Imagination Blog

Research into neural network architectures generally prioritises accuracy over efficiency. Certain papers have investigated efficiency (Tan and Le 2020).

The last decade of AI research has been characterised by the exploration of the potential of deep neural networks. The advances we have seen in recent.

Last year I read a fascinating article on LinkedIn about using deep-learning-based super-resolution networks to increase the apparent detail contained in.

Why should current-day AI researchers look into biology and the brain? We discuss in our latest AI research blog post.

This blog post is a result of a collaboration between Visidon, headquartered in Finland and Imagination, based in the UK. Visidon is recognised as an.

Welcome to the second in a series of articles where we explore how Imagination and Humanising Autonomy, a UK-based AI behaviour company, are teaming up to.

A major challenge for fast, low-power inference on neural network accelerators is the size of the models. There is a trend in recent years towards deeper.

Welcome to the first in a series of articles where we explore how Imagination and Humanising Autonomy, a UK-based behaviour AI company, are teaming up to.

The versatility and power of deep learning means that modern neural networks have found myriad applications in areas as diverse as machine translation,.