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
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
Frustratingly short attention spans in neural language modeling
M Daniluk, T Rocktäschel, J Welbl, S Riedel
arXiv preprint arXiv:1702.04521, 2017
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
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
Neural random forests
G Biau, E Scornet, J Welbl
arXiv preprint arXiv:1604.07143, 2016
Casting random forests as artificial neural networks (and profiting from it)
J Welbl
German Conference on Pattern Recognition, 765-771, 2014
Neural random forests
G Biau, E Scornet, J Welbl
Sankhya A 81 (2), 347-386, 2019
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
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
Making sense of sensory input
R Evans, J Hernández-Orallo, J Welbl, P Kohli, M Sergot
Artificial Intelligence 293, 103438, 2021
Jack the reader-A machine reading framework
D Weissenborn, P Minervini, T Dettmers, I Augenstein, J Welbl, ...
arXiv preprint arXiv:1806.08727, 2018
A factorization machine framework for testing bigram embeddings in knowledgebase completion
J Welbl, G Bouchard, S Riedel
arXiv preprint arXiv:1604.05878, 2016
Undersensitivity in neural reading comprehension
J Welbl, P Minervini, M Bartolo, P Stenetorp, S Riedel
arXiv preprint arXiv:2003.04808, 2020
Event detection by feature unpredictability in phase-contrast videos of cell cultures
M Kandemir, JC Rubio, U Schmidt, C Wojek, J Welbl, B Ommer, ...
International Conference on Medical Image Computing and Computer-Assisted …, 2014
Evaluating the Apperception Engine
R Evans, J Hernandez-Orallo, J Welbl, P Kohli, M Sergot
arXiv preprint arXiv:2007.05367, 2020
Towards Verified Robustness under Text Deletion Interventions
J Welbl, PS Huang, R Stanforth, S Gowal, KD Dvijotham, M Szummer, ...
Training Datasets for Machine Reading Comprehension and Their Limitations
J Welbl
UCL (University College London), 2020
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