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We are very happy to announce that we’ve been awarded an Artificial Intelligence for Decision Making Initiative grant from Defence Science Institute on Effective updating of deep learning models with limited new data. More specifically, the grant is for the Continual Few-Shot Learning project that we blogged about recently.
In continual learning, the neural network learns from a stream of data, acquiring new knowledge incrementally. It’s not possible to assume an i.i.d. stationary dataset available in one batch. Catastrophic forgetting of previous knowledge is a well known challenge. A wide variety of approaches fall broadly into 3 categories [6]: Regularization-based methods, Parameter isolation methods…
We were really excited to present a couple of papers recently at IJCNN, the International Joint Conference on Neural Networks. One of those papers was an extension to the work that we previously published on AHA, Artificial Hippocampal Algorithm based on Complementary Learning Systems by O’Reilly and McClelland (see that paper summary blog post here).…
We’re very happy to report that we recently published a preprint on AHA, an ‘Artificial Hippocampal Algorithm’ for Episodic Machine Learning. It’s the culmination of a multi-year research project and is a starting point for the next wave of developments. This article describes the motivation for developing AHA and a brief description of the research.…
Over the last few years, there have been several breakthroughs and exciting new research directions in Reinforcement Learning, Hippocampus Inspired Architectures, Attention and Few-Shot Learning. There has been a move towards multi-component, heterogeneous, stateful architectures, many guided by ideas from cognitive sciences. Google DeepMind and Google Brain are leading the way making progress on several…
ML Today Today’s Machine Learning has demonstrated unprecedented performance in what seems like every application thrown at it. Almost all the success has been based on advanced memory systems that can learn to recognise an input based on a large number of training examples. This is the equivalent to memory for facts. The biological terminology…