Maja Trębacz
Maja Trębacz
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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
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
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
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
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
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
Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases
P Scherer, M Trębacz, N Simidjievski, R Viņas, Z Shams, HA Terre, ...
Bioinformatics 38 (5), 1320-1327, 2022
More than a label: machine-assisted data interpretation
M Trebacz, L Church
Participatory Approaches to Machine Learning Workshop (ICML), 2020
LLM Critics Help Catch LLM Bugs
N McAleese, RM Pokorny, JFC Uribe, E Nitishinskaya, M Trebacz, J Leike
arXiv preprint arXiv:2407.00215, 2024
Incorporating network based protein complex discovery into automated model construction
P Scherer, M Trȩbacz, N Simidjievski, Z Shams, HA Terre, P Liō, ...
Machine Learning in Computational Biology (MLCB) meeting, 2020
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