Search Results for author:
Found 9 papers, 6 papers with code
Augmenting Replay in World Models for Continual Reinforcement Learning
Also, the concept of replay comes from biological inspiration, where evidence suggests that replay is applied to a world model, which implies model-based RL -- and model-based RL should have benefits for continual RL, where it is possible to exploit knowledge independent of the policy.
Left/Right Brain, human motor control and the implications for robotics
The models were trained and tested on two tasks common in the human motor control literature: the random reach task, suited to the dominant system, a model with better coordination, and the hold position task, suited to the non-dominant system, a model with more stable movement.
Deep learning in a bilateral brain with hemispheric specialization
We took a different approach and aimed to understand how dual hemispheres in a bilateral architecture interact to perform well in a given task.
Expanding continual few-shot learning benchmarks to include recognition of specific instances
Continual learning and few-shot learning are important frontiers in progress towards broader Machine Learning (ML) capabilities.
One-shot learning for the long term: consolidation with an artificial hippocampal algorithm
The results demonstrated that with the addition of AHA, the system could learn in one-shot and consolidate the knowledge for the long term without catastrophic forgetting.
Unsupervised One-shot Learning of Both Specific Instances and Generalised Classes with a Hippocampal Architecture
We propose an extension to the standard Omniglot classification-generalisation framework that additionally tests the ability to distinguish specific instances after one exposure and introduces noise and occlusion corruption.
AHA! an 'Artificial Hippocampal Algorithm' for Episodic Machine Learning
AHA is a biologically-plausible computational model of the Hippocampus.
Learning distant cause and effect using only local and immediate credit assignment
We present a recurrent neural network memory that uses sparse coding to create a combinatoric encoding of sequential inputs.
Sparse Unsupervised Capsules Generalize Better
We show that unsupervised training of latent capsule layers using only the reconstruction loss, without masking to select the correct output class, causes a loss of equivariances and other desirable capsule qualities.
