5 papers · 1 filter
In Two Minds about Lifelong Learning: Exploring Hemispheric Redundancy and Specialisation in Neural Models
Benjamin Smith, Levin Kuhlmann, Kaushik Roy +1
Persistent intelligent systems require the ability to learn continually, but current machine learning approaches face significant challenges in this area compared to biological lea…
Not All Timesteps Matter Equally: Selective Alignment Knowledge Distillation for Spiking Neural Networks
Kai Sun, Peibo Duan, Yongsheng Huang +4
Spiking neural networks (SNNs), which are brain-inspired and spike-driven, achieve high energy efficiency. However, a performance gap between SNNs and artificial neural networks (A…
ARROW: Augmented Replay for RObust World models
Abdulaziz Alyahya, Abdallah Al Siyabi, Markus R. Ernst +3
Continual reinforcement learning challenges agents to acquire new skills while retaining previously learned ones with the goal of improving performance in both past and future task…
Graceful task adaptation with a bi-hemispheric RL agent
Grant Nicholas, Levin Kuhlmann, Gideon Kowadlo
In humans, responsibility for performing a task gradually shifts from the right hemisphere to the left. The Novelty-Routine Hypothesis (NRH) states that the right and left hemisphe…
Augmenting Replay in World Models for Continual Reinforcement Learning
Luke Yang, Levin Kuhlmann, Gideon Kowadlo
Continual RL requires an agent to learn new tasks without forgetting previous ones, while improving on both past and future tasks. The most common approaches use model-free algorit…