Practical Learning To Learn
Gradient descent continues to be our main work horse for training neural networks. One recurring problem though is the large amount of data required. Meta learning frames the problem not as learning from a single large dataset, but learning how to learn from multiple related smaller datasets. In this talk we'll first discuss some key concepts around gradient descent; fine-tuning, transfer learning, joint training and catastrophic forgetting and compare them to how simple meta learning techniques can make optimisation feasible for much smaller datasets.
Outline/Structure of the Talk
- High level review of gradient descent and it's general behaviours in differing cases of datasets.
- Discussion of two methods for meta learning; MAML and Reptile; with a description of how they are related.
Learning Outcome
- A practical understanding of approaches to first order meta learning.
Target Audience
Applied machine learning researchers and engineers.
Prerequisites for Attendees
- Some background / experience in training models using gradient descent.
Links
- Personal Blog
- List of publications while at Google Research
- "Deep Reinforcement Learning for Robotics (youtube)" (Melbourne ML/AI meetup talk)
- "The building blocks of neural networks (slides)" (Thoughtworks Evolution)
schedule Submitted 2 years ago
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