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20102022
most citedPolicy Recognition in the Abstract Hidden Markov Model

121 citations · 404 across the 45 of their papers we have counts for

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34 papers · 1 filter

cs.LG2022

Learning Theory of Mind via Dynamic Traits Attribution

Dung Nguyen, Phuoc Nguyen, Hung Le +3

Machine learning of Theory of Mind (ToM) is essential to build social agents that co-live with humans and other agents. This capacity, once acquired, will help machines infer the m…

cs.LG2021★ 3 cited

Model-Based Episodic Memory Induces Dynamic Hybrid Controls

Hung Le, Thommen Karimpanal George, Majid Abdolshah +2

Episodic control enables sample efficiency in reinforcement learning by recalling past experiences from an episodic memory. We propose a new model-based episodic memory of trajecto…

cs.LG2021★ 1 cited

A Field Guide to Scientific XAI: Transparent and Interpretable Deep Learning for Bioinformatics Research

Thomas P Quinn, Sunil Gupta, Svetha Venkatesh +1

Deep learning has become popular because of its potential to achieve high accuracy in prediction tasks. However, accuracy is not always the only goal of statistical modelling, espe…

cs.LG2021

Plug and Play, Model-Based Reinforcement Learning

Majid Abdolshah, Hung Le, Thommen Karimpanal George +3

Sample-efficient generalisation of reinforcement learning approaches have always been a challenge, especially, for complex scenes with many components. In this work, we introduce P…

cs.LG2021★ 3 cited

A New Representation of Successor Features for Transfer across Dissimilar Environments

Majid Abdolshah, Hung Le, Thommen Karimpanal George +3

Transfer in reinforcement learning is usually achieved through generalisation across tasks. Whilst many studies have investigated transferring knowledge when the reward function ch…

cs.LG2021

ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms

Huong Ha, Sunil Gupta, Santu Rana +1

Machine learning models are being used extensively in many important areas, but there is no guarantee a model will always perform well or as its developers intended. Understanding…