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Responding To Complexity: A Discussion With Yaneer Bar-Yam
🔖 A high-bias, low-variance introduction to Machine Learning for physicists
Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G.R. Day, Clint Richardson, Charles K. Fisher, David J. Schwab
https://arxiv.org/pdf/1803.08823
Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, and generalization before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists maybe able to contribute. (Notebooks are available at this https URL: https://physics.bu.edu/~pankajm/MLnotebooks.html)
Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G.R. Day, Clint Richardson, Charles K. Fisher, David J. Schwab
https://arxiv.org/pdf/1803.08823
Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, and generalization before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists maybe able to contribute. (Notebooks are available at this https URL: https://physics.bu.edu/~pankajm/MLnotebooks.html)
💎 Bill Rand's new tutorial, Fundamentals of NetLogo, has officially launched! Take the new tutorial and start using it in your work!
http://netlogo.complexityexplorer.org/
http://netlogo.complexityexplorer.org/
🔸 The Key to Creating Cost-Effective Experiments at Scale - Behavioral Scientist:
http://behavioralscientist.org/the-key-to-creating-engaging-and-cost-effective-experiments-at-scale/
http://behavioralscientist.org/the-key-to-creating-engaging-and-cost-effective-experiments-at-scale/
Behavioral Scientist
The Key to Creating Cost-Effective Experiments at Scale
How can we build large-scale, cost-effective experiments that people want to participate in?
💻 A Free Oxford Course on Deep Learning: Cutting Edge Lessons in Artificial Intelligence
Slides and Videos:
🔗 https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/
Slides and Videos:
🔗 https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/
Forwarded from TCNS 2018
این گروه جهت اطلاع رسانی و معرفی برنامههای سمپوزیوم علوم اعصاب نظری و محاسباتی ایجاد شده است. این همایش با همکاری دانشگاه فرایبورگ آلمان، در تاریخ بیست و هفتم و بیست و هشتم فروردین ماه سال 1397 در محل دانشکده مهندسی برق و کامپیوتر دانشگاه تهران، برگزار خواهد شد.
https://goo.gl/Tgm6wX
https://goo.gl/Tgm6wX
TCNS 2018
این گروه جهت اطلاع رسانی و معرفی برنامههای سمپوزیوم علوم اعصاب نظری و محاسباتی ایجاد شده است. این همایش با همکاری دانشگاه فرایبورگ آلمان، در تاریخ بیست و هفتم و بیست و هشتم فروردین ماه سال 1397 در محل دانشکده مهندسی برق و کامپیوتر دانشگاه تهران، برگزار خواهد…
📌 علاقمندان جهت ثبت نام در این همایش، رزومه خود را به نشانی زیر ارسال کنند:
ut.tcns2018@gmail.com
ut.tcns2018@gmail.com
💭 So many great mathematicians, from Archimedes, Bernoulli, Lagrange, to Pontryagin and Lions have put their stamp on "optimization" and "optimal control" problems. Here is a great summary.
www.unige.ch/~gander/Preprints/LagrangeTalk.pdf
www.unige.ch/~gander/Preprints/LagrangeTalk.pdf
✅ When mathematicians write a review on statistical physics theories it gets pretty exciting:
🔗 arxiv.org/pdf/1803.11132.pdf
🔗 arxiv.org/pdf/1803.11132.pdf
🎞 Machine Learning Summer School 2017 Tübingen
Max Planck Institute for Intelligent Systems
33 Videos:
https://www.youtube.com/watch?v=XLHB-Aktxw0&list=PLqJm7Rc5-EXFUOvoYCdKikfck8YeUCnl9
Max Planck Institute for Intelligent Systems
33 Videos:
https://www.youtube.com/watch?v=XLHB-Aktxw0&list=PLqJm7Rc5-EXFUOvoYCdKikfck8YeUCnl9
YouTube
What is Machine Learning - Bernhard Schölkopf - MLSS 2017
This is Bernhard Schölkopf's Introduction to Machine Learning, given at the Machine Learning Summer School 2017, held at the Max Planck Institute for Intelligent Systems, in Tübingen, Germany, from 19-30 June 2017.
Slides for this talk, in pdf format, as…
Slides for this talk, in pdf format, as…
🔸 An Introduction to Graphical Lasso
Bo Chang
Graphical Models Reading Group
May 15, 2015
Slides:
https://www.stat.ubc.ca/~bchang/gmrg/files/Bo_05152015.pdf
Bo Chang
Graphical Models Reading Group
May 15, 2015
Slides:
https://www.stat.ubc.ca/~bchang/gmrg/files/Bo_05152015.pdf
Forwarded from انجمن علمی فیزیک بهشتی (SBU)
#سمینار_عمومی این هفته
Atoms in Molecular/Crystals From Being to Interacting
-۳شنبه ۲۱ فروردین؛ ساعت ۱۵:۱۵
-تالار ابن هیثم، دانشکده فیزیک
کانال انجمن علمی دانشجویی فیزیک
@sbu_physics
Atoms in Molecular/Crystals From Being to Interacting
-۳شنبه ۲۱ فروردین؛ ساعت ۱۵:۱۵
-تالار ابن هیثم، دانشکده فیزیک
کانال انجمن علمی دانشجویی فیزیک
@sbu_physics
Seventh International Conference on Complex Networks & Their Applications
Submission deadline: September 04, 2018
https://www.complexnetworks.org/
Submission deadline: September 04, 2018
https://www.complexnetworks.org/
Success in books: a big data approach to bestsellers
Burcu Yucesoy ,XindiWang , Junming Huang and Albert-László Barabási
https://link.springer.com/content/pdf/10.1140%2Fepjds%2Fs13688-018-0135-y.pdf
Burcu Yucesoy ,XindiWang , Junming Huang and Albert-László Barabási
https://link.springer.com/content/pdf/10.1140%2Fepjds%2Fs13688-018-0135-y.pdf
🔖 From Bitcoin to Bitcoin Cash: a network analysis
Marco Alberto Javarone, Craig Steven Wright
🔗 arxiv.org/pdf/1804.02350v1.pdf
In the last years, Bitcoins and Blockchain technologies are gathering a wide attention from different scientific communities. Notably, thanks to widespread industrial applications and to the continuous introduction of cryptocurrencies, even the public opinion is increasing its attention towards this field. The underlying structure of these technologies constitutes one of their core concepts. In particular, they are based on peer-to-peer networks. Accordingly, all nodes lay at the same level, so that there is no place for privileged actors as, for instance, banking institutions in classical financial networks. In this work, we perform a preliminary investigation on two networks, i.e. the Bitcoin network and the Bitcoin Cash network. Notably, we aim to analyze their global structure and to evaluate if they are provided with a small-world behavior. Results suggest that the principle known as 'fittest-gets-richer', combined with a continuous increasing of connections, might constitute the mechanism leading these networks to reach their current structure. In addition, further observations open the way to new investigations into this direction.
Marco Alberto Javarone, Craig Steven Wright
🔗 arxiv.org/pdf/1804.02350v1.pdf
In the last years, Bitcoins and Blockchain technologies are gathering a wide attention from different scientific communities. Notably, thanks to widespread industrial applications and to the continuous introduction of cryptocurrencies, even the public opinion is increasing its attention towards this field. The underlying structure of these technologies constitutes one of their core concepts. In particular, they are based on peer-to-peer networks. Accordingly, all nodes lay at the same level, so that there is no place for privileged actors as, for instance, banking institutions in classical financial networks. In this work, we perform a preliminary investigation on two networks, i.e. the Bitcoin network and the Bitcoin Cash network. Notably, we aim to analyze their global structure and to evaluate if they are provided with a small-world behavior. Results suggest that the principle known as 'fittest-gets-richer', combined with a continuous increasing of connections, might constitute the mechanism leading these networks to reach their current structure. In addition, further observations open the way to new investigations into this direction.