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 |
Alex Graves is a computer scientist and research scientist at DeepMind. [1]
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]
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]
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 |
Alex Graves is a computer scientist and research scientist at DeepMind. [1]
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]
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]