Towards a Theory of Generalization in Reinforcement Learning: guest lecture by Sham Kakade

Scribe notes by Hamza Chaudhry and Zhaolin Ren Previous post: Natural Language Processing - guest lecture by Sasha Rush Next post: TBD. See also all seminar posts and course webpage. See also video of lecture. Lecture slides: Original form: main / bandit analysis. Annotated: main / bandit analysis. Sham Kakade is a professor in the … Continue reading Towards a Theory of Generalization in Reinforcement Learning: guest lecture by Sham Kakade

Natural Language Processing (guest lecture by Sasha Rush)

Scribe notes by Benjamin Basseri and Richard Xu Previous post: Inference and statistical physics Next post: TBD. See also all seminar posts and course webpage. Alexander (Sasha) Rush is a professor at Cornell working in in Deep Learning / NLP. He applies machine learning to problems of text generation, summarizing long documents, and interactions between … Continue reading Natural Language Processing (guest lecture by Sasha Rush)

Inference and statistical physics

Scribe notes by Franklyn Wang Previous post: Robustness in train and test time Next post: Natural Language Processing (guest lecture by Sasha Rush). See also all seminar posts and course webpage. lecture slides (pdf) - lecture slides (Powerpoint with animation and annotation) - video Digression: Frequentism vs Bayesianism Before getting started, we'll discuss the difference … Continue reading Inference and statistical physics

What do deep networks learn and when do they learn it

Scribe notes by Manos Theodosis Previous post: A blitz through statistical learning theory Next post: Unsupervised learning and generative models. See also all seminar posts and course webpage. Lecture video - Slides (pdf) - Slides (powerpoint with ink and animation) In this lecture, we talk about what neural networks end up learning (in terms of … Continue reading What do deep networks learn and when do they learn it