#129 Bayesian Deep Learning & AI for Science with Vincent Fortuin
Learning Bayesian Statistics
Navigating the Shift: From Biochemistry to Bayesian Deep Learning
This chapter explores a researcher's transition from biochemistry to the realms of bioinformatics and deep learning. It emphasizes the integration of Bayesian statistics in deep learning, focusing on enhanced calibration and inference methods for scientific research applications. The discussion highlights the philosophical differences between traditional and Bayesian approaches, underlining the importance of uncertainty quantification in model predictions.
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Takeaways:
- The hype around AI in science often fails to deliver practical results.
- Bayesian deep learning combines the strengths of deep learning and Bayesian statistics.
- Fine-tuning LLMs with Bayesian methods improves prediction calibration.
- There is no single dominant library for Bayesian deep learning yet.
- Real-world applications of Bayesian deep learning exist in various fields.
- Prior knowledge is crucial for the effectiveness of Bayesian deep learning.
- Data efficiency in AI can be enhanced by incorporating prior knowledge.
- Generative AI and Bayesian deep learning can inform each other.
- The complexity of a problem influences the choice between Bayesian and traditional deep learning.
- Meta-learning enhances the efficiency of Bayesian models.
- PAC-Bayesian theory merges Bayesian and frequentist ideas.
- Laplace inference offers a cost-effective approximation.
- Subspace inference can optimize parameter efficiency.
- Bayesian deep learning is crucial for reliable predictions.
- Effective communication of uncertainty is essential.
- Realistic benchmarks are needed for Bayesian methods
- Collaboration and communication in the AI community are vital.
Chapters:
00:00 Introduction to Bayesian Deep Learning
06:12 Vincent's Journey into Machine Learning
12:42 Defining Bayesian Deep Learning
17:23 Current Landscape of Bayesian Libraries
22:02 Real-World Applications of Bayesian Deep Learning
24:29 When to Use Bayesian Deep Learning
29:36 Data Efficient AI and Generative Modeling
31:59 Exploring Generative AI and Meta-Learning
34:19 Understanding Bayesian Deep Learning and Prior Knowledge
39:01 Algorithms for Bayesian Deep Learning Models
43:25 Advancements in Efficient Inference Techniques
49:35 The Future of AI Models and Reliability
52:47 Advice for Aspiring Researchers in AI
56:06 Future Projects and Research Directions
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, 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, Steven Rowland, 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, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, 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 and Suyog Chandramouli.
Links from the show:
- Vincent’s website: https://fortuin.github.io/
- Vincent on Linkedin: https://www.linkedin.com/in/vincent-fortuin-42426b134/
- Vincent on GitHub: https://github.com/fortuin
- Vincent on Medium: https://medium.com/@vincefort
- Vincent on BlueSky: https://bsky.app/profile/vincefort.bsky.social
- LBS #107 Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt: https://learnbayesstats.com/episode/107-amortized-bayesian-inference-deep-neural-networks-marvin-schmitt/
- Position paper on Bayesian deep learning: https://proceedings.mlr.press/v235/papamarkou24b.html
- Position paper on generative AI: https://arxiv.org/abs/2403.00025
- BNN review paper: https://arxiv.org/abs/2309.16314
- Priors in BDL review paper: https://onlinelibrary.wiley.com/doi/10.1111/insr.12502
- BayesFlow: https://bayesflow.org/
- BayesianTorch: https://github.com/IntelLabs/bayesian-torch
- Laplace Torch: https://aleximmer.github.io/Laplace/
- TyXe: https://github.com/TyXe-BDL/TyXe
- Introduction to PAC-Bayes: https://arxiv.org/abs/2110.11216
- Training GPT2 with Bayesian methods: https://proceedings.mlr.press/v235/shen24b.html
- Bayesian fine-tuning for LLMs: https://arxiv.org/abs/2405.03425
- Try out NormalizingFlow initialization with Nutpie: https://discourse.pymc.io/t/new-experimental-sampling-algorithm-fisher-hmc-in-nutpie-for-pymc-and-stan-models/16114/5
Transcript
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