Category Archives

48 Articles

Experiment/Theory

Understanding Equivariance

Posted by David Rawlinson on
Understanding Equivariance

Effect of univariate perturbation of hidden layer resulting from Predictive Capsules algorithm

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 a ...

Theory

New Ideas in Reinforcement Learning

Posted by Yi-Ling Hwong on

Today’s blog post is about Reinforcement Learning (RL), a concept that is very relevant to Artificial General Intelligence. The goal of RL is to create an agent that can learn to behave optimally in an environment by observing the consequences - rewards - of its own actions. By incorporating deep ...

Theory

Continuous Learning

Posted by Gideon Kowadlo on
Continuous Learning

  The standard machine learning approach is to learn to accomplish a specific task with an associated dataset. A model is trained using the dataset and is only able to perform that one task. This is in stark contrast to animals which continue to learn throughout life and accumulate and re-pu ...