C
Charles Sutton
Researcher at Google
Publications - 204
Citations - 15393
Charles Sutton is an academic researcher from Google. The author has contributed to research in topics: Source code & Conditional random field. The author has an hindex of 46, co-authored 191 publications receiving 11553 citations. Previous affiliations of Charles Sutton include University of California, Berkeley & The Turing Institute.
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Journal Article
PaLM: Scaling Language Modeling with Pathways
Aakanksha Chowdhery,Sharan Narang,Jacob Devlin,Maarten Bosma,Gaurav Mishra,Adam Roberts,Paul Barham,Hyung Won Chung,Charles Sutton,Sebastian Gehrmann,Parker Schuh,Kensen Shi,Sasha Tsvyashchenko,Joshua Maynez,Abhishek Rao,Parker Barnes,Yi Tay,Noam Shazeer,Velu Prabhakaran,Emily Reif,Nan Du,B. C. Hutchinson,Reiner Pope,James Bradbury,Jacob Austin,Michael Isard,Guy Gur-Ari,Peng Yin,Toju Duke,Anselm Levskaya,Sanjay Ghemawat,Sunipa Dev,Henryk Michalewski,Xavier Garcia,Vedant Misra,Kevin Robinson,L Fedus,Denny Zhou,Daphne Ippolito,David Luan,Hyeontaek Lim,Barret Zoph,Alexander Spiridonov,Ryan Sepassi,David Dohan,Shivani Agrawal,Mark Omernick,Andrew M. Dai,Thanumalayan Sankaranarayana Pillai,Marie Pellat,Aitor Lewkowycz,Erica Oliveira Moreira,Rewon Child,Oleksandr Polozov,Katherine Lee,Zong Tuan Zhou,Xuezhi Wang,Brennan Saeta,Mark Díaz,Orhan Firat,M. Catasta,Jason Loh Seong Wei,Kathleen S. Meier-Hellstern,Douglas Eck,Jeffrey Dean,Slav Petrov,Noah Fiedel +66 more
TL;DR: A 540-billion parameter, densely activated, Transformer language model, which is called PaLM achieves breakthrough performance, outperforming the state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark.
An Introduction to Conditional Random Fields for Relational Learning
Charles Sutton,Andrew McCallum +1 more
TL;DR: A solution to this problem is to directly model the conditional distribution p(y|x), which is sufficient for classification, and this is the approach taken by conditional random fields.
Proceedings ArticleDOI
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
TL;DR: In this paper, a generalization of linear-chain CRFs, called dynamic conditional random fields (DCRFs), is proposed, in which each time slice contains a set of state variables and edges and parameters are tied across slices.
Posted Content
An Introduction to Conditional Random Fields
Charles Sutton,Andrew McCallum +1 more
TL;DR: Conditional Random Fields (CRFs) as discussed by the authors are a popular probabilistic method for structured prediction and have seen wide application in natural language processing, computer vision, and bioinformatics.