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Jordan Hoffmann
Jordan Hoffmann
Microsoft AI
Verified email at microsoft.com - Homepage
Title
Cited by
Cited by
Year
Training compute-optimal large language models
J Hoffmann, S Borgeaud, A Mensch, E Buchatskaya, T Cai, E Rutherford, ...
arXiv preprint arXiv:2203.15556, 2022
1629*2022
Scaling language models: Methods, analysis & insights from training gopher
JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ...
arXiv preprint arXiv:2112.11446, 2021
1014*2021
Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization
FY Sun, J Hoffmann, V Verma, J Tang
arXiv preprint arXiv:1908.01000, 2019
9362019
Improving language models by retrieving from trillions of tokens
S Borgeaud, A Mensch, J Hoffmann, T Cai, E Rutherford, K Millican, ...
International conference on machine learning, 2206-2240, 2022
7872022
Recurrent independent mechanisms
A Goyal, A Lamb, J Hoffmann, S Sodhani, S Levine, Y Bengio, ...
arXiv preprint arXiv:1909.10893, 2019
3342019
Unified scaling laws for routed language models
A Clark, D de Las Casas, A Guy, A Mensch, M Paganini, J Hoffmann, ...
International conference on machine learning, 4057-4086, 2022
122*2022
Reconnaissance of the HR 8799 exosolar system. II. Astrometry and orbital motion
L Pueyo, R Soummer, J Hoffmann, R Oppenheimer, JR Graham, ...
The Astrophysical Journal 803 (1), 31, 2015
1192015
An empirical analysis of compute-optimal large language model training
J Hoffmann, S Borgeaud, A Mensch, E Buchatskaya, T Cai, E Rutherford, ...
Advances in Neural Information Processing Systems 35, 30016-30030, 2022
1082022
vgraph: A generative model for joint community detection and node representation learning
FY Sun, M Qu, J Hoffmann, CW Huang, J Tang
Advances in Neural Information Processing Systems 32, 2019
1022019
Data-driven approach to encoding and decoding 3-d crystal structures
J Hoffmann, L Maestrati, Y Sawada, J Tang, JM Sellier, Y Bengio
arXiv preprint arXiv:1909.00949, 2019
752019
Machine learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets
J Hoffmann, Y Bar-Sinai, LM Lee, J Andrejevic, S Mishra, SM Rubinstein, ...
Science advances 5 (4), eaau6792, 2019
732019
Ion correlations in nanofluidic channels: Effects of ion size, valence, and concentration on voltage-and pressure-driven currents
J Hoffmann, D Gillespie
Langmuir 29 (4), 1303-1317, 2013
572013
A simple developmental model recapitulates complex insect wing venation patterns
J Hoffmann, S Donoughe, K Li, MK Salcedo, CH Rycroft
Proceedings of the National Academy of Sciences 115 (40), 9905-9910, 2018
482018
A systematic investigation of commonsense knowledge in large language models
XL Li, A Kuncoro, J Hoffmann, CM d'Autume, P Blunsom, A Nematzadeh
arXiv preprint arXiv:2111.00607, 2021
462021
Computational analysis of size, shape and structure of insect wings
MK Salcedo, J Hoffmann, S Donoughe, L Mahadevan
Biology Open 8 (10), bio040774, 2019
422019
Nuclear speed and cycle length co-vary with local density during syncytial blastoderm formation in a cricket
S Donoughe, J Hoffmann, T Nakamura, CH Rycroft, CG Extavour
Nature communications 13 (1), 3889, 2022
22*2022
Training compute-optimal large language models. arXiv 2022
J Hoffmann, S Borgeaud, A Mensch, E Buchatskaya, T Cai, E Rutherford, ...
arXiv preprint arXiv:2203.15556 10, 2022
212022
The role of negative selection in protein evolution revealed through the energetics of the native state ensemble
J Hoffmann, JO Wrabl, VJ Hilser
Proteins: Structure, Function, and Bioinformatics 84 (4), 435-447, 2016
212016
Training compute-optimal large language models. arXiv
J Hoffmann, S Borgeaud, A Mensch, E Buchatskaya, T Cai, E Rutherford, ...
arXiv preprint arXiv:2203.15556, 2022
182022
Scaling language models: Methods, analysis & insights from training gopher. arXiv
JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ...
Preprint posted online on December 8, 2021
142021
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