Welbl, Johannes

Complex Embeddings for Simple Link Prediction

Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., & Bouchard, G. (2016) · arXiv

Advanced Graph Neural NetworksTopic Modeling

Cited by 2,100

Competition-Level Code Generation with AlphaCode

Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Lago, A. D., et al. (2022) · arXiv

Software Engineering ResearchTeaching and Learning Programming

Cited by 1,043

Competition-level code generation with AlphaCode

Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al. (2022) · Science

Software Engineering ResearchTeaching and Learning Programming

Cited by 1,043

Training Compute-Optimal Large Language Models

Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. D. L., Hendricks, L. A., Welbl, J., Clark, A., et al. (2022) · Advances in Neural Information Processing Systems 35

Topic ModelingNatural Language Processing Techniques

Cited by 932

Training Compute-Optimal Large Language Models

Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. D. L., Hendricks, L. A., Welbl, J., Clark, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 929

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Rae, J. W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, F., Aslanides, J., Henderson, S., Ring, R., Young, S., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 235

Collaboration between clinicians and vision–language models in radiology report generation

Tanno, R., Barrett, D. G. T., Sellergren, A., Ghaisas, S., Dathathri, S., See, A., Welbl, J., Lau, C., Tu, T., Azizi, S., et al. (2025) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 186

Scalable watermarking for identifying large language model outputs

Dathathri, S., See, A., Ghaisas, S., Huang, P.-S., McAdam, R., Welbl, J., Bachani, V., Kaskasoli, A., Stanforth, R., Matejovicova, T., et al. (2024) · Nature

Advanced Steganography and Watermarking TechniquesAdversarial Robustness in Machine Learning

Cited by 132

Reducing Sentiment Bias in Language Models via Counterfactual Evaluation

Huang, P.-S., Zhang, H., Jiang, R., Stanforth, R., Welbl, J., Rae, J., Maini, V., Yogatama, D., & Kohli, P. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020

Sentiment Analysis and Opinion MiningEthics and Social Impacts of AI

Cited by 124

Challenges in Detoxifying Language Models

Welbl, J., Glaese, A., Uesato, J., Dathathri, S., Mellor, J., Hendricks, L. A., Anderson, K., Kohli, P., Coppin, B., & Huang, P.-S. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021

Hate Speech and Cyberbullying DetectionEthics and Social Impacts of AI

Cited by 112

UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)

Yoneda, T., Mitchell, J., Welbl, J., Stenetorp, P., & Riedel, S. (2018) · Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)

Topic ModelingInformation Retrieval and Search Behavior

Cited by 101

Making sense of sensory input

Evans, R., Hernandez-Orallo, J., Welbl, J., Kohli, P., & Sergot, M. (2019) · arXiv

AI-based Problem Solving and PlanningBayesian Modeling and Causal Inference

Cited by 58

Making sense of sensory input

Evans, R., Hernández-Orallo, J., Welbl, J., Kohli, P., & Sergot, M. (2021) · Artificial Intelligence

AI-based Problem Solving and PlanningBayesian Modeling and Causal Inference

Cited by 58

Knowledge Graph Completion via Complex Tensor Factorization

Trouillon, T., Dance, C. R., Welbl, J., Riedel, S., Gaussier, É., & Bouchard, G. (2017) · arXiv

Advanced Graph Neural NetworksRecommender Systems and Techniques

Cited by 41