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- Intro to Bayes Course (first 2 lessons free)
- Advanced Regression Course (first 2 lessons free)
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Takeaways:
- Bayesian deep learning is a growing field with many challenges.
- Current research focuses on applying Bayesian methods to neural networks.
- Diffusion methods are emerging as a new approach for uncertainty quantification.
- The integration of machine learning tools into Bayesian models is a key area of research.
- The complexity of Bayesian neural networks poses significant computational challenges.
- Future research will focus on improving methods for uncertainty quantification. Generalized Bayesian inference offers a more robust approach to uncertainty.
- Uncertainty quantification is crucial in fields like medicine and epidemiology.
- Detecting out-of-distribution examples is essential for model reliability.
- Exploration-exploitation trade-off is vital in reinforcement learning.
- Marginal likelihood can be misleading for model selection.
- The integration of Bayesian methods in LLMs presents unique challenges.
Chapters:
00:00 Introduction to Bayesian Deep Learning
03:12 Panelist Introductions and Backgrounds
10:37 Current Research and Challenges in Bayesian Deep Learning
18:04 Contrasting Approaches: Bayesian vs. Machine Learning
26:09 Tools and Techniques for Bayesian Deep Learning
31:18 Innovative Methods in Uncertainty Quantification
36:23 Generalized Bayesian Inference and Its Implications
41:38 Robust Bayesian Inference and Gaussian Processes
44:24 Software Development in Bayesian Statistics
46:51 Understanding Uncertainty in Language Models
50:03 Hallucinations in Language Models
53:48 Bayesian Neural Networks vs Traditional Neural Networks
58:00 Challenges with Likelihood Assumptions
01:01:22 Practical Applications of Uncertainty Quantification
01:04:33 Meta Decision-Making with Uncertainty
01:06:50 Exploring Bayesian Priors in Neural Networks
01:09:17 Model Complexity and Data Signal
01:12:10 Marginal Likelihood and Model Selection
01:15:03 Implementing Bayesian Methods in LLMs
01:19:21 Out-of-Distribution Detection in LLMs
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Joshua Meehl, Javier Sabio, Kristian Higgins, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia, Michael Cao, Yiğit Aşık, Suyog Chandramouli and Adam Tilmar Jakobsen.
Dr. Mélodie Monod (Imperial College London, School of Public Health)
Mélodie completed her PhD as part of the EPSRC Modern Statistics and Statistical Machine Learning program at Imperial College London, transitioned to Novartis as Principal Biostatistician, and is currently a Postdoctoral Researcher in Machine Learning at Imperial. Her research includes diffusion models, Bayesian deep learning, non-parametric Bayesian statistics and pandemic modelling. For more details, see her Google Scholar Publications page.
Dr. François-Xavier Briol (University College London, Department of Statistical Science)
F-X is Associate Professor in the Department of Statistical Science at University College London, where he leads the Fundamentals of Statistical Machine Learning research group and is co-director of the UCL ELLIS unit. His research focuses on developing statistical and machine learning methods for the sciences and engineering, with his recent work focusing on Bayesian computation and robustness to model misspecification. For more details, see his Google Scholar page.
Dr. Yingzhen Li (Imperial College London, Department of Computing)
Yingzhen is Associate Professor in Machine Learning at the Department of Computing at Imperial College London, following several years at Microsoft Research Cambridge as senior researcher. Her research focuses on building reliable machine learning systems which can generalise to unseen environments, including topics such as (deep) probabilistic graphical model design, fast and accurate (Bayesian) inference/computation techniques, uncertainty quantification for computation and downstream tasks, and robust and adaptive machine learning systems. For more details, see her Google Scholar Publications page.
Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
