University of Cambridge > > NLIP Seminar Series > [POSTPONED] Parametric vs Nonparametric Knowledge, and what we can learn from Knowledge Bases

[POSTPONED] Parametric vs Nonparametric Knowledge, and what we can learn from Knowledge Bases

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  • UserSebastian Riedel (Facebook AI Research and UCL)
  • ClockFriday 18 February 2022, 12:00-13:00
  • HouseVirtual (Zoom).

If you have a question about this talk, please contact Georgi Karadzhov.

The seminar on 18.02. is cancelled. I will be postponed later in the term.

Traditionally, AI and Machine Learning communities have considered knowledge from the perspective of discrete vs continuous representations, knowledge bases (KBs) vs dense vectors or logic vs algebra. While these are important dichotomies, in this talk I will argue that we should put more focus on another: parametric vs non-parametric modelling. Roughly, in the former a fixed set of parameters is used, in the latter parameters grow with data. I will explain recent advances in knowledge intensive NLP from this perspective, show the benefit of hybrid approaches, and discuss KBs as non-parametric approaches with relatively crude assumptions about what future information needs will be. By replacing these assumptions with a learnt model, we show that such “modern KBs” are a very attractive alternative or complement to current approaches.


Sebastian Riedel is a researcher at Facebook AI research, professor in Natural Language Processing and Machine Learning at the University College London (UCL) and an Allen Distinguished Investigator. He works in the intersection of Natural Language Processing and Machine Learning, and focuses on teaching machines how to read and reason. He was educated in Hamburg-Harburg (Dipl. Ing) and Edinburgh (MSc., PhD), and worked at the University of Massachusetts Amherst and Tokyo University before joining UCL .

Georgi Karadzhov is inviting you to a scheduled Zoom meeting.

Topic: NLIP Seminar 18.02.2022 Time: Feb 18, 2022 12:00 PM London

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