BLOG
27 February 2021
Agents are becoming more artificially intelligent but a number of problems must be solved before we can unleash them into our physical world (i.e. robots) and trust them in our digital one.
2021
9 December 2020
A novel framework which uses individual sample losses as error measures to determine the relative difficulty of samples in a dataset. Can be plugged on top of existing neural network models to implement curriculum learning for any task, even with noisy datasets.
2020
4 August 2020
A new method called FORCE achieves extreme sparsity in artificial neural networks by progressively removing up to 99.9% of parameters at initialization, making it a promising candidate for training networks on edge devices (like drones or smartphones).
2020
10 July 2020
A overview of current research methods in lifelong learning (rehearse, grow, regularize), the advantages and drawback of each and where we see future work heading.
2020