BiPiSci13-10-14

💡 https://www.quantamagazine.org/the-atomic-theory-of-origami-20171031/

#Geometry #Mathematics #Origami #Phase_Transitions #Statistical_Physics #Topology

PhD program

http://sc.cs.cmu.edu/

🎞 Second-Order Phase Transitions: Beyond Landau-Ginzburg Theory
Zohar Komargodski (Weizmann)

http://media.physics.harvard.edu/video/html5/?id=COLLOQ_KOMARGODSKI_111416

🎞 Learning and Inference When There Is Little Data
Yasser Roudi (NTNU)

http://media.physics.harvard.edu/video/html5/?id=COLLOQ_ROUDI_050216

اولین #کارگاه #علم_داده:
"Big Data"

🗓 25آبان، 2 و 9 آذر 96
🕰 ساعت 9 الی 12:30
📍دانشكده فيزيك دانشگاه شهيد بهشتی

ثبت نام و اطلاعات تکمیلی:
http://sbuphysics.ir
http://rusherg.com

@sbu_physics

Percolation and cancer: The physics of network phase transitions can help illuminate the process of tumorigenesis

💡 http://www.nature.com/news/the-new-thermodynamics-how-quantum-physics-is-bending-the-rules-1.22937

1️⃣ But what *is* a Neural Network? | Deep learning, Part 1
🔗 https://www.aparat.com/v/XkQFy

2️⃣ Gradient descent, how neural networks learn | Deep learning, part 2
🔗 https://www.aparat.com/v/uvUxW

3️⃣ What is backpropagation and what is it actually doing? | Deep learning
🔗 https://www.aparat.com/v/EZ9RV

3️⃣*️⃣ Backpropagation calculus | Appendix to deep learning
🔗 https://www.aparat.com/v/0tSKg

#سمینارهای_هفتگی گروه سیستم‌های پیچیده و علم شبکه دانشگاه شهید بهشتی

🔹دوشنبه، ۱۵ آبان ماه، ساعت ۴:۰۰ - کلاس۱ دانشکده فیزیک دانشگاه شهید بهشتی.

@carimi

#سمینار_عمومی این هفته
کوانتوم؛ مغز و هوش مصنوعی

-۳شنبه ۱۶ آبان؛ ساعت ۱۶
-تالار ابن هیثم، دانشکده فیزیک

کانال انجمن علمی دانشجویی فیزیک
@sbu_physics

♦️ https://pulitzercenter.org/reporting/can-math-be-used-predict-outbreak

🔖 Variational Inference: A Review for Statisticians

David M. Blei, Alp Kucukelbir, Jon D. McAuliffe

🔗 https://arxiv.org/pdf/1601.00670

📌 ABSTRACT
One of the core problems of modern statistics is to approximate difficult-to-compute probability densities. This problem is especially important in Bayesian statistics, which frames all inference about unknown quantities as a calculation involving the posterior density. In this paper, we review variational inference (VI), a method from machine learning that approximates probability densities through optimization. VI has been used in many applications and tends to be faster than classical methods, such as Markov chain Monte Carlo sampling. The idea behind VI is to first posit a family of densities and then to find the member of that family which is close to the target. Closeness is measured by Kullback-Leibler divergence. We review the ideas behind mean-field variational inference, discuss the special case of VI applied to exponential family models, present a full example with a Bayesian mixture of Gaussians, and derive a variant that uses stochastic optimization to scale up to massive data. We discuss modern research in VI and highlight important open problems. VI is powerful, but it is not yet well understood. Our hope in writing this paper is to catalyze statistical research on this class of algorithms

One of the best reviews on the computational nature of life: causality, adaptation and phase transitions 👇🏻👇👇🏼