Hierarchical network models are iterative algorithms for creating networks which are able to reproduce the unique properties of the scale-free topology and the high clustering of the nodes at the same time. These characteristics are widely observed in nature, from biology to language to some social networks. (Wikipedia).
Network Science 2021 @ HSE http://www.leonidzhukov.net/hse/2021/networks/
From playlist Network Science, 2021
Data structures: Introduction to graphs
See complete series on data structures here: http://www.youtube.com/playlist?list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P In this lesson, we have described Graph data structure as a mathematical model. We have briefly described the concept of Graph and some of its applications. For practice
From playlist Data structures
Network Analysis. Lecture 3. Random graphs.
Erdos-Reni random graph model. Poisson and Bernulli distributions. Distribution of node degrees. Phase transition, gigantic connected component. Diameter and cluster coefficient. Configuration model Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture3.pdf
From playlist Structural Analysis and Visualization of Networks.
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
Generative Model Basics - Unconventional Neural Networks p.1
Hello and welcome to a series where we will just be playing around with neural networks. The idea here is to poke around with various neural networks, doing unconventional things with them. Doing things like trying to teach a sequence to sequence model math, doing classification with a gen
From playlist Unconventional Neural Networks
Graph Neural Networks, Session 1: Introduction to Graphs
Examples of Graph representation of data Motivation for doing machine learning on Graphs
From playlist Graph Neural Networks (Hands-on)
From playlist Week 9: Social Networks
Data structures: Introduction to Trees
See complete series on data structures here: http://www.youtube.com/playlist?list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P In this lesson, we have described tree data structure as a logical model in computer science. We have briefly discussed tree as a non-linear hierarchical data structure, i
From playlist Data structures
Flavio Rusch - Self-organized criticality in hierarchical modular networks...
Self-organized criticality in hierarchical modular networks of Galves-Löcherbach neurons ---------------------------------- Institut Henri Poincaré, 11 rue Pierre et Marie Curie, 75005 PARIS http://www.ihp.fr/ Rejoingez les réseaux sociaux de l'IHP pour être au courant de nos actualités :
From playlist Workshop "Workshop on Mathematical Modeling and Statistical Analysis in Neuroscience" - January 31st - February 4th, 2022
Ruslan Salakhutdinov: "Advanced Hierarchical Models"
Graduate Summer School 2012: Deep Learning, Feature Learning "Advanced Hierarchical Models" Ruslan Salakhutdinov Institute for Pure and Applied Mathematics, UCLA July 24, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-deep-learning-fe
From playlist GSS2012: Deep Learning, Feature Learning
NVAE: A Deep Hierarchical Variational Autoencoder (Paper Explained)
VAEs have been traditionally hard to train at high resolutions and unstable when going deep with many layers. In addition, VAE samples are often more blurry and less crisp than those from GANs. This paper details all the engineering choices necessary to successfully train a deep hierarchic
From playlist Papers Explained
Jamie Haddock - Hierarchical and neural nonnegative tensor factorizations - IPAM at UCLA
Recorded 02 December 2022. Jamie Haddock of Harvey Mudd College presents "Hierarchical and neural nonnegative tensor factorizations" at IPAM's Multi-Modal Imaging with Deep Learning and Modeling Workshop. Abstract: Nonnegative matrix factorization (NMF) has found many applications includin
From playlist 2022 Multi-Modal Imaging with Deep Learning and Modeling
Neural networks and the brain: from the retina to semantic cognition - Surya Ganguli
Surya Ganguli research spans the fields of neuroscience, machine learning and physics, focusing on understanding and improving how both biological and artificial neural networks learn striking emergent computations. In this talk Dr. Ganguli shows how a synthesis of machine learning, neuros
From playlist Wu Tsai Neurosciences Institute
Deep Learning of Hierarchical Multiscale Differential Equation Time Steppers
This video by Yuying Liu introduces a new deep learning architecture to accurately and efficiently integrate multiscale differential equations forward in time. This approach is benchmarked on several illustrative dynamical systems. Check out the paper on arXiv: https://arxiv.org/abs/20
From playlist Data-Driven Science and Engineering
Neural opinion dynamics model for the prediction of user-level stance dynamics - Yulan He, Warwick
Around the world, digital participation platforms are being used as a tool for direct democracy, aiming to empower citizens to contribute to policy making. As trust in traditional democratic institutions declines, these deliberative platforms offer a way to build new relationships and trus
From playlist Citizen participation and machine learning for a better democracy
Workshop on Theory of Deep Learning: Where next? Topic: Spotlight Talks Speakers: Yuanzhi Li, Soham De, Mahyar Fazlyab, Maithra Raghu, Valentin Thomas Date: October 15, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
Physics of functional networks - Henrik Ronellenfitsch
Workshop on Topology: Identifying Order in Complex Systems Topic: Physics of functional networks Speaker: Henrik Ronellenfitsch Affiliation: Williams College Date: March 19, 2021 For more video please visit http://video.ias.edu
From playlist Mathematics
On Expressiveness and Optimization in Deep Learning - Nadav Cohen
Members' Seminar Topic: On Expressiveness and Optimization in Deep Learning Speaker: Nadav Cohen Affiliation: Member, School of Mathematics Date: April 2, 2018 For more videos, please visit http://video.ias.edu
From playlist Mathematics
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