The dependency network approach provides a system level analysis of the activity and topology of directed networks. The approach extracts causal topological relations between the network's nodes (when the network structure is analyzed), and provides an important step towards inference of causal activity relations between the network nodes (when analyzing the network activity). This methodology has originally been introduced for the study of financial data, it has been extended and applied to other systems, such as the immune system, and semantic networks. In the case of network activity, the analysis is based on partial correlations, which are becoming ever more widely used to investigate complex systems. In simple words, the partial (or residual) correlation is a measure of the effect (or contribution) of a given node, say j, on the correlations between another pair of nodes, say i and k. Using this concept, the dependency of one node on another node is calculated for the entire network. This results in a directed weighted adjacency matrix of a fully connected network. Once the adjacency matrix has been constructed, different algorithms can be used to construct the network, such as a threshold network, Minimal Spanning Tree (MST), Planar Maximally Filtered Graph (PMFG), and others. (Wikipedia).
From playlist Week 9: Social Networks
Graph Neural Networks, Session 2: Graph Definition
Types of Graphs Common data structures for storing graphs
From playlist Graph Neural Networks (Hands-on)
Star Network - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Networking
This lecture gives an overview of neural networks, which play an important role in machine learning today. Book website: http://databookuw.com/ Steve Brunton's website: eigensteve.com
From playlist Intro to Data Science
Garnet Chan - Arithmetic tensor networks and integration - IPAM at UCLA
Recorded 26 January 2022. Garnet Chan of the California Institute of Technology presents "Arithmetic tensor networks and integration" at IPAM's Quantum Numerical Linear Algebra Workshop. Abstract: I will discuss how to perform arithmetic with tensor networks and the consequences for the in
From playlist Quantum Numerical Linear Algebra - Jan. 24 - 27, 2022
Neural Ordinary Differential Equations
https://arxiv.org/abs/1806.07366 Abstract: We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a black-bo
From playlist Deep Learning Architectures
Multiple Time scale phenomena on Complex networks by G. Ambika
DISCUSSION MEETING INDIAN STATISTICAL PHYSICS COMMUNITY MEETING ORGANIZERS Ranjini Bandyopadhyay, Abhishek Dhar, Kavita Jain, Rahul Pandit, Sanjib Sabhapandit, Samriddhi Sankar Ray and Prerna Sharma DATE: 14 February 2019 to 16 February 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalo
From playlist Indian Statistical Physics Community Meeting 2019
Learning from Censored and Dependent Data - Constantinos Daskalakis
Computer Science/Discrete Mathematics Seminar I Topic: Learning from Censored and Dependent Data Speaker: Constantinos Daskalakis Affiliation: Massachusetts Institute of Technology; Member, School of Mathematics Date: March 9, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
DDPS | Neural Galerkin schemes with active learning for high-dimensional evolution equations
Title: Neural Galerkin schemes with active learning for high-dimensional evolution equations Speaker: Benjamin Peherstorfer (New York University) Description: Fitting parameters of machine learning models such as deep networks typically requires accurately estimating the population loss
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Critical Paths Analysis (2) - Activity Network (without dummies)
Powered by https://www.numerise.com/ Critical Paths Analysis (2) Decision Maths 1 Edexcel A-Level Maths.
From playlist Decision Maths - Critical Paths Analysis
Mathematical Modeling of Epidemics. Lecture2: Epidemics on networks
This lecture explain modeling epdimeic spread on networks and exponential growth rate of infection. This lecture is a part of Network Science course at HSE. Lecture slides: http://www.leonidzhukov.net/hse/2020/networks/lectures/lecture10.pdf Course website: http://www.leonidzhukov.net/hse
From playlist COVID-19 Modeling
Explosive death in coupled oscillators by Manish Shrimali
PROGRAM DYNAMICS OF COMPLEX SYSTEMS 2018 ORGANIZERS Amit Apte, Soumitro Banerjee, Pranay Goel, Partha Guha, Neelima Gupte, Govindan Rangarajan and Somdatta Sinha DATE: 16 June 2018 to 30 June 2018 VENUE: Ramanujan hall for Summer School held from 16 - 25 June, 2018; Madhava hall for W
From playlist Dynamics of Complex systems 2018
Understanding the inductive bias due to dropout - Raman Arora
Workshop on Theory of Deep Learning: Where next? Topic: Understanding the inductive bias due to dropout Speaker: Raman Arora Affiliation: Johns Hopkins University; Member, School of Mathematics Date: October 17, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
An intro to the core protocols of the Internet, including IPv4, TCP, UDP, and HTTP. Part of a larger series teaching programming. See codeschool.org
From playlist The Internet
Luigi Malagò : A review of Different Geometries for the Training of Neural Networks
Recording during the thematic meeting : "Geometrical and Topological Structures of Information" the August 30, 2017 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematician
From playlist Geometry