Algorithmic Seminars Jeremias Knoblauch - Optimization centric generalizations of Bayesian Inference Published 2021-03-19 Download video MP4 360p Download video MP4 720p Recommendations 50:15 Algorithms Seminar - Adeline Samson 1:02:33 Algorithms Seminar - Gilles Louppe - The frontiers of simulation-based inference 3:31:03 A conversation between Nassim Nicholas Taleb and Stephen Wolfram at the Wolfram Summer School 2021 1:37:12 Stocks Down & Bonds Up | Bloomberg Markets: The Close 05/30/2024 3:29:35 Stephen Wolfram's Picks of Cellular Automata from the Computational Universe 3:54:38 Optogenetics: Illuminating the Path toward Causal Neuroscience 31:29 Kit Fraser-Taliente - Machine-Learning Yukawa couplings from String Theory 3:04:57 Long Range Interactions and Strong Disorder: In Physics and in Life - May 4 - Morning Session 3:33:03 Deep Learning: A Crash Course (2018) | SIGGRAPH Courses 3:47:20 Math for Game Devs [2022, part 10] • Abstract Algebra, Procedural Animation & Splines 3:42:00 Celebrating Emil Post & His "Intractable Problem" of Tag: 100 Years Later 1:08:36 Rigorous results from machine learning | Fabian Ruehle 51:55 Professor Vlatko Vedral: Quantum Physics, Theory of Everything, Universe, Black Holes 50:48 How This Ex-Navy SEAL Turned A Book Into A $1M+ Empire - Jack Carr 20:55 Salary & Wage Survey: SA companies innovate benefits to combat rising living costs 59:36 Laurentiu Rodina: Scattering amplitudes: a Lagrangian for the 21st century 3:57:35 Math for Game Devs [2022, part 1] • Numbers, Vectors & Dot Product 3:57:16 Wolfram Physics Project: Working Session Tuesday, Aug. 4, 2020 [Empirical Physical Metamathematics] 2:27:53 Bloomberg Surveillance 05/28/2024 1:53:03 STREAMLINE Collaboration Machine Learning Symposium - Part 6, May 10, 2024 Similar videos 53:44 Optimization-centric Generalizations of Bayesian Inference --- Generalized VI & beyond 33:03 Jeremias Knoblauch, How Wasserstein Gradient Flows connect Deep Ensembles and Bayesian Methods 14:49 Optimal Continual Learning has Perfect Memory and is NP hard 49:55 Algorithms Seminar - E2. Joe Meagher. Bayesian Ancestral Reconstruction for Bat Echolocation 14:31 Variational Methods: How to Derive Inference for New Models (with Xanda Schofield) 52:06 Emtiyaz Khan - The Bayesian Learning Rule for Adaptive AI 35:45 Understanding Approximate Inference in Bayesian Neural Networks: A Joint Talk 49:52 ContinualAI RG: "Optimal Continual Learning has Perfect Memory and is NP-hard" 28:13 ContinualAI RG: "Memory-Efficient Incremental Learning Through Feature Adaptation" 52:04 Convergence of square tilings to the Riemann map - Agelos Georgakopoulos (University of Warwick) More results