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Theo Weber proslijedio/la je Tweet
We are organizing a workshop on Causal learning for Decision Making at
@iclr_conf along with@rosemary_ke@DeepSpiker@theophaneweber, Jovana Mitrovic,@janexwang, Stefan and@csilviavr. https://sites.google.com/view/causal-learning-icrl2020/home …@MILAMontreal Consider submitting your work!Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Theo Weber proslijedio/la je Tweet
Check out our extensive review paper on normalizing flows! This paper is the product of years of thinking about flows: it contains everything we know about them, and many new insights. With
@eric_nalisnick,@DeepSpiker,@shakir_za,@balajiln. http://arxiv.org/abs/1912.02762 Thread
https://twitter.com/DeepSpiker/status/1202868429780336640 …
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Theo Weber proslijedio/la je Tweet
My PhD thesis is now available on arXiv: Neural Density Estimation and Likelihood-free Inference https://arxiv.org/abs/1910.13233 There's a lot in it for those interested in probabilistic modelling with normalizing flows, and in likelihood-free inference using machine learning. (cont.)https://twitter.com/driainmurray/status/1155054417747615744 …
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Really excited to share this new paper with Lars Buesing and Nicolas Heess: https://arxiv.org/abs/1910.06862 By following connections between RL and inference, we inspire ourselves from alpha-zero style MCTS (for optimization) and develop a tree-search based alg. for inference.
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Theo Weber proslijedio/la je Tweet
Great new paper by Lars, Nicholas and
@theophaneweber expanding the tools we have for doing approximate inference in discrete probabilistic models using MCTS
We need more such papers connecting our understanding of RL and inference to important models
https://arxiv.org/abs/1910.06862 Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Theo Weber proslijedio/la je Tweet
Wanna play around with SPIRAL but the installation seems complicated? I've just built a Docker image to make the experience as hassle-free as possible. To get the agent up and running on your machine follow the instructions here: https://github.com/ddtm/spiral-docker … Have fun!https://twitter.com/yaroslav_ganin/status/1180120687131926528 …
0:07Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Theo Weber 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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Theo Weber proslijedio/la je Tweet
We are excited to release Behaviour Suite for Reinforcement Learning, or ‘bsuite’ – a collection of carefully-designed experiments that investigate core capabilities of RL agents GitHub: http://github.com/deepmind/bsuite Paper: https://arxiv.org/abs/1908.03568v1 …
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Theo Weber proslijedio/la je Tweet
Really excited to release
#bsuite to the public! - Clear, scalable experiments that test core#RL capabilities. - Works with OpenAI gym, Dopamine. - Detailed colab analysis - Automated LaTeX appendix Example report: http://bit.ly/bsuite-agents https://twitter.com/DeepMind/status/1161318318697005056 …pic.twitter.com/IHuu5Gjmmk
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Theo Weber proslijedio/la je Tweet
I'm developing a pet peeve around slides showing children learning things "one/few-shot", allegedly super magically. A child does not have a few months/years of experience. It has about 500 million years of experience.
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I'm sure I am missing many references so if you believe I missed something please don't hesitate to ping me! (Similarly if you see anything dodgy mathematically)
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And nevermind on the second paper - it will be on arxiv soon enough :)
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Also recently published two papers: TD-VAE (https://arxiv.org/abs/1806.03107 ), with Karol Gregor,
@gpapamak, and friends. How can an agent build a temporally abstract model of the world, and use it to compute a 'belief state' - a representation of the agent's uncertainty.(Oral at ICLR)Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Helps to connect and understand current algorithms, and hopefully offers a turnkey methodology to derive new ones in increasingly structured models. Find out more at https://arxiv.org/pdf/1901.01761.pdf …, or come chat during AISTATS! Feedback very welcome :)
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New paper out! It deals with credit assignment in stochastic computation graphs. Attempts to unify and generalize collection of distinct results - how to see DPG, actor-critic, variance reduction in stochastic nets, action-conditional baselines,synth grads.. all from common lens?
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A must-read review of the literature on model-based RL, world models using DL and how it compares to analogues in human cognition (mental sim, imagination). Lots of work left, in particular regarding finding the right abstractions, deal with time, and building partial models!https://twitter.com/jhamrick/status/1082054065356529665 …
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Theo Weber proslijedio/la je Tweet
I hear the GOP thinks women dancing are scandalous. Wait till they find out Congresswomen dance too!
Have a great weekend everyone :)pic.twitter.com/9y6ALOw4F6Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
The barber's paradox of papers (kind of).https://twitter.com/chethan/status/1076081083001769985 …
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Theo Weber proslijedio/la je Tweet
My personal journey into Belief Propagation is narrated here https://ucla.in/2Qg0Rfs , a chapter we had to discard from
#Bookofwhy for space considerations. But many find it educational, especially the story about Bill Gates.Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Theo Weber proslijedio/la je Tweet
Our poster "Deep Learning for Classical Japanese Literature" is here. Thank you
#Neurips4creativitypic.twitter.com/ywzns17iux
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