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

Making TCS more connected / less insular

[Announcement from Jelani Nelson --Boaz]TL;DR: https://tinyurl.com/tcs-connections A task force has been convened by CATCS to investigate possibleapproaches to modifying aspects of the TCS community, especially ourpublishing culture, to enhance connections with other areas of CS andbe as welcoming as possible to a broad range of contributions withintheory. This committee will collect and synthesize feedback from … Continue reading Making TCS more connected / less insular