DeepMind x UCL | Deep Learning Lectures | 8/12 | Attention and Memory in Deep Learning
977Attention and memory have emerged as two vital new components of deep learning over the last few years. This lecture by DeepMind Research Scientist Alex Graves covers a broad range of contemporary attention mechanisms, including the implicit attention present in any deep network, as well as both discrete and differentiable variants of explicit attention. It then discusses networks with external memory and explains how attention provides them with selective recall. It briefly reviews transformers, a particularly successful type of attention network, and lastly looks at variable computation time, which can be seen as a form of 'attention by concentration'.
Download the slides here:
https://storage.googleapis.com/deepmind-media/UCLxDeepMind_2020/L8%20-%20UCLxDeepMind%20DL2020.pdf
Find out more about how DeepMind increases access to science here:
https://deepmind.com/about#access_to_science
Speaker Bio:
Alex Graves completed a BSc in Theoretical Physics at the University of Edinburgh, Part III Maths at the University of Cambridge and a PhD in artificial intelligence at IDSIA with Jürgen Schmidhuber, followed by postdocs at the Technical University of Munich and with Geoff Hinton at the University of Toronto. He is now a research scientist at DeepMind. His contributions include the Connectionist Temporal Classification algorithm for sequence labelling (widely used for commercial speech and handwriting recognition), stochastic gradient variational inference, the Neural Turing Machine / Differentiable Neural Computer architectures, and the A2C algorithm for reinforcement learning.
About the lecture series:
The Deep Learning Lecture Series is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence. Over the past decade, Deep Learning has evolved as the leading artificial intelligence paradigm providing us with the ability to learn complex functions from raw data at unprecedented accuracy and scale. Deep Learning has been applied to problems in object recognition, speech recognition, speech synthesis, forecasting, scientific computing, control and many more. The resulting applications are touching all of our lives in areas such as healthcare and medical research, human-computer interaction, communication, transport, conservation, manufacturing and many other fields of human endeavour. In recognition of this huge impact, the 2019 Turing Award, the highest honour in computing, was awarded to pioneers of Deep Learning.
In this lecture series, research scientists from leading AI research lab, DeepMind, deliver 12 lectures on an exciting selection of topics in Deep Learning, ranging from the fundamentals of training neural networks via advanced ideas around memory, attention, and generative modelling to the important topic of responsible innovation.