| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 2070 | 2023 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 614 | 2024 |
| Improving alignment of dialogue agents via targeted human judgements A Glaese, N McAleese, M Trębacz, J Aslanides, V Firoiu, T Ewalds, ... arXiv preprint arXiv:2209.14375, 2022 | 436 | 2022 |
| Teaching language models to support answers with verified quotes J Menick, M Trebacz, V Mikulik, J Aslanides, F Song, M Chadwick, ... arXiv preprint arXiv:2203.11147, 2022 | 204 | 2022 |
| Open-ended learning leads to generally capable agents OEL Team, A Stooke, A Mahajan, C Barros, C Deck, J Bauer, J Sygnowski, ... arXiv preprint arXiv:2107.12808, 2021 | 164 | 2021 |
| LLM Critics Help Catch LLM Bugs N McAleese, RM Pokorny, JFC Uribe, E Nitishinskaya, M Trebacz, J Leike arXiv preprint arXiv:2407.00215, 2024 | 26 | 2024 |
| Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases P Scherer, M Trębacz, N Simidjievski, R Viñas, Z Shams, HA Terre, ... Bioinformatics 38 (5), 1320-1327, 2022 | 4 | 2022 |
| Using ontology embeddings for structural inductive bias in gene expression data analysis M Trębacz, Z Shams, M Jamnik, P Scherer, N Simidjievski, HA Terre, ... Machine Learning in Computational Biology (MLCB) meeting, 2020 | 3 | 2020 |
| More than a label: machine-assisted data interpretation M Trebacz, L Church Participatory Approaches to Machine Learning Workshop (ICML), 2020 | 1* | 2020 |
| Incorporating network based protein complex discovery into automated model construction P Scherer, M Trȩbacz, N Simidjievski, Z Shams, HA Terre, P Liò, ... Machine Learning in Computational Biology (MLCB) meeting, 2020 | | 2020 |
