no code implementations • 18 Dec 2023 • Samuel Yang-Zhao, Kee Siong Ng, Marcus Hutter
Prior approximations of AIXI, a Bayesian optimality notion for general reinforcement learning, can only approximate AIXI's Bayesian environment model using an a-priori defined set of models.
no code implementations • 25 Sep 2023 • Tianyu Wang, Kee Siong Ng, Miaomiao Liu
We tackle the task of scalable unsupervised object-centric representation learning on 3D scenes.
no code implementations • 13 Oct 2022 • Samuel Yang-Zhao, Tianyu Wang, Kee Siong Ng
We propose a practical integration of logical state abstraction with AIXI, a Bayesian optimality notion for reinforcement learning agents, to significantly expand the model class that AIXI agents can be approximated over to complex history-dependent and structured environments.
no code implementations • 5 Jun 2022 • Dawei Chen, Samuel Yang-Zhao, John Lloyd, Kee Siong Ng
This paper introduces the factored conditional filter, a new filtering algorithm for simultaneously tracking states and estimating parameters in high-dimensional state spaces.
no code implementations • 10 Jun 2021 • Tianyu Wang, Miaomiao Liu, Kee Siong Ng
Experimental results demonstrate that SPAIR3D has strong scalability and is capable of detecting and segmenting an unknown number of objects from a point cloud in an unsupervised manner.
1 code implementation • 4 Jun 2019 • Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar, Yitong Li, Xingjun Ma, Jiong Jin, Han Yu, Kee Siong Ng
This problem can be addressed by either a centralized framework that deploys a central server to train a global model on the joint data from all parties, or a distributed framework that leverages a parameter server to aggregate local model updates.
1 code implementation • 14 Nov 2011 • Joel Veness, Kee Siong Ng, Marcus Hutter, Michael Bowling
This paper describes the Context Tree Switching technique, a modification of Context Tree Weighting for the prediction of binary, stationary, n-Markov sources.
Information Theory Information Theory
no code implementations • AAAI 2010 2010 • Joel Veness, Kee Siong Ng, Marcus Hutter, David Silver
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent.
General Reinforcement Learning Open-Ended Question Answering +2
2 code implementations • 4 Sep 2009 • Joel Veness, Kee Siong Ng, Marcus Hutter, William Uther, David Silver
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent.
General Reinforcement Learning Open-Ended Question Answering +2