🔖 The assembly of prestige and status in networks
Daniel Larremore

http://tuvalu.santafe.edu/events/workshops/images/8/87/Larremore_CSSS_PrestigeNetworks.pdf

#CSSS2017

#سمینارهای_هفتگی گروه سیستم‌های پیچیده و علم شبکه دانشگاه شهید بهشتی

🔹دوشنبه، ۱۷ مهرماه، ساعت ۴:۰۰ - کلاس ٬۵ دانشکده فیزیک دانشگاه شهید بهشتی.

@carimi

🌀 https://www.quantamagazine.org/how-to-win-at-deep-learning-20171009/

🔹 Something that bothers me about deep neural nets
John D. Cook, PhD

https://www.johndcook.com/blog/2017/10/09/something-that-bothers-me-about-deep-neural-nets/

Overfitting happens when a model does too good a job of matching a particular data set and so does a poor job on new data. The way traditional statistical models address the danger of overfitting is to limit the number of parameters. For example, you might fit a straight line (two parameters) to 100 data points, rather than using a 99-degree polynomial that could match the input data exactly and probably do a terrible job on new data. You find the best fit you can to a model that doesn’t have enough flexibility to match the data too closely.

Deep neural networks have enough parameters to overfit the data, but there are various strategies to keep this from happening. A common way to avoid overfitting is to deliberately do a mediocre job of fitting the model.

When it works well, the shortcomings of the optimization procedure yield a solution that differs from the optimal solution in a beneficial way. But the solution could fail to be useful in several ways. It might be too far from optimal, or deviate from the optimal solution in an unhelpful way, or the optimization method might accidentally do too good a job.

It a nutshell, the disturbing thing is that you have a negative criteria for what constitutes a good solution: one that’s not too close to optimal. But there are a lot of ways to not be too close to optimal. In practice, you experiment until you find an optimally suboptimal solution, i.e. the intentionally suboptimal fit that performs the best in validation.

صوت سخنرانی عباس کریمی با موضوع:
🎧 Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models

💲 PhD studentships in Statistical Applied Mathematics at the University of Bath

www.bath.ac.uk/samba
https://www.findaphd.com/search/PhDDetails.aspx?CAID=2444

⌨️ Starts October 10th

Advanced MATLAB for Scientific Computing.

Sign up/learn more about advanced graphics and tools.
http://online.stanford.edu/course/advanced-matlab-scientific-computing

🤷‍♂ http://jarisaramaki.fi/2017/10/09/paper-writing-for-phd-students-part-3-the-importance-of-focus/?fb_action_ids=10155211234076973&fb_action_types=news.publishes&fb_source=other_multiline&action_object_map=%5B1549889575105575%5D&action_type_map=%5B%22news.publishes%22%5D&action_ref_map=%5B%5D

📄 Novel and topical business news and their impact on stock market activity
Takayuki Mizuno, Takaaki Ohnishi and Tsutomu Watanabe

🔗 https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-017-0123-7

📌 ABSTRACT
We propose an indicator to measure the degree to which a particular news article is novel, as well as an indicator to measure the degree to which a particular news item attracts attention from investors. The novelty measure is obtained by comparing the extent to which a particular news article is similar to earlier news articles, and an article is regarded as novel if there was no similar article before it. On the other hand, we say a news item receives a lot of attention and thus is highly topical if it is simultaneously reported by many news agencies and read by many investors who receive news from those agencies. The topicality measure for a news item is obtained by counting the number of news articles whose content is similar to an original news article but which are delivered by other news agencies. To check the performance of the indicators, we empirically examine how these indicators are correlated with intraday financial market indicators such as the number of transactions and price volatility. Specifically, we use a dataset consisting of over 90 million business news articles reported in English and a dataset consisting of minute-by-minute stock prices on the New York Stock Exchange and the NASDAQ Stock Market from 2003 to 2014, and show that price volatility, transaction volumes, and the number of transactions exhibited a significant response to a news article when it was novel and topical.

Keywords
#novelty #topicality #exogenous #shocks #financial_markets #business_news

💡 https://www.countbayesie.com/blog/2017/5/9/kullback-leibler-divergence-explained?utm_content=buffer0a90f&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

🎞 https://www.aparat.com/v/Ucr8n

🔹 https://www.countbayesie.com/blog/2015/3/3/6-amazing-trick-with-monte-carlo-simulations

#سمینارهای_هفتگی گروه سیستم‌های پیچیده و علم شبکه دانشگاه شهید بهشتی

🔹دوشنبه، ۲۴ مهرماه، ساعت ۴:۰۰ - کلاس۱ دانشکده فیزیک دانشگاه شهید بهشتی.

@carimi

فیلم ارائه عباس کریمی (قسمت اول):

🎞 Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models

🎙 رادیوفیزیک
@radiophysicsir

فیلم ارائه عباس کریمی (قسمت دوم):

🎞 Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models

🎙 رادیوفیزیک
@radiophysicsir