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
Ana Romero: Effective computation of spectral systems and relation with multi-parameter persistence
Title: Effective computation of spectral systems and their relation with multi-parameter persistence Abstract: Spectral systems are a useful tool in Computational Algebraic Topology that provide topological information on spaces with generalized filtrations over a poset and generalize the
From playlist AATRN 2022
Carlos Simpson - Spectral networks and harmonic maps to buildings
This is joint work with L. Katzarkov, A. Noll, and P. Pandit in Vienna. A boundary point of the character variety gives rise to a spectral curve, and a harmonic map to a building. The differential of the harmonic map is the real part of the multivalued tuple of differentials defined over t
From playlist 3e Séminaire Itzykson: Wall crossing in Hitchin integrable systems
Spectral Sequences 02: Spectral Sequence of a Filtered Complex
I like Ivan Mirovic's Course notes. http://people.math.umass.edu/~mirkovic/A.COURSE.notes/3.HomologicalAlgebra/HA/2.Spring06/C.pdf Also, Ravi Vakil's Foundations of Algebraic Geometry and the Stacks Project do this well as well.
From playlist Spectral Sequences
From playlist Week 9: Social Networks
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Representation of wide sense stationary random processes in the frequency domain - the power spectral density or power spectrum is the DTFT of the a
From playlist Random Signal Characterization
Ring 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
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
Introduction into spectral networks (Lecture 1) by Lotte Hollands
Program: Quantum Fields, Geometry and Representation Theory ORGANIZERS : Aswin Balasubramanian, Saurav Bhaumik, Indranil Biswas, Abhijit Gadde, Rajesh Gopakumar and Mahan Mj DATE & TIME : 16 July 2018 to 27 July 2018 VENUE : Madhava Lecture Hall, ICTS, Bangalore The power of symmetries
From playlist Quantum Fields, Geometry and Representation Theory
Ginestra Bianconi (8/28/21): The topological Dirac operator and the dynamics of topological signals
Topological signals associated not only to nodes but also to links and to the higher dimensional simplices of simplicial complexes are attracting increasing interest in signal processing, machine learning and network science. Typically, topological signals of a given dimension are investig
From playlist Beyond TDA - Persistent functions and its applications in data sciences, 2021
Soledad Villar: "Graph neural networks for combinatorial optimization problems"
Machine Learning for Physics and the Physics of Learning 2019 Workshop IV: Using Physical Insights for Machine Learning "Graph neural networks for combinatorial optimization problems" Soledad Villar - New York University Abstract: Graph neural networks are natural objects to express fu
From playlist Machine Learning for Physics and the Physics of Learning 2019
Graph Convolutional Networks (GCN) | GNN Paper Explained
❤️ Become The AI Epiphany Patreon ❤️ ► https://www.patreon.com/theaiepiphany ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ In this video I do a deep dive into the graph convolutional networks paper! It's currently the most cited paper in the GNN literature at the time of making this video. You'll learn abou
From playlist Graph Neural Nets
Xavier Bresson: "Convolutional Neural Networks on Graphs"
New Deep Learning Techniques 2018 "Convolutional Neural Networks on Graphs" Xavier Bresson, Nanyang Technological University, Singapore Abstract: Convolutional neural networks have greatly improved state-of-the-art performances in computer vision and speech analysis tasks, due to its hig
From playlist New Deep Learning Techniques 2018
Bosonic Complex Quantum Networks: What, when and why - S. Maniscalco - Workshop 1 - CEB T2 2018
Sabrina Maniscalco (Univ. Turku) / 17.05.2018 Bosonic Complex Quantum Networks: What, when and why. In this talk I will present some perspectives on these questions by looking at Hamiltonian models describing complex networks of quantum harmonic oscillators. I will first show that such
From playlist 2018 - T2 - Measurement and Control of Quantum Systems: Theory and Experiments
Concentration of random graphs and application to community detection – E. Levina – ICM2018
Probability and Statistics Invited Lecture 12.10 Concentration of random graphs and application to community detection Elizaveta Levina Abstract: Random matrix theory has played an important role in recent work on statistical network analysis. In this paper, we review recent results on r
From playlist Probability and Statistics
Understanding Deep Neural Networks: From Generalization to Interpretability - Gitta Kutyniok
Seminar on Theoretical Machine Learning Topic: Understanding Deep Neural Networks: From Generalization to Interpretability Speaker: Gitta Kutyniok Affiliation: Technische Universität Berlin Date: March 5, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Networks - 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
The Power of Nonlinearities - A. Marandi - 11/11/2020
Earnest C. Watson Lecture by Professor Marandi, "The Power of Nonlinearities: Unlocking Opportunities for Sensing and Computing with Light." As the information age evolves, we are faced with new challenges in how to capture and process information. Nonlinearity, which leads to functions
From playlist Caltech Watson Lecture Series