Johannes Welbl
Johannes Welbl
Research Scientist, DeepMind
Verified email at
Cited by
Cited by
Complex embeddings for simple link prediction
T Trouillon, J Welbl, S Riedel, É Gaussier, G Bouchard
International conference on machine learning, 2071-2080, 2016
Constructing datasets for multi-hop reading comprehension across documents
J Welbl, P Stenetorp, S Riedel
Transactions of the Association for Computational Linguistics 6, 287-302, 2018
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
Training compute-optimal large language models
J Hoffmann, S Borgeaud, A Mensch, E Buchatskaya, T Cai, E Rutherford, ...
arXiv preprint arXiv:2203.15556, 2022
Knowledge graph completion via complex tensor factorization
T Trouillon, CR Dance, J Welbl, S Riedel, É Gaussier, G Bouchard
arXiv preprint arXiv:1702.06879, 2017
Competition-level code generation with alphacode
Y Li, D Choi, J Chung, N Kushman, J Schrittwieser, R Leblond, T Eccles, ...
Science 378 (6624), 1092-1097, 2022
Achieving verified robustness to symbol substitutions via interval bound propagation
PS Huang, R Stanforth, J Welbl, C Dyer, D Yogatama, S Gowal, ...
arXiv preprint arXiv:1909.01492, 2019
Crowdsourcing multiple choice science questions
J Welbl, NF Liu, M Gardner
arXiv preprint arXiv:1707.06209, 2017
Frustratingly short attention spans in neural language modeling
M Daniluk, T Rocktäschel, J Welbl, S Riedel
arXiv preprint arXiv:1702.04521, 2017
Ucl machine reading group: Four factor framework for fact finding (hexaf)
T Yoneda, J Mitchell, J Welbl, P Stenetorp, S Riedel
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER …, 2018
Beat the AI: Investigating adversarial human annotation for reading comprehension
M Bartolo, A Roberts, J Welbl, S Riedel, P Stenetorp
Transactions of the Association for Computational Linguistics 8, 662-678, 2020
Reducing sentiment bias in language models via counterfactual evaluation
PS Huang, H Zhang, R Jiang, R Stanforth, J Welbl, J Rae, V Maini, ...
arXiv preprint arXiv:1911.03064, 2019
Neural random forests
G Biau, E Scornet, J Welbl
Sankhya A 81, 347-386, 2019
Challenges in detoxifying language models
J Welbl, A Glaese, J Uesato, S Dathathri, J Mellor, LA Hendricks, ...
arXiv preprint arXiv:2109.07445, 2021
Casting random forests as artificial neural networks (and profiting from it)
J Welbl
Pattern Recognition: 36th German Conference, GCPR 2014, Münster, Germany …, 2014
Making sense of sensory input
R Evans, J Hernández-Orallo, J Welbl, P Kohli, M Sergot
Artificial Intelligence 293, 103438, 2021
Neural random forests
G Biau, E Scornet, J Welbl
arXiv preprint arXiv:1604.07143, 2016
Undersensitivity in neural reading comprehension
J Welbl, P Minervini, M Bartolo, P Stenetorp, S Riedel
arXiv preprint arXiv:2003.04808, 2020
Evaluating the apperception engine
R Evans, J Hernández-Orallo, J Welbl, P Kohli, M Sergot
arXiv preprint arXiv:2007.05367, 2020
Jack the reader-A machine reading framework
D Weissenborn, P Minervini, T Dettmers, I Augenstein, J Welbl, ...
arXiv preprint arXiv:1806.08727, 2018
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