇒ longer episodes • In-painting instead of stacking ⇒ better reconstructions Lots of nice samples :)https://twitter.com/arkitus/status/1179760320685969409 …
0:40Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Michael Figurnov proslijedio/la je Tweet
Thrilled to be able to share what I've been working on for the last year - solving the fundamental equations of quantum mechanics with deep learning! https://arxiv.org/abs/1909.02487
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Michael Figurnov proslijedio/la je Tweet
We’re excited to release episodes 1 - 4 of the
#DMpodcast! Get the inside track on some of the big questions and challenges the field is wrestling with today. No need to be an expert - the amazing@FryRsquared speaks to the people behind the science.http://deepmind.com/podcastPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Michael Figurnov proslijedio/la je Tweet
Really excited to share our latest paper in
@nature today on machine learning for health data to make early predictions of acute kidney injury. Has been an amazing journey over the last 2 years and with an amazing set of people. https://www.nature.com/articles/s41586-019-1390-1 …pic.twitter.com/XhMhdjArCb
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Michael Figurnov proslijedio/la je Tweet
After a short delay, the code in a notebook to reproduce the graphs in section 3 of our paper (https://arxiv.org/abs/1906.10652 ) is online. More to be come soon. See thread above
. https://github.com/deepmind/mc_gradients …
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Michael Figurnov proslijedio/la je Tweet
For anyone interested in constrained optimisation with DL models (e.g. as in https://arxiv.org/abs/1810.00597 ), we just released a few handy tools to deal with inequality constraints for Sonnet (http://tiny.cc/a9va9y ). Thanks
@fabiointheuk !#Sonnet#ConstrainedOptimisationHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Michael Figurnov proslijedio/la je TweetHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Michael Figurnov proslijedio/la je Tweet
Exited to share our new paper: 'Monte Carlo Gradient Estimation in Machine Learning', with @elaClaudia
@mfigurnov@AndriyMnih. It reviews of all the things we know about computing gradients of probabilistic functions. https://arxiv.org/abs/1906.10652
Thread
pic.twitter.com/2eTPsFO7mZ
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Michael Figurnov proslijedio/la je Tweet
Right now
@dmolch111,@senya_ashuha,@andrew_atanov and Oleg Ivanov with@mfigurnov present their works at#ICLR2019. Catch then while you can! - The Deep Weight Prior, #48 - Variance Networks, #72 - VAE with Arbitrary Conditioning, #74pic.twitter.com/KsNRcwKnlF
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Michael Figurnov proslijedio/la je Tweet
Our new blog post overviews unsupervised learning, a paradigm for creating artificial intelligence that learns about data without a particular task in mind. Read more about how we might teach computers to learn for the sake of learning:http://deepmind.com/blog/unsupervised-learning …
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Michael Figurnov proslijedio/la je Tweet
Yesterday
@mfigurnov successfully defended his PhD thesis! Congratulations!pic.twitter.com/NqxwTH0vtn
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Michael Figurnov proslijedio/la je Tweet
Likelihood is a great loss fn, it's all about the space you measure it in! Our latest work on hierarchical AR image models (w/
@JeffreyDeFauw, Karen Simonyan): https://arxiv.org/abs/1903.04933 We generated 128x128 & 256x256 samples for all ImageNet classes: https://bit.ly/2FJkvhJ (1/2)pic.twitter.com/4SsaOlqzV6
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