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--- | ||
--- | ||
References | ||
========== | ||
@incollection{ha2018worldmodels, | ||
title = {Recurrent World Models Facilitate Policy Evolution}, | ||
author = {Ha, David and Schmidhuber, J{\"u}rgen}, | ||
booktitle = {Advances in Neural Information Processing Systems 31}, | ||
pages = {2451--2463}, | ||
year = {2018}, | ||
publisher = {Curran Associates, Inc.}, | ||
url = {https://papers.nips.cc/paper/7512-recurrent-world-models-facilitate-policy-evolution}, | ||
note = "\url{https://worldmodels.github.io}", | ||
} | ||
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@misc{cuccu2019playingatarineurons, | ||
title={Playing Atari with Six Neurons}, | ||
author={Giuseppe Cuccu and Julian Togelius and Philippe Cudre-Mauroux}, | ||
year={2019}, | ||
eprint={1806.01363}, | ||
archivePrefix={arXiv}, | ||
primaryClass={cs.LG}, | ||
url={https://arxiv.org/abs/1806.01363}, | ||
} | ||
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@misc{junyent2018improvingwidthbasedplanningcompact, | ||
title={Improving width-based planning with compact policies}, | ||
author={Miquel Junyent and Anders Jonsson and Vicenç Gómez}, | ||
year={2018}, | ||
eprint={1806.05898}, | ||
archivePrefix={arXiv}, | ||
primaryClass={cs.AI}, | ||
url={https://arxiv.org/abs/1806.05898}, | ||
} | ||
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@article{Silver2017MasteringTG, | ||
title={Mastering the game of Go without human knowledge}, | ||
author={David Silver and Julian Schrittwieser and Karen Simonyan and Ioannis Antonoglou and Aja Huang and Arthur Guez and Thomas Hubert and Lucas baker and Matthew Lai and Adrian Bolton and Yutian Chen and Timothy P. Lillicrap and Fan Hui and L. Sifre and George van den Driessche and Thore Graepel and Demis Hassabis}, | ||
journal={Nature}, | ||
year={2017}, | ||
volume={550}, | ||
pages={354-359}, | ||
url={https://api.semanticscholar.org/CorpusID:205261034} | ||
} | ||
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@misc{besold2017neuralsymboliclearningreasoningsurvey, | ||
title={Neural-Symbolic Learning and Reasoning: A Survey and Interpretation}, | ||
author={Tarek R. Besold and Artur d'Avila Garcez and Sebastian Bader and Howard Bowman and Pedro Domingos and Pascal Hitzler and Kai-Uwe Kuehnberger and Luis C. Lamb and Daniel Lowd and Priscila Machado Vieira Lima and Leo de Penning and Gadi Pinkas and Hoifung Poon and Gerson Zaverucha}, | ||
year={2017}, | ||
eprint={1711.03902}, | ||
archivePrefix={arXiv}, | ||
primaryClass={cs.AI}, | ||
url={https://arxiv.org/abs/1711.03902}, | ||
} | ||
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@misc{sanchezgonzalez2018graphnetworkslearnablephysics, | ||
title={Graph networks as learnable physics engines for inference and control}, | ||
author={Alvaro Sanchez-Gonzalez and Nicolas Heess and Jost Tobias Springenberg and Josh Merel and Martin Riedmiller and Raia Hadsell and Peter Battaglia}, | ||
year={2018}, | ||
eprint={1806.01242}, | ||
archivePrefix={arXiv}, | ||
primaryClass={cs.LG}, | ||
url={https://arxiv.org/abs/1806.01242}, | ||
} | ||
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@misc{rocktäschel2017endtoenddifferentiableproving, | ||
title={End-to-End Differentiable Proving}, | ||
author={Tim Rocktäschel and Sebastian Riedel}, | ||
year={2017}, | ||
eprint={1705.11040}, | ||
archivePrefix={arXiv}, | ||
primaryClass={cs.NE}, | ||
url={https://arxiv.org/abs/1705.11040}, | ||
} | ||
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@misc{higgins2018scanlearninghierarchicalcompositional, | ||
title={SCAN: Learning Hierarchical Compositional Visual Concepts}, | ||
author={Irina Higgins and Nicolas Sonnerat and Loic Matthey and Arka Pal and Christopher P Burgess and Matko Bosnjak and Murray Shanahan and Matthew Botvinick and Demis Hassabis and Alexander Lerchner}, | ||
year={2018}, | ||
eprint={1707.03389}, | ||
archivePrefix={arXiv}, | ||
primaryClass={stat.ML}, | ||
url={https://arxiv.org/abs/1707.03389}, | ||
} |
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--- | ||
layout: post | ||
title: Compressed sensing and machine learning | ||
categories: | ||
- tutorial | ||
--- | ||
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This post is about; | ||
|
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--- | ||
title: "AutoInt: Automatic integration" | ||
title: "Automatic integration" | ||
subtitle: "Where is it?" | ||
categories: | ||
- "proposal" | ||
scholar: | ||
bibliography: "auto-int.bib" | ||
--- | ||
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I work in machine learning, and everyday I see the awesome utility of automatic differentiation. | ||
Within machine learning, automatic differentiation proves its awesome utility. | ||
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However, as far as I know, there is no analog for automatic integration. | ||
Why not? | ||
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The key to automatic differentiation is the chain rule. | ||
It allows us to compute the derivative of a function by breaking it down into a sequence of elementary operations. | ||
Calculating their derivatives independently, we can then combine them to get the derivative of the original function. | ||
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## Bibliography | ||
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{% bibliography --cited %} |
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title: Jax | ||
subtitle: Reading the docs | ||
permalink: jax-stack | ||
categories: | ||
- tutorial | ||
--- | ||
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_posts/inbetween-posts/2018-03-26-regulating-markets-to-remove-inefficiencies.md
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