Blundell, Charles

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2016) · arXiv

Adversarial Robustness in Machine LearningGaussian Processes and Bayesian Inference

Cited by 3,666

Weight Uncertainty in Neural Networks

Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. (2015) · arXiv

Gaussian Processes and Bayesian InferenceAdversarial Robustness in Machine Learning

Cited by 1,342

Reinforcement learning, fast and slow

Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019) · Trends in Cognitive Sciences

Reinforcement Learning in RoboticsNeural and Behavioral Psychology Studies

Cited by 667

Learning to reinforcement learn

Wang, J. X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J. Z., Munos, R., Blundell, C., Kumaran, D., & Botvinick, M. (2016) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 579

Darla Improving zero-shot transfer in reinforcement learning

Higgins, I., Pal, A., Rusu, A. A., Matthey, L., Burgess, C. P., Pritzel, A., Botvinick, M., Blundell, C., & Lerchner, A. (2017) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 219

Never Give Up: Learning Directed Exploration Strategies

Badia, A. P., Sprechmann, P., Vitvitskyi, A., Guo, D., Piot, B., Kapturowski, S., Tieleman, O., Arjovsky, M., Pritzel, A., Bolt, A., et al. (2020) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 170

Model-Free Episodic Control

Blundell, C., Uria, B., Pritzel, A., Li, Y., Ruderman, A., Leibo, J. Z., Rae, J., Wierstra, D., & Hassabis, D. (2016) · arXiv

Reinforcement Learning in RoboticsMemory and Neural Mechanisms

Cited by 162

Bayesian Recurrent Neural Networks

Fortunato, M., Blundell, C., & Vinyals, O. (2017) · arXiv

Gaussian Processes and Bayesian InferenceTopic Modeling

Cited by 145

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

Coordination among neural modules through a shared global workspace

Goyal, A., Didolkar, A., Lamb, A., Badola, K., Ke, N. R., Rahaman, N., Binas, J., Blundell, C., Mozer, M., & Bengio, Y. (2021) · arXiv

Neural Networks and ApplicationsPsychiatry, Mental Health, Neuroscience

Cited by 59

A model of egocentric to allocentric understanding in mammalian brains

Uria, B., Ibarz, B., Banino, A., Zambaldi, V., Kumaran, D., Hassabis, D., Barry, C., & Blundell, C. (2020) · bioRxiv (Cold Spring Harbor Laboratory)

Memory and Neural MechanismsSpatial Cognition and Navigation

Cited by 48

Neural algorithmic reasoning

Veličković, P., & Blundell, C. (2021) · Patterns

Neural Networks and ApplicationsArtificial Intelligence Applications

Cited by 39

PonderNet: Learning to Ponder

Banino, A., Balaguer, J., & Blundell, C. (2021) · arXiv

Neural Networks and ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 24

Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?

Tomasev, N., Bica, I., McWilliams, B., Buesing, L., Pascanu, R., Blundell, C., & Mitrovic, J. (2022) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 22

Improving fine-grained understanding in image-text pre-training

Bica, I., Ilić, A., Bauer, M., Erdogan, G., Bošnjak, M., Kaplanis, C., Gritsenko, A. A., Minderer, M., Blundell, C., Pascanu, R., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 4