#150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer & Jakob Robnik
Falha ao colocar no Carrinho.
Falha ao adicionar à Lista de Desejos.
Falha ao remover da Lista de Desejos
Falha ao adicionar à Biblioteca
Falha ao seguir podcast
Falha ao parar de seguir podcast
-
Narrado por:
-
De:
Sobre este título
• Support & get perks!
• Proudly sponsored by PyMC Labs! Get in touch at alex.andorra@pymc-labs.com
• Intro to Bayes and Advanced Regression courses (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work !
Chapters:
00:00 Scaling Bayesian Neural Networks
04:26 Origin Stories of the Researchers
09:46 Research Themes in Bayesian Neural Networks
12:05 Making Bayesian Neural Networks Fast
16:19 Microcanonical Langevin Sampler Explained
22:57 Bottlenecks in Scaling Bayesian Neural Networks
29:09 Practical Tools for Bayesian Neural Networks
36:48 Trade-offs in Computational Efficiency and Posterior Fidelity
40:13 Exploring High Dimensional Gaussians
43:03 Practical Applications of Bayesian Deep Ensembles
45:20 Comparing Bayesian Neural Networks with Standard Approaches
50:03 Identifying Real-World Applications for Bayesian Methods
57:44 Future of Bayesian Deep Learning at Scale
01:05:56 The Evolution of Bayesian Inference Packages
01:10:39 Vision for the Future of Bayesian Statistics
Thank you to my Patrons for making this episode possible!
Come meet Alex at the Field of Play Conference in Manchester, UK, March 27, 2026!
Links from the show:
David Rügamer:
* Website
* Google Scholar
* GitHub
Emanuel Sommer:
* Website
* GitHub
* Google Scholar
Jakob Robnik:
* Google Scholar
* GitHub
* Microcanonical Langevin paper
* LinkedIn