Search Results for author:
Found 9 papers, 3 papers with code
Dynamic Knowledge Injection for AIXI Agents
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.
Variational Inference for Scalable 3D Object-centric Learning
We tackle the task of scalable unsupervised object-centric representation learning on 3D scenes.
A Direct Approximation of AIXI Using Logical State Abstractions
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.
Factored Conditional Filtering: Tracking States and Estimating Parameters in High-Dimensional Spaces
This paper introduces the factored conditional filter, a new filtering algorithm for simultaneously tracking states and estimating parameters in high-dimensional state spaces.
Spatially Invariant Unsupervised 3D Object-Centric Learning and Scene Decomposition
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.
Towards Fair and Privacy-Preserving Federated Deep Models
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.
Context Tree Switching
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
Reinforcement Learning via AIXI Approximation
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent.
General Reinforcement Learning
Open-Ended Question Answering
+2
A Monte Carlo AIXI Approximation
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent.
General Reinforcement Learning
Open-Ended Question Answering
+2
