Danihelka, Ivo

Grandmaster level in StarCraft II using multi-agent reinforcement learning

Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., et al. (2019) · Nature

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 3,455

Hybrid computing using a neural network with dynamic external memory

Graves, A., Wayne, G., Reynolds, M., Harley, T., Danihelka, I., Grabska-Barwińska, A., Colmenarejo, S. G., Grefenstette, E., Ramalho, T., Agapiou, J., et al. (2016) · Nature

Neural Networks and ApplicationsTopic Modeling

Cited by 1,412

Neural Turing Machines

Graves, A., Wayne, G., & Danihelka, I. (2014) · arXiv

Neural Networks and ApplicationsNeural Networks and Reservoir Computing

Cited by 1,058

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

The Cramer Distance as a Solution to Biased Wasserstein Gradients

Bellemare, M. G., Danihelka, I., Dabney, W., Mohamed, S., Lakshminarayanan, B., Hoyer, S., & Munos, R. (2017) · arXiv

Generative Adversarial Networks and Image SynthesisGeometric Analysis and Curvature Flows

Cited by 223

Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes

Rae, J. W., Hunt, J. J., Harley, T., Danihelka, I., Senior, A., Wayne, G., Graves, A., & Lillicrap, T. P. (2016) · arXiv

Neural Networks and ApplicationsTopic Modeling

Cited by 89