Patents by Inventor Alexander Lloyd GAUNT

Alexander Lloyd GAUNT has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20240071577
    Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for predicting an exchange-correlation energy of an atomic system. The system obtains respective electron-orbital features of the atomic system at each of a plurality of grid points; generates, for each of the plurality of grid points, a respective input feature vector for the electron-orbital features at the grid point; and processes the respective input feature vectors for the plurality of grid points using a neural network to generate a predicted exchange-correlation energy of the atomic system.
    Type: Application
    Filed: January 7, 2022
    Publication date: February 29, 2024
    Inventors: James Kirkpatrick, Brendan Charles McMorrow, David Herbert Phlipp Turban, Alexander Lloyd Gaunt, James Spencer, Alexander Graeme de Garis Matthews, Aron Jonathan Cohen
  • Publication number: 20240036832
    Abstract: A computer program predictor is described which has a processor configured to access a program attribute predictor; and a memory storing a search component configured to search a space of possible programs, to find a program which, given an input data instance and an output data instance, will compute the output data instance from the input data instance, the search being guided by attributes predicted by the attribute predictor given the input data instance and the output data instance.
    Type: Application
    Filed: October 9, 2023
    Publication date: February 1, 2024
    Inventors: Alexander Lloyd GAUNT, Sebastian NOWOZIN, Marc Manuel Johannes BROCKSCHMIDT, Daniel Stefan TARLOW, Matej BALOG
  • Patent number: 11816457
    Abstract: A computer program predictor is described which has a processor configured to access a program attribute predictor; and a memory storing a search component configured to search a space of possible programs, to find a program which, given an input data instance and an output data instance, will compute the output data instance from the input data instance, the search being guided by attributes predicted by the attribute predictor given the input data instance and the output data instance.
    Type: Grant
    Filed: August 28, 2020
    Date of Patent: November 14, 2023
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Alexander Lloyd Gaunt, Sebastian Nowozin, Marc Manuel Johannes Brockschmidt, Daniel Stefan Tarlow, Matej Balog
  • Publication number: 20220222531
    Abstract: A neural network training apparatus is described which has a network of worker nodes each having a memory storing a subgraph of a neural network to be trained. The apparatus has a control node connected to the network of worker nodes. The control node is configured to send training data instances into the network to trigger parallelized message passing operations which implement a training algorithm which trains the neural network. At least some of the message passing operations asynchronously update parameters of individual subgraphs of the neural network at the individual worker nodes.
    Type: Application
    Filed: March 28, 2022
    Publication date: July 14, 2022
    Inventors: Ryota TOMIOKA, Matthew Alastair JOHNSON, Daniel Stefan TARLOW, Samuel Alexander WEBSTER, Dimitrios VYTINIOTIS, Alexander Lloyd GAUNT, Maik RIECHERT
  • Patent number: 11288575
    Abstract: A neural network training apparatus is described which has a network of worker nodes each having a memory storing a subgraph of a neural network to be trained. The apparatus has a control node connected to the network of worker nodes. The control node is configured to send training data instances into the network to trigger parallelized message passing operations which implement a training algorithm which trains the neural network. At least some of the message passing operations asynchronously update parameters of individual subgraphs of the neural network at the individual worker nodes.
    Type: Grant
    Filed: May 18, 2017
    Date of Patent: March 29, 2022
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Ryota Tomioka, Matthew Alastair Johnson, Daniel Stefan Tarlow, Samuel Alexander Webster, Dimitrios Vytiniotis, Alexander Lloyd Gaunt, Maik Riechert
  • Publication number: 20210334655
    Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for predicting one or more properties of a material. One of the methods includes maintaining data specifying a set of known materials each having a respective known physical structure; receiving data specifying a new material; identifying a plurality of known materials in the set of known materials that are similar to the new material; determining a predicted embedding of the new material from at least respective embeddings corresponding to each of the similar known materials; and processing the predicted embedding of the new material using an experimental prediction neural network to predict one or more properties of the new material.
    Type: Application
    Filed: April 26, 2021
    Publication date: October 28, 2021
    Inventors: Annette Ada Nkechinyere Obika, Tian Xie, Victor Constant Bapst, Alexander Lloyd Gaunt, James Kirkpatrick
  • Patent number: 11150875
    Abstract: An editing tool is described which has a memory storing a neural network having been trained to compute a change representation from pairs, each pair comprising a representation of a first version of a content item and a second version of the content item, and for each of the change representations, predict an updated content item from the change representation and the first version of the content item. The editing tool has a processor configured to receive an input content item and to compute an updated version of the input content item according to a change representation, using the neural network.
    Type: Grant
    Filed: September 27, 2018
    Date of Patent: October 19, 2021
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Marc Manuel Johannes Brockschmidt, Miltiadis Allamanis, Alexander Lloyd Gaunt, Pengcheng Yin
  • Publication number: 20210224355
    Abstract: Examples are disclosed that relate to encoding data on a data-storage medium. The method comprises obtaining a representation of a measurement performed on the data-storage medium, the representation being based on a previously recorded pattern of data encoded in the data-storage medium in a layout that defines a plurality of data locations. The method further comprises inputting the representation into a data decoder comprising a trained machine-learning function, and obtaining from the data decoder, for each data location of the layout, a plurality of probability values, wherein each probability value is associated with a corresponding data value and represents the probability that the corresponding data value matches the actual data value in the previously recorded pattern of data at a same location in the layout.
    Type: Application
    Filed: April 5, 2021
    Publication date: July 22, 2021
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Ioan Alexandru STEFANOVICI, Benn Charles Thomsen, Alexander Lloyd Gaunt, Antony Ian Taylor Rowstron, Reinhard Sebastian Bernhard Nowozin
  • Patent number: 10970363
    Abstract: Examples are disclosed that relate to reading stored data. The method comprises obtaining a representation of a measurement performed on a data-storage medium, the representation being based on a previously recorded pattern of data encoded in the data-storage medium in a layout that defines a plurality of data locations. The method further comprises inputting the representation into a data decoder comprising a trained machine-learning function, and obtaining from the data decoder, for each data location of the layout, a plurality of probability values, wherein each probability value is associated with a corresponding data value and represents the probability that the corresponding data value matches the actual data value in the previously recorded pattern of data at a same location in the layout.
    Type: Grant
    Filed: October 17, 2017
    Date of Patent: April 6, 2021
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Ioan Alexandru Stefanovici, Benn Charles Thomsen, Alexander Lloyd Gaunt, Antony Ian Taylor Rowstron, Reinhard Sebastian Bernhard Nowozin
  • Publication number: 20200394024
    Abstract: A computer program predictor is described which has a processor configured to access a program attribute predictor; and a memory storing a search component configured to search a space of possible programs, to find a program which, given an input data instance and an output data instance, will compute the output data instance from the input data instance, the search being guided by attributes predicted by the attribute predictor given the input data instance and the output data instance.
    Type: Application
    Filed: August 28, 2020
    Publication date: December 17, 2020
    Inventors: Alexander Lloyd GAUNT, Sebastian NOWOZIN, Marc Manuel Johannes BROCKSCHMIDT, Daniel Stefan TARLOW, Matej BALOG
  • Patent number: 10782939
    Abstract: A computer program predictor is described which has a processor configured to access a program attribute predictor; and a memory storing a search component configured to search a space of possible programs, to find a program which, given an input data instance and an output data instance, will compute the output data instance from the input data instance, the search being guided by attributes predicted by the attribute predictor given the input data instance and the output data instance.
    Type: Grant
    Filed: August 7, 2017
    Date of Patent: September 22, 2020
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Alexander Lloyd Gaunt, Sebastian Nowozin, Marc Manuel Johannes Brockschmidt, Daniel Stefan Tarlow, Matej Balog
  • Patent number: 10768825
    Abstract: A data-storage system comprises a head receiver configured to variably receive up to a number M of write heads. The data-storage system also includes an installed number N of write heads arranged in the head receiver, a substrate receiver configured to receive one or more data-storage substrates, and a positioner machine configured to adjust a relative placement of each of the M write heads with respect to at least one of the one or more data-storage substrates.
    Type: Grant
    Filed: November 12, 2019
    Date of Patent: September 8, 2020
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Antony Ian Taylor Rowstron, Ioan Alexandru Stefanovici, Aaron William Ogus, Douglas Wayne Phillips, Richard John Black, Austin Nicholas Donnelly, Alexander Lloyd Gaunt, Andreas Georgiou, Ariel Gomez Diaz, Serguei Anatolievitch Legtchenko, Reinhard Sebastian Bernhard Nowozin, Benn Charles Thomsen, Hugh David Paul Williams, David Lara Saucedo, Patrick Neil Anderson, Andromachi Chatzieleftheriou, John Christopher Dainty, James Hilton Clegg, Raluca Andreea Diaconu, Rokas Drevinskas, Mengyang Yang
  • Patent number: 10719239
    Abstract: A data-storage system comprises a head receiver configured to variably receive up to a number M of write heads. The data-storage system also includes an installed number N of write heads arranged in the head receiver, a substrate receiver configured to receive one or more data-storage substrates, and a positioner machine configured to adjust a relative placement of each of the M write heads with respect to at least one of the one or more data-storage substrates.
    Type: Grant
    Filed: May 16, 2018
    Date of Patent: July 21, 2020
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Antony Ian Taylor Rowstron, Ioan Alexandru Stefanovici, Aaron William Ogus, Douglas Wayne Phillips, Richard John Black, Austin Nicholas Donnelly, Alexander Lloyd Gaunt, Andreas Georgiou, Ariel Gomez Diaz, Serguei Anatolievitch Legtchenko, Reinhard Sebastian Bernhard Nowozin, Benn Charles Thomsen, Hugh David Paul Williams, David Lara Saucedo, Patrick Neil Anderson, Andromachi Chatzieleftheriou, John Christopher Dainty, James Hilton Clegg, Raluca Andreea Diaconu, Rokas Drevinskas, Mengyang Yang
  • Publication number: 20200104102
    Abstract: An editing tool is described which has a memory storing a neural network having been trained to compute a change representation from pairs, each pair comprising a representation of a first version of a content item and a second version of the content item, and for each of the change representations, predict an updated content item from the change representation and the first version of the content item. The editing tool has a processor configured to receive an input content item and to compute an updated version of the input content item according to a change representation, using the neural network.
    Type: Application
    Filed: September 27, 2018
    Publication date: April 2, 2020
    Inventors: Marc Manuel Johannes BROCKSCHMIDT, Miltiadis ALLAMANIS, Alexander Lloyd GAUNT, Pengcheng YIN
  • Publication number: 20200081619
    Abstract: A data-storage system comprises a head receiver configured to variably receive up to a number M of write heads. The data-storage system also includes an installed number N of write heads arranged in the head receiver, a substrate receiver configured to receive one or more data-storage substrates, and a positioner machine configured to adjust a relative placement of each of the M write heads with respect to at least one of the one or more data-storage substrates.
    Type: Application
    Filed: November 12, 2019
    Publication date: March 12, 2020
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Antony Ian Taylor ROWSTRON, Ioan Alexandru STEFANOVICI, Aaron William OGUS, Douglas Wayne PHILLIPS, Richard John BLACK, Austin Nicholas DONNELLY, Alexander Lloyd GAUNT, Andreas GEORGIOU, Ariel GOMEZ DIAZ, Serguei Anatolievitch LEGTCHENKO, Reinhard Sebastian Bernhard NOWOZIN, Benn Charles THOMSEN, Hugh David Paul WILLIAMS, David LARA SAUCEDO, Patrick Neil ANDERSON, Andromachi CHATZIELEFTHERIOU, John Christopher DAINTY, James Hilton CLEGG, Raluca Andreea DIACONU, Rokas DREVINSKAS, Mengyang YANG
  • Publication number: 20190354283
    Abstract: A data-storage system comprises a head receiver configured to variably receive up to a number M of write heads. The data-storage system also includes an installed number N of write heads arranged in the head receiver, a substrate receiver configured to receive one or more data-storage substrates, and a positioner machine configured to adjust a relative placement of each of the M write heads with respect to at least one of the one or more data-storage substrates.
    Type: Application
    Filed: May 16, 2018
    Publication date: November 21, 2019
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Antony Ian Taylor ROWSTRON, Ioan Alexandru STEFANOVICI, Aaron William OGUS, Douglas Wayne PHILLIPS, Richard John BLACK, Austin Nicholas DONNELLY, Alexander Lloyd GAUNT, Andreas GEORGIOU, Ariel GOMEZ DIAZ, Serguei Anatolievitch LEGTCHENKO, Reinhard Sebastian Bernhard NOWOZIN, Benn Charles THOMSEN, Hugh David Paul WILLIAMS, David LARA SAUCEDO, Patrick Neil ANDERSON, Andromachi CHATZIELEFTHERIOU, John Christopher DAINTY, James Hilton CLEGG, Raluca Andreea DIACONU, Rokas DREVINSKAS, Mengyang YANG
  • Publication number: 20190114307
    Abstract: Examples are disclosed that relate to reading stored data. The method comprises obtaining a representation of a measurement performed on a data-storage medium, the representation being based on a previously recorded pattern of data encoded in the data-storage medium in a layout that defines a plurality of data locations. The method further comprises inputting the representation into a data decoder comprising a trained machine-learning function, and obtaining from the data decoder, for each data location of the layout, a plurality of probability values, wherein each probability value is associated with a corresponding data value and represents the probability that the corresponding data value matches the actual data value in the previously recorded pattern of data at a same location in the layout.
    Type: Application
    Filed: October 17, 2017
    Publication date: April 18, 2019
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Ioan Alexandru STEFANOVICI, Benn Charles THOMSEN, Alexander Lloyd GAUNT, Antony Ian Taylor ROWSTRON, Reinhard Sebastian Bernhard NOWOZIN
  • Publication number: 20190042210
    Abstract: A computer program predictor is described which has a processor configured to access a program attribute predictor; and a memory storing a search component configured to search a space of possible programs, to find a program which, given an input data instance and an output data instance, will compute the output data instance from the input data instance, the search being guided by attributes predicted by the attribute predictor given the input data instance and the output data instance.
    Type: Application
    Filed: August 7, 2017
    Publication date: February 7, 2019
    Inventors: Alexander Lloyd GAUNT, Sebastian NOWOZIN, Marc Manuel Johannes BROCKSCHMIDT, Daniel Stefan TARLOW, Matej BALOG
  • Publication number: 20180336458
    Abstract: A neural network training apparatus is described which has a network of worker nodes each having a memory storing a subgraph of a neural network to be trained. The apparatus has a control node connected to the network of worker nodes. The control node is configured to send training data instances into the network to trigger parallelized message passing operations which implement a training algorithm which trains the neural network. At least some of the message passing operations asynchronously update parameters of individual subgraphs of the neural network at the individual worker nodes.
    Type: Application
    Filed: May 18, 2017
    Publication date: November 22, 2018
    Inventors: Ryota TOMIOKA, Matthew Alastair JOHNSON, Daniel Stefan TARLOW, Samuel Alexander WEBSTER, Dimitrios VYTINIOTIS, Alexander Lloyd GAUNT, Maik RIECHERT