David Rawlinson

https://brickpixels.net/2018/09/01/robot-exploration/ By Ben Teoh

Research Roadmap: 2020-2021

We’ve just undertaken a review and refresh of our research roadmap! The topics and approach we will take in the coming year are all laid out in a new page: Research Roadmap Our primary topics for the coming year include: Continual Few-Shot Learning (CFSL) via our Episodic memory system Using… Read More »Research Roadmap: 2020-2021

5th WBAI Hackathon

The Whole Brain Architecture Initiative (WBAI) aims to foster research into architectural approaches to general intelligence. They have held a series of events – the Hackathons – that encourage and support the development of new models which provide working solutions to questions about the possible architecture of general intelligence in… Read More »5th WBAI Hackathon

Biologically-plausible learning rules for artificial neural networks

Artificial neural networks (ANNs) – are conceptually simple; the combination of inputs and weights in a classical ANN can be represented as a single matrix product operation followed by an elementwise nonlinearity. However, as the number of learned parameters increases, it becomes very difficult to train these networks effectively. Most… Read More »Biologically-plausible learning rules for artificial neural networks

Learning partially-observable higher-order sequences using local and immediate credit assignment

One of our key projects is a memory system that can learn to associate distant cause & effect while only using local, immediate & unsupervised credit assignment. Our approach is called RSM – Recurrent Sparse Memory. We recently uploaded a preprint describing RSM. This is the first of several blog… Read More »Learning partially-observable higher-order sequences using local and immediate credit assignment

Learning distant cause and effect using only local and immediate credit assignment

We’ve uploaded a new paper to arXiv presenting our algorithm for biologically-plausible learning of distant cause & effect using only local and immediate credit assignment. This is a big step for us – it ticks almost all our requirements for a general purpose representation. The training regime is unsupervised &… Read More »Learning distant cause and effect using only local and immediate credit assignment

Predictive Capsules Networks – Research update

We recently talked about Capsules networks and equivariances. NB: If you’re not familiar with Capsules networks, read this first. Our primary objective with Capsules networks is to exploit their enhanced generalization abilities. However, what we’ve found instead raises new questions about how generalization can be measured and whether Capsules networks are… Read More »Predictive Capsules Networks – Research update

Understanding Equivariance

We are exploring the nature of equivariance, a concept that is now closely associated with the capsules network architecture (see key papers Sabour et al, and Hinton et al). Machine learning representations that capture equivariance must learn the way that patterns in the input vary together, in addition to statistical clusters in… Read More »Understanding Equivariance

Convolutional Competitive Learning vs. Sparse Autoencoders (2/2)

This is the second part of our comparison between convolutional competitive learning and convolutional or fully-connected sparse autoencoders. To understand our motivation for this comparison, have a look at the first article. We decided to compare two specific algorithms that tick most of the features we require: K-Sparse autoencoders, and… Read More »Convolutional Competitive Learning vs. Sparse Autoencoders (2/2)