Lecture 27: Bayesian Optimal Experimental Design. Active Learning: Gaussian Processes and Networks.

Lecture 28: Turing Machines. Algorithmic (Kolomogoroff) Complexity. Universal (Levin) Search.

Roman Garnett - Bayesian Optimization

Lecture 26: Active Learning for Network Training: Uncertainty Sampling and other approaches.

Lecture 22: Implicit Layers. Hamiltonian and Lagrangian Networks. Reinforcement Learning Overview.

MIA: Martin Jankowiak, Bayesian methods for adaptive experimental design

4 Hours Chopin for Studying, Concentration & Relaxation

«Экономическая ситуация меняется так, как не предвидели» — Олег Вьюгин

[DeepBayes2019]: Day 5, Lecture 3. Langevin dynamics for sampling and global optimization

Lecture 21: Transformers (and examples). Implicit Layers.

Yuxin Chen: "Bayesian Experimental Design in the Physical Sciences"

Как строили корабли для мирового господства

Lecture 29: Solomonoff's Algorithmic Probability and Theory of Induction. Conclusions/Outlook.

Lecture 9: Bayes. Gaussian Random Processes.

Accelerating the Search for New Materials using Machine Learning and Adaptive Design

Lecture 1: Introduction. Artificial Scientific Discovery.

Lecture 9: Optimal Experimental Design

Computational Audiology Network Podcast episode 1: Bayesian Active Learning in Audiology

Lecture 20: Attention. Differentiable Neural Computer. Transformers.

Jon Paul Janet | AstraZeneca AB - Accelerating drug design with AI & simulation

Happy December Morning Jazz ☕ Positive Coffee Music and Delicate Bossa Nova Piano for Joyful Moods