Alex Graves is a computer scientist and research scientist at DeepMind.[1]
Alex Graves | |
|---|---|
| Alma mater | |
| Known for | |
| Scientific career | |
| Fields | |
| Institutions | DeepMind University of Toronto Dalle Molle Institute for Artificial Intelligence Research |
| Thesis | Supervised sequence labelling with recurrent neural networks (2008) |
| Doctoral advisor | Jürgen Schmidhuber |
| Website | www |
Education
Graves earned his Bachelor of Science degree in Theoretical Physics from the University of Edinburgh[when?] and a PhD in artificial intelligence from the Technical University of Munich supervised by Jürgen Schmidhuber at the Dalle Molle Institute for Artificial Intelligence Research.[2][3]
Career and research
After his PhD, Graves was postdoc working with Schmidhuber at the Technical University of Munich and Geoffrey Hinton[4] at the University of Toronto.
At the Dalle Molle Institute for Artificial Intelligence Research, Graves trained long short-term memory (LSTM) neural networks by a novel method called connectionist temporal classification (CTC).[5] This method outperformed traditional speech recognition models in certain applications.[6] In 2009, his CTC-trained LSTM was the first recurrent neural network (RNN) to win pattern recognition contests, winning several competitions in connected handwriting recognition.[7][8]Google uses CTC-trained LSTM for speech recognition on the smartphone.[9][10]
Graves is also the creator of neural Turing machines[11] and the closely related differentiable neural computer.[12][13]In 2023, he published the paper Bayesian Flow Networks.[14]