14 citations · 26 across the 7 of their papers we have counts for
9 papers · 1 filter
Universal Graph Continual Learning
Thanh Duc Hoang, Do Viet Tung, Duy-Hung Nguyen +3
We address catastrophic forgetting issues in graph learning as incoming data transits from one to another graph distribution. Whereas prior studies primarily tackle one setting of…
Beyond Surprise: Improving Exploration Through Surprise Novelty
Hung Le, Kien Do, Dung Nguyen +1
We present a new computing model for intrinsic rewards in reinforcement learning that addresses the limitations of existing surprise-driven explorations. The reward is the novelty…
Functional Indirection Neural Estimator for Better Out-of-distribution Generalization
Kha Pham, Hung Le, Man Ngo +1
The capacity to achieve out-of-distribution (OOD) generalization is a hallmark of human intelligence and yet remains out of reach for machines. This remarkable capability has been…
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…
Memory and attention in deep learning
Hung Le
Intelligence necessitates memory. Without memory, humans fail to perform various nontrivial tasks such as reading novels, playing games or solving maths. As the ultimate goal of ma…
Neurocoder: Learning General-Purpose Computation Using Stored Neural Programs
Hung Le, Svetha Venkatesh
Artificial Neural Networks are uniquely adroit at machine learning by processing data through a network of artificial neurons. The inter-neuronal connection weights represent the l…