Nat Dilokthanakul
Nat Dilokthanakul
Faculty of Information Technology, KMITL
Verified email at - Homepage
Cited by
Cited by
Deep unsupervised clustering with gaussian mixture variational autoencoders
N Dilokthanakul, PAM Mediano, M Garnelo, MCH Lee, H Salimbeni, ...
arXiv preprint arXiv:1611.02648, 2016
Feature control as intrinsic motivation for hierarchical reinforcement learning
N Dilokthanakul, C Kaplanis, N Pawlowski, M Shanahan
IEEE transactions on neural networks and learning systems 30 (11), 3409-3418, 2019
MetaSleepLearner: A pilot study on fast adaptation of bio-signals-based sleep stage classifier to new individual subject using meta-learning
N Banluesombatkul, P Ouppaphan, P Leelaarporn, P Lakhan, ...
IEEE Journal of Biomedical and Health Informatics 25 (6), 1949-1963, 2020
Classifying options for deep reinforcement learning
K Arulkumaran, N Dilokthanakul, M Shanahan, AA Bharath
IJCAI Workshop on Deep Reinforcement Learning: Frontiers and Challenges, 2016
An explicit local and global representation disentanglement framework with applications in deep clustering and unsupervised object detection
R Charakorn, Y Thawornwattana, S Itthipuripat, N Pawlowski, ...
arXiv preprint arXiv:2001.08957, 2020
Deep Reinforcement Learning with Risk-Seeking Exploration
N Dilokthanakul, M Shanahan
The 15th International Conference on the Simulation of Adaptive Behavior …, 2018
MIN2net: End-to-end multi-task learning for subject-independent motor imagery EEG classification
P Autthasan, R Chaisaen, T Sudhawiyangkul, P Rangpong, ...
IEEE Transactions on Biomedical Engineering 69 (6), 2105-2118, 2021
Visual Goal Human-Robot Communication Framework with Few-Shot Learning: a Case Study in Robot Waiter System
G Sawadwuthikul, T Tothong, T Lodkaew, P Soisudarat, S Nutanong, ...
IEEE Transactions on Industrial Informatics, 2021
CH; Salimbeni, H.; Arulkumaran, K.; and Shanahan, M. 2016. Deep unsupervised clustering with gaussian mixture variational autoencoders
N Dilokthanakul, PAM Mediano, M Garnelo, M Lee
arXiv preprint arXiv:1611.02648, 0
Dynamical state forcing on central pattern generators for efficient robot locomotion control
T Chuthong, B Leung, K Tiraborisute, P Ngamkajornwiwat, ...
International Conference on Neural Information Processing, 799-810, 2020
Investigating partner diversification methods in cooperative multi-agent deep reinforcement learning
R Charakorn, P Manoonpong, N Dilokthanakul
International Conference on Neural Information Processing, 395-402, 2020
Towards better data efficiency in deep reinforcement learning
N Dilokthanakul
Imperial College London, 2018
GRAB: GRAdient-Based Shape-Adaptive Locomotion Control
S Phodapol, T Chuthong, B Leung, A Srisuchinnawong, P Manoonpong, ...
IEEE Robotics and Automation Letters, 2021
Learning to Cooperate with Unseen Agents Through Meta-Reinforcement Learning
R Charakorn, P Manoonpong, N Dilokthanakul
Proceedings of the 20th International Conference on Autonomous Agents and …, 2021
Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result
M Piriyajitakonkij, S Itthipuripat, T Wilaiprasitporn, N Dilokthanakul
arXiv preprint arXiv:2106.10112, 2021
Advanced Collaborative Robots for the Factory of the Future
K Rothomphiwat, A Harnkhamen, T Tothong, T Suthisomboon, ...
2021 IEEE/SICE International Symposium on System Integration (SII), 578-579, 2021
MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification
T Sudhawiyangkul, P Rangpong, S Kiatthaveephong, N Dilokthanakul
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