Casting Random Forests as Artificial Neural Networks (and Profiting from It). - Johannes Welbl (GCPR, 2014)

Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures. - Melih Kandemir; José C. Rubio; Ute Schmidt; Christian Wojek; Johannes Welbl; Björn Ommer; Fred A. Hamprecht (MICCAI (2), 2014)

A Factorization Machine Framework for Testing Bigram Embeddings in Knowledgebase Completion. - Johannes Welbl; Guillaume Bouchard; Sebastian Riedel 001 (AKBC@NAACL-HLT, 2016)

Complex Embeddings for Simple Link Prediction. - Théo Trouillon; Johannes Welbl; Sebastian Riedel 001; Éric Gaussier; Guillaume Bouchard (ICML, 2016)

Neural Random Forests. - Gérard Biau; Erwan Scornet; Johannes Welbl (CoRR, 2016)

Crowdsourcing Multiple Choice Science Questions. - Johannes Welbl; Nelson F. Liu; Matt Gardner 001 (NUT@EMNLP, 2017)

Frustratingly Short Attention Spans in Neural Language Modeling. - Michal Daniluk; Tim Rocktäschel; Johannes Welbl; Sebastian Riedel 001 (ICLR (Poster), 2017)

Knowledge Graph Completion via Complex Tensor Factorization. - Théo Trouillon; Christopher R. Dance; Éric Gaussier; Johannes Welbl; Sebastian Riedel 001; Guillaume Bouchard (J. Mach. Learn. Res., 2017)

Jack the Reader - A Machine Reading Framework. - Dirk Weissenborn; Pasquale Minervini; Isabelle Augenstein; Johannes Welbl; Tim Rocktäschel; Matko Bosnjak; Jeff Mitchell 001; Thomas Demeester; Tim Dettmers; Pontus Stenetorp; Sebastian Riedel 001 (ACL (4), 2018)

UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF). - Takuma Yoneda; Jeff Mitchell 001; Johannes Welbl; Pontus Stenetorp; Sebastian Riedel 001 (FEVER@EMNLP, 2018)

Constructing Datasets for Multi-hop Reading Comprehension Across Documents. - Johannes Welbl; Pontus Stenetorp; Sebastian Riedel 001 (Trans. Assoc. Comput. Linguistics, 2018)

Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation. - Po-Sen Huang; Robert Stanforth; Johannes Welbl; Chris Dyer; Dani Yogatama; Sven Gowal; Krishnamurthy Dvijotham; Pushmeet Kohli (EMNLP/IJCNLP (1), 2019)

Reducing Sentiment Bias in Language Models via Counterfactual Evaluation. - Po-Sen Huang; Huan Zhang; Ray Jiang; Robert Stanforth; Johannes Welbl; Jack Rae; Vishal Maini; Dani Yogatama; Pushmeet Kohli (EMNLP (Findings), 2020)

Undersensitivity in Neural Reading Comprehension. - Johannes Welbl; Pasquale Minervini; Max Bartolo; Pontus Stenetorp; Sebastian Riedel 001 (EMNLP (Findings), 2020)

Towards Verified Robustness under Text Deletion Interventions. - Johannes Welbl; Po-Sen Huang; Robert Stanforth; Sven Gowal; Krishnamurthy (Dj) Dvijotham; Martin Szummer; Pushmeet Kohli (ICLR, 2020)

Beat the AI: Investigating Adversarial Human Annotations for Reading Comprehension. - Max Bartolo; Alastair Roberts; Johannes Welbl; Sebastian Riedel 001; Pontus Stenetorp (CoRR, 2020)

Evaluating the Apperception Engine. - Richard Evans 001; José Hernández-Orallo; Johannes Welbl; Pushmeet Kohli; Marek J. Sergot (CoRR, 2020)

Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension. - Max Bartolo; Alastair Roberts; Johannes Welbl; Sebastian Riedel 001; Pontus Stenetorp (Trans. Assoc. Comput. Linguistics, 2020)

Training datasets for machine reading comprehension and their limitations. - Johannes Welbl (2020)

Challenges in Detoxifying Language Models. - Johannes Welbl; Amelia Glaese; Jonathan Uesato; Sumanth Dathathri; John Mellor; Lisa Anne Hendricks; Kirsty Anderson; Pushmeet Kohli; Ben Coppin; Po-Sen Huang (EMNLP (Findings), 2021)

Making sense of sensory input. - Richard Evans 001; José Hernández-Orallo; Johannes Welbl; Pushmeet Kohli; Marek J. Sergot (Artif. Intell., 2021)

Scaling Language Models: Methods, Analysis & Insights from Training Gopher. - Jack W. Rae; Sebastian Borgeaud; Trevor Cai; Katie Millican; Jordan Hoffmann; H. Francis Song; John Aslanides; Sarah Henderson; Roman Ring; Susannah Young; Eliza Rutherford; Tom Hennigan; et al. (CoRR, 2021)

An empirical analysis of compute-optimal large language model training. - Jordan Hoffmann; Sebastian Borgeaud; Arthur Mensch; Elena Buchatskaya; Trevor Cai; Eliza Rutherford; Diego de Las Casas; Lisa Anne Hendricks; Johannes Welbl; Aidan Clark; Tom Hennigan; Eric Noland; et al. (NeurIPS, 2022)

Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models. - Maribeth Rauh; John Mellor; Jonathan Uesato; Po-Sen Huang; Johannes Welbl; Laura Weidinger; Sumanth Dathathri; Amelia Glaese; Geoffrey Irving; Iason Gabriel; William Isaac 001; Lisa Anne Hendricks (NeurIPS, 2022)

Competition-Level Code Generation with AlphaCode. - Yujia Li 001; David H. Choi; Junyoung Chung; Nate Kushman; Julian Schrittwieser; Rémi Leblond; Tom Eccles; James Keeling; Felix Gimeno; Agustin Dal Lago; Thomas Hubert; Peter Choy; et al. (CoRR, 2022)

Training Compute-Optimal Large Language Models. - Jordan Hoffmann; Sebastian Borgeaud; Arthur Mensch; Elena Buchatskaya; Trevor Cai; Eliza Rutherford; Diego de Las Casas; Lisa Anne Hendricks; Johannes Welbl; Aidan Clark; Tom Hennigan; Eric Noland; et al. (CoRR, 2022)

Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation. - Ryutaro Tanno; David G. T. Barrett; Andrew Sellergren; Sumedh Ghaisas; Sumanth Dathathri; Abigail See; Johannes Welbl; Karan Singhal; Shekoofeh Azizi; Tao Tu 001; Mike Schaekermann; Rhys May; et al. (CoRR, 2023)

Scalable watermarking for identifying large language model outputs. - Sumanth Dathathri; Abigail See; Sumedh Ghaisas; Po-Sen Huang; Rob McAdam; Johannes Welbl; Vandana Bachani; Alex Kaskasoli; Robert Stanforth; Tatiana Matejovicova; Jamie Hayes; Nidhi Vyas; et al. (Nat., 2024)