45 citations · 58 across the 10 of their papers we have counts for
10 papers
Active perception and disentangled representations allow continual, episodic zero and few-shot learning
David Rawlinson, Gideon Kowadlo
Generalization is often regarded as an essential property of machine learning systems. However, perhaps not every component of a system needs to generalize. Training models for gen…
HRM-Agent: Training a recurrent reasoning model in dynamic environments using reinforcement learning
Long H Dang, David Rawlinson
The Hierarchical Reasoning Model (HRM) has impressive reasoning abilities given its small size, but has only been applied to supervised, static, fully-observable problems. One of H…
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…
Deep learning in a bilateral brain with hemispheric specialization
Chandramouli Rajagopalan, David Rawlinson, Elkhonon Goldberg +1
The brains of all bilaterally symmetric animals on Earth are divided into left and right hemispheres. The anatomy and functionality of the hemispheres have a large degree of overla…
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.…
Long Distance Relationships without Time Travel: Boosting the Performance of a Sparse Predictive Autoencoder in Sequence Modeling
Jeremy Gordon, David Rawlinson, Subutai Ahmad
In sequence learning tasks such as language modelling, Recurrent Neural Networks must learn relationships between input features separated by time. State of the art models such as…
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…