Chapters
Transcript
Episode notes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Introduction
00:00 • 2min
How I Decided to Do a PhD
01:59 • 3min
Sequential Decisions and Predictions in NLP
04:40 • 2min
How Machine Learning Interacts With Structure Prediction
06:13 • 3min
How to Combine Imitation Learning and Reinforcement Learning
09:32 • 5min
Offline Reinforcement Learning for Text Generation
14:24 • 2min
How to Frame NLP Problems as Sequential Decision Making
16:27 • 6min
How to Adapt a Pre-Trained Language Model to a Conditional Task
22:09 • 2min
How to Optimize a Language Model for Funniness
24:26 • 2min
How to Use Reinforcement Learning to Solve Complex Problems
26:22 • 2min
Coaching: A Way to Improve Text Generation
28:47 • 4min
The Relationship Between the Oracle and the Trust Region
32:25 • 5min
The Variation of Key Learning
37:18 • 2min
How to Model the Knowledge Graph of Two Different Agents
38:59 • 2min
The Benefits of Modeling the Information of Friends
41:00 • 3min
The Future of Translation Models
43:45 • 4min
The PhD and the PhD in Text Generation
47:38 • 2min
The Importance of a Test-Time Distribution
49:41 • 4min
Scaling Up Models to Improve Robustness
53:34 • 3min
How to Handle Failure in the First Year of a PhD
56:29 • 4min