MATHEMATICS OF
LARGE NETWORKS
SUMMER SCHOOL

A P P L I C A T I O N D E A D L I N E : F E B 1 , 2 0 2 2

Quantifying Relevance in Learning and Inference

Review paper by Matteo Marsili, Yasser Roudi

https://arxiv.org/pdf/2202.00339

Learning is a distinctive feature of intelligent behaviour. High-throughput experimental data and Big Data promise to open new windows on complex systems such as cells, the brain or our societies. Yet, the puzzling success of Artificial Intelligence and Machine Learning shows that we still have a poor conceptual understanding of learning. These applications push statistical inference into uncharted territories where data is high-dimensional and scarce, and prior information on "true" models is scant if not totally absent. Here we review recent progress on understanding learning, based on the notion of "relevance". The relevance, as we define it here, quantifies the amount of information that a dataset or the internal representation of a learning machine contains on the generative model of the data. This allows us to define maximally informative samples, on one hand, and optimal learning machines on the other. These are ideal limits of samples and of machines, that contain the maximal amount of information about the unknown generative process, at a given resolution (or level of compression). Both ideal limits exhibit critical features in the statistical sense: Maximally informative samples are characterised by a power-law frequency distribution (statistical criticality) and optimal learning machines by an anomalously large susceptibility. The trade-off between resolution (i.e. compression) and relevance distinguishes the regime of noisy representations from that of lossy compression. These are separated by a special point characterised by Zipf's law statistics. This identifies samples obeying Zipf's law as the most compressed loss-less representations that are optimal in the sense of maximal relevance. Criticality in optimal learning machines manifests in an exponential degeneracy of energy levels, that leads to unusual thermodynamic properties.

A review/perspective of how our statistical field theory of information dynamics has been applied to better understand a variety of empirical complex systems.

https://t.co/0EtLPsf7LI

Mathematics Behind Critical Phenomena:
https://youtu.be/Ei7EMfdpCDc

Kalle Kytölä
Department of Mathematics and Systems Analysis

One #PhD opening to study complementarity mechanisms in networks #tudelft, sponsored by #NWO_Science.

This line of work is at the intersection of Network Science, Machine Learning, and Science of Science. Details at https://t.co/fPzLT8MgGv

A postdoc Researcher position open on optimization and machine learning fields! For details: https://t.co/OftbgNH0Oj

We offer a #postdoc position on network dynamics and modeling! Spread the word! The salary is expected to increase dramatically soon :)

Rachith Aiyappa & @yy shows social network models with a weighted belief network within each individual can unify simple and contagion dynamics
Very cool
#NetSciX2022 @NetSciX2022 https://t.co/AmCbop5wea

Here’s a mathematical take on variational inference that might be interesting to statistical physics types: https://t.co/n7GZbGBYcX

⭕️مقاله‌خوانی در حوزه‌ی سیستم‌های پیچیده⭕️
💡 موضوع ارائه:

Emergence of cooperation through chain-reaction death

🗣 ارائه دهنده: دکتر سامان مقیمی
📅 زمان ارائه: چهارشنبه ۲۷ بهمن ساعت ۱۲
🚪 محل برگزاری:
🔗 اتاق مجازی سمینار‌ها و مقاله‌خوانی‌های گروه فیزیک آماری

📌 پیوست یک: لینک گروه مقاله خوانی سیستم های پیچیده

📌 پیوست دو: لینک مقاله

💢#اطلاع_رسانی_سمینارهای_دانشکده
💢#مقاله‌خوانی_سیستم‌های‌پیچیده
💢#دکتر_مقیمی

Comment: Machine learning as a tool in theoretical science. Michael R. Douglas discusses recent advances and ponders on the impact of ML methods relying on synthetic data in mathematics and theoretical physics
https://t.co/T55fcxPc7C

فردا ساعت ۸ شب در صفحه اینستاگرام انجمن علمی فیزیک دانشگاه تبریز یا صفحه سیتپـــــور می‌تونید برنامه زنده ما در مورد علم شبکه رو دنبال کنید.

توی این برنامه دوست داریم به پرسش‌های شما پیرامون این شاخه از علم پاسخ بدیم. برای همین هر سوالی که به نظرتون می‌رسه رو می‌تونید موقع پخش زنده بپرسین!

🔴 instagram.com/sitpor_media
🔴 instagram.com/physics__tabriz

برای آشنایی بیشتر با سیستم‌های پیچیده به اینجا سر بزنید و برای آشنایی بیشتر با علم شبکه‌ این ویدیو رو می‌تونید ببینید.

-----------------------------------------------
@sitpor | sitpor.org
@anjoman_physics_tabriz_uni
#سیتپـــــور به خاطر روایتگری در علم

According to a new study on memory involving a map of the brain’s responses to linguistic information, memories aren’t exact copies of past perceptions. In reality, they are much more similar to a semantic reconstruction of the experience. https://t.co/GD14oWU6BC #QuantaMagazine

Research on spreading phenomena, resilience and modularity in probabilistic graphs applied to disaster science.

#PhD position, call: https://t.co/cTy7yxa0hS (Project 5)]

⭕️ سمینار‌های فیزیک آماری ⭕️
💡 موضوع ارائه: مدل‌سازی مداخله‌های دارویی و غیردارویی برای کنترل همه‌گیری‌ها
🗣 ارائه دهنده: عباس کریمی ریزی (دانشگاه آلتو فنلاند)
📅 زمان ارائه: یک‌شنبه ۱ اسفند ساعت ۱۵:۰۰
🚪 محل برگزاری:
🔗 اتاق مجازی گروه فیزیک آماری

💢#اطلاع_رسانی_سمینارهای_دانشکده
💢#سمینار_فیزیک_آماری
💢#دکتر_مقیمی #دکتر_قنبرنژاد #دکتر_روحانی

🆔 @anjoman_elmi_phys_sut

"Michael Fisher 1931–2021" (by Daniel Garisto_: https://t.co/b7vHgfsnsP

"Michael Fisher, a statistical physicist who excavated the secrets underlying critical phenomena across physics, chemistry, and biophysics, died November 26, 2021 at 90."

The lab could have openings for a #PhD position (fully funded) & a #postdoc or assistant professor (not tenured) position starting next fall 2022. The topic is #NetworkMedicine

Feel free to contact us asap w/ an expression of interest and to arrange a meeting.

https://t.co/VrbqdViIJN

The Helsinki Summer School on Mathematical Ecology and Evolution 2022

https://wiki.helsinki.fi/display/BioMath/The+Helsinki+Summer+School+on+Mathematical+Ecology+and+Evolution+2022

Topics and lecturers

Josef Hofbauer (University of Vienna): Dynamical systems in mathematical ecology
Gergely Röst (University of Szeged): The mathematics of infectious diseases
Pieter Trapman (Stockholm University): Stochastic models of epidemics
Jarno Vanhatalo (University of Helsinki): Linking ecological models to data through Bayesian statistics
Christian Hilbe (Max Planck Institute for Evolutionary Biology): Dynamics of social behaviour

Fully funded #PhD position in Statistical and Soft Matter Physics #TU_Munich. Your chance to make materials smart by teaching them to self-optimize for efficient transport. Do theory in collaboration with experiments.

https://t.co/I4shrEgAPE

#PhD student research assistant (MSc level) (gn) / #Postdoc research fellow (PhD level) (gn) with a focus on infectious disease epidemiology/mathematical modelling of infectious diseases

University of Münster, Germany

The Clinical Epidemiology Unit uses and develops modern statistical and mathematical methods for the analysis of primary and secondary data. A particular focus of the Unit is the application of dynamic mathematical modelling approaches to simulate the spread of infectious agents in human populations. The Unit is involved in various SARS-CoV-2 projects and is looking for a researcher to primarily work on new upcoming SARS-CoV-2 projects.

Apply Here

Very excited to announce the release of the #statnet Multilayer ERGM package developed during a fantastic collaboration with @PavelKrivitsky @KoehlyLaura and myself @SocNetAnalysts
@net_science #networkscience https://t.co/bFtjzj7PEB