Garnelo, Marta

Interaction between tumour‐infiltrating B cells and T cells controls the progression of hepatocellular carcinoma

Garnelo, M., Tan, A., Her, Z., Yeong, J., Lim, C. J., Chen, J., Lim, K. H., Weber, A., Chow, P., Chung, A., et al. (2017) · Gut

Cancer Immunotherapy and BiomarkersHepatocellular Carcinoma Treatment and Prognosis

Cited by 461

Conditional Neural Processes

Garnelo, M., Rosenbaum, D., Maddison, C. J., Ramalho, T., Saxton, D., Shanahan, M., Teh, Y. W., Rezende, D. J., & Eslami, S. M. A. (2018) · arXiv

Gaussian Processes and Bayesian InferenceNeural Networks and Applications

Cited by 287

Reconciling deep learning with symbolic artificial intelligence: representing objects and relations

Garnelo, M., & Shanahan, M. (2019) · Current Opinion in Behavioral Sciences

Logic, Reasoning, and KnowledgeNeural Networks and Applications

Cited by 255

Towards Deep Symbolic Reinforcement Learning

Garnelo, M., Arulkumaran, K., & Shanahan, M. (2016) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 146

Open-ended Learning in Symmetric Zero-sum Games

Balduzzi, D., Garnelo, M., Bachrach, Y., Czarnecki, W. M., Perolat, J., Jaderberg, M., & Graepel, T. (2019) · arXiv

Artificial Intelligence in GamesEvolutionary Algorithms and Applications

Cited by 88

Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity

Garnelo, M., Czarnecki, W. M., Liu, S., Tirumala, D., Oh, J., Gidel, G., van Hasselt, H., & Balduzzi, D. (2021) · arXiv

Artificial Intelligence in GamesEvolutionary Algorithms and Applications

Cited by 3