Genewein, Tim

The Power of Ensembles for Active Learning in Image Classification

Beluch, W. H., Genewein, T., Nurnberger, A., & Kohler, J. M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Machine Learning and AlgorithmsDomain Adaptation and Few-Shot Learning

Cited by 655

Meta-learning of Sequential Strategies

Ortega, P. A., Wang, J. X., Rowland, M., Genewein, T., Kurth-Nelson, Z., Pascanu, R., Heess, N., Veness, J., Pritzel, A., Sprechmann, P., et al. (2019) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 47

Language Modeling Is Compression

Delétang, G., Ruoss, A., Duquenne, P.-A., Catt, E., Genewein, T., Mattern, C., Grau-Moya, J., Wenliang, L. K., Aitchison, M., Orseau, L., et al. (2023) · arXiv

Topic ModelingAlgorithms and Data Compression

Cited by 39

Meta-trained agents implement Bayes-optimal agents

Mikulik, V., Delétang, G., McGrath, T., Genewein, T., Martic, M., Legg, S., & Ortega, P. A. (2020) · arXiv

Advanced Bandit Algorithms ResearchReinforcement Learning in Robotics

Cited by 24

Randomized Positional Encodings Boost Length Generalization of Transformers

Ruoss, A., Delétang, G., Genewein, T., Grau-Moya, J., Csordás, R., Bennani, M., Legg, S., & Veness, J. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1

LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations

Ruoss, A., Pardo, F., Chan, H., Li, B., Mnih, V., & Genewein, T. (2024) · arXiv

Reinforcement Learning in RoboticsMultimodal Machine Learning Applications

Cited by 0