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CS-330 Lecture 8: Variational Inference
This lecture is part of the CS-330 Deep Multi-Task and Meta Learning course, taught by Chelsea Finn in Fall 2023 at Stanford. This post will talk about variational inference, which is a way of approximating complex distributions through Bayesian inference. We will go from talking about latent variable models all the way to amortized variational inference!
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CS-330 Lecture 7: Unsupervised Pre-Training: Reconstruction-Based Methods
This lecture is part of the CS-330 Deep Multi-Task and Meta Learning course, taught by Chelsea Finn in Fall 2023 at Stanford. The goal of this post is to introduce to widely-used methods for unsupervised pre-training, which is essential in many fields nowadays, most notably in the development of foundation models. We also introduce methods that help with efficient fine-tuning of pre-trained models!
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CS-330 Lecture 6: Unsupervised Pre-Training: Contrastive Learning
This lecture is part of the CS-330 Deep Multi-Task and Meta Learning course, taught by Chelsea Finn in Fall 2023 at Stanford. The goal of this lecture is to understand the intuition, design choices, and implementation of contrastive learning for unsupervised representation learning. We will also talk about the relationship between contrastive learning and meta learning!
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CS-330 Lecture 5: Few-Shot Learning via Metric Learning
This lecture is part of the CS-330 Deep Multi-Task and Meta Learning course, taught by Chelsea Finn in Fall 2023 at Stanford. The goal of this lecture is to to understand the third form of meta learning: non-parametric few-shot learning. We will also compare the three different methods of meta learning. Finally, we give practical examples of meta learning, in domains such as imitation learning, drug discovery, motion prediction, and language generation!
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CS-330 Lecture 4: Optimization-Based Meta-Learning
This lecture is part of the CS-330 Deep Multi-Task and Meta Learning course, taught by Chelsea Finn in Fall 2023 at Stanford. The goal of this lecture is to understand the basics of optimization-based meta learning techniques. You will also learn about the trade-offs between black-box and optimization-based meta learning!