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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.

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

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Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer,...