A Brief Survey of Deep Reinforcement Learning K Arulkumaran, MP Deisenroth, M Brundage, AA Bharath IEEE Signal Processing Magazine 34 (6), 26-38, 2017 | 4332* | 2017 |
Generative Adversarial Networks: An Overview A Creswell, T White, V Dumoulin, K Arulkumaran, B Sengupta, ... IEEE Signal Processing Magazine 35 (1), 53-65, 2018 | 3792 | 2018 |
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders N Dilokthanakul, PAM Mediano, M Garnelo, MCH Lee, H Salimbeni, ... arXiv preprint arXiv:1611.02648, 2016 | 679 | 2016 |
AlphaStar: An Evolutionary Computation Perspective K Arulkumaran, A Cully, J Togelius Genetic and Evolutionary Computation Conference Companion, 314-315, 2019 | 284 | 2019 |
Towards Deep Symbolic Reinforcement Learning M Garnelo, K Arulkumaran, M Shanahan Workshop on Deep Reinforcement Learning, Neural Information Processing Systems, 2016 | 262 | 2016 |
Adaptive Neural Trees R Tanno, K Arulkumaran, DC Alexander, A Criminisi, A Nori International Conference on Machine Learning, 2019 | 194 | 2019 |
On Denoising Autoencoders Trained to Minimise Binary Cross-entropy A Creswell, K Arulkumaran, AA Bharath arXiv preprint arXiv:1708.08487, 2017 | 101 | 2017 |
The Societal Implications of Deep Reinforcement Learning J Whittlestone, K Arulkumaran, M Crosby Journal of Artificial Intelligence Research 70, 1003-1030, 2021 | 51 | 2021 |
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation T Dai, K Arulkumaran, T Gerbert, S Tukra, F Behbahani, AA Bharath Neurocomputing 493, 143-165, 2022 | 34 | 2022 |
Classifying Options for Deep Reinforcement Learning K Arulkumaran, N Dilokthanakul, M Shanahan, AA Bharath Workshop on Deep Reinforcement Learning: Frontiers and Challenges …, 2016 | 32 | 2016 |
Image Synthesis with a Convolutional Capsule Generative Adversarial Network C Bass, T Dai, B Billot, K Arulkumaran, A Creswell, C Clopath, V De Paola, ... International Conference on Medical Imaging with Deep Learning 102, 38-61, 2019 | 31 | 2019 |
On the Link Between Conscious Function and General Intelligence in Humans and Machines A Juliani, K Arulkumaran, S Sasai, R Kanai Transactions on Machine Learning Research, 2022 | 29 | 2022 |
BETH Dataset: Real Cybersecurity Data for Anomaly Detection Research K Highnam, K Arulkumaran, Z Hanif, NR Jennings Conference on Applied Machine Learning for Information Security, 2021 | 25* | 2021 |
Deep Reinforcement Learning for Subpixel Neural Tracking T Dai, M Dubois, K Arulkumaran, J Campbell, C Bass, B Billot, F Uslu, ... International Conference on Medical Imaging with Deep Learning 102, 110-131, 2019 | 23 | 2019 |
Variational Inference for Data-Efficient Model Learning in POMDPs S Tschiatschek, K Arulkumaran, J Stühmer, K Hofmann arXiv preprint arXiv:1805.09281, 2018 | 22 | 2018 |
Improving Sampling from Generative Autoencoders with Markov Chains K Arulkumaran, A Creswell, AA Bharath arXiv preprint arXiv:1610.09296, 2016 | 20* | 2016 |
EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis K Pan, G Hurault, K Arulkumaran, H Williams, RJ Tanaka International Workshop on Machine Learning in Medical Imaging, 2020 | 16 | 2020 |
Privileged Information Dropout in Reinforcement Learning PA Kamienny, K Arulkumaran, F Behbahani, W Böhmer, S Whiteson Beyond “Tabula Rasa” in Reinforcement Learning Workshop, International …, 2020 | 14 | 2020 |
An Analysis of Emergency Tracheal Intubations in Critically Ill Patients by Critical Care Trainees N Arulkumaran, CS McLaren, K Arulkumaran, BJ Philips, M Cecconi Journal of the Intensive Care Society 19 (3), 180-187, 2018 | 12 | 2018 |
Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay T Dai, H Liu, K Arulkumaran, G Ren, AA Bharath Pacific Rim International Conference on Artificial Intelligence, 2021 | 11 | 2021 |