45 citations · 53 across the 6 of their papers we have counts for
6 papers
Expanding continual few-shot learning benchmarks to include recognition of specific instances
Gideon Kowadlo, Abdelrahman Ahmed, Amir Mayan +1
Continual learning and few-shot learning are important frontiers in progress toward broader Machine Learning (ML) capabilities. Recently, there has been intense interest in combini…
One-shot learning for the long term: consolidation with an artificial hippocampal algorithm
Gideon Kowadlo, Abdelrahman Ahmed, David Rawlinson
Standard few-shot experiments involve learning to efficiently match previously unseen samples by class. We claim that few-shot learning should be long term, assimilating knowledge…
Unsupervised One-shot Learning of Both Specific Instances and Generalised Classes with a Hippocampal Architecture
Gideon Kowadlo, Abdelrahman Ahmed, David Rawlinson
Established experimental procedures for one-shot machine learning do not test the ability to learn or remember specific instances of classes, a key feature of animal intelligence.…
AHA! an 'Artificial Hippocampal Algorithm' for Episodic Machine Learning
Gideon Kowadlo, Abdelrahman Ahmed, David Rawlinson
The majority of ML research concerns slow, statistical learning of i.i.d. samples from large, labelled datasets. Animals do not learn this way. An enviable characteristic of animal…
Learning distant cause and effect using only local and immediate credit assignment
David Rawlinson, Abdelrahman Ahmed, Gideon Kowadlo
We present a recurrent neural network memory that uses sparse coding to create a combinatoric encoding of sequential inputs. Using several examples, we show that the network can as…
Sparse Unsupervised Capsules Generalize Better
David Rawlinson, Abdelrahman Ahmed, Gideon Kowadlo
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 a…