Alex Graves (computer scientist)

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Alex Graves
Alma mater
Known for
Scientific career
Fields
Institutions
DeepMind
University of Toronto
Dalle Molle Institute for Artificial Intelligence Research
ThesisSupervised sequence labelling with recurrent neural networks (2008)
Doctoral advisorJürgen Schmidhuber
Websitewww.cs.toronto.edu/~graves Edit this at Wikidata

Alex Graves is a computer scientist and research scientist at

DeepMind.[1]

Education

Graves earned his

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]

References

  1. ^ a b Alex Graves publications indexed by Google Scholar Edit this at Wikidata
  2. OCLC 1184353689
    .
  3. ^ "Alex Graves". Canadian Institute for Advanced Research. Archived from the original on 1 May 2015.
  4. ^ "Marginally Interesting: What is going on with DeepMind and Google?". Blog.mikiobraun.de. 28 January 2014. Retrieved May 17, 2016.
  5. ^ Alex Graves, Santiago Fernandez, Faustino Gomez, and Jürgen Schmidhuber (2006). Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural nets. Proceedings of ICML’06, pp. 369–376.
  6. doi:10.1007/978-3-540-74695-9_23
  7. doi:10.5555/2981780.2981848
  8. doi:10.1109/TPAMI.2008.137
  9. ^ Google Research Blog. The neural networks behind Google Voice transcription. August 11, 2015. By Françoise Beaufays http://googleresearch.blogspot.co.at/2015/08/the-neural-networks-behind-google-voice.html
  10. ^ Google Research Blog. Google voice search: faster and more accurate. September 24, 2015. By Haşim Sak, Andrew Senior, Kanishka Rao, Françoise Beaufays and Johan Schalkwyk – Google Speech Team http://googleresearch.blogspot.co.uk/2015/09/google-voice-search-faster-and-more.html
  11. ^ "Google's Secretive DeepMind Startup Unveils a "Neural Turing Machine"". Retrieved May 17, 2016.
  12. S2CID 205251479
    .
  13. ^ "Differentiable neural computers | DeepMind". DeepMind. Retrieved 2016-10-19.
  14. Wikidata Q121625910
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