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

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]

References

  1. ^ a b Alex Graves publications indexed by Google Scholar Edit this at Wikidata
  2. ^ Graves, Alex (2008). Supervised sequence labelling with recurrent neural networks (PDF) (PhD thesis). Technischen Universitat Munchen. 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. ^ Santiago Fernandez, Alex Graves, and Jürgen Schmidhuber (2007). An application of recurrent neural networks to discriminative keyword spotting. Proceedings of ICANN (2), pp. 220–229. doi:10.1007/978-3-540-74695-9_23
  7. ^ Graves, Alex; and Schmidhuber, Jürgen; Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks, in Bengio, Yoshua; Schuurmans, Dale; Lafferty, John; Williams, Chris K. I.; and Culotta, Aron (eds.), Advances in Neural Information Processing Systems 22 (NIPS'22), December 7th–10th, 2009, Vancouver, BC, Neural Information Processing Systems (NIPS) Foundation, 2009, pp. 545–552 doi:10.5555/2981780.2981848
  8. ^ A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, J. Schmidhuber. A Novel Connectionist System for Improved Unconstrained Handwriting Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 5, 2009. 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. ^ Graves, Alex; Wayne, Greg; Reynolds, Malcolm; Harley, Tim; Danihelka, Ivo; Grabska-Barwińska, Agnieszka; Colmenarejo, Sergio Gómez; Grefenstette, Edward; Ramalho, Tiago (2016-10-12). "Hybrid computing using a neural network with dynamic external memory". Nature. 538 (7626): 471–476. Bibcode:2016Natur.538..471G. doi:10.1038/nature20101. ISSN 1476-4687. PMID 27732574. S2CID 205251479.
  13. ^ "Differentiable neural computers | DeepMind". DeepMind. Retrieved 2016-10-19.
  14. ^ Alex Graves; Rupesh Kumar Srivastava; Timothy Atkinson; Faustino Gomez (14 August 2023), Bayesian Flow Networks (PDF), arXiv:2308.07037, doi:10.48550/ARXIV.2308.07037, Wikidata Q121625910
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