Unit 8: Track 3: Science and Deep Learning
Resources: Track 3: Science and Deep Learning
Resources (2 hrs 50 mins)
- Chinchilla's wild implications
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- Scaling laws from the data manifold dimension
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- Understanding deep learning requires rethinking generalization
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- Deep double descent: blog post
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- A mechanistic interpretability analysis of grokking
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- In-context learning and induction heads
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Optional Resources
- Explaining neural scaling laws
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- Omnigrok: grokking beyond algorithmic data
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- Neural tangent kernal: convergence and generalization in neural networks
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- Equivalence between policy gradients and soft q-learning
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- The lottery ticket hypothesis
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- A review of select results from PDLT
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- Scaling laws for transfer
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- Scaling laws and interpretability of learning from repeated data
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- Training compute-optimal large language models
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