公开(公告)号：US20200151398A1

公开(公告)日：2020-05-14

申请号：US16746012

申请日：2020-01-17

Applicant: DeepMind Technologies Limited

Inventor： Lei Yu ,  Christopher James Dyer ,  Tomas Kocisky ,  Philip Blunsom

IPC: G06F40/44 ,  G06F40/16 ,  G06N3/04 ,  G06F17/18

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a target sequence from an input sequence. In one aspect, a method comprises maintaining a set of current hypotheses, wherein each current hypothesis comprises an input prefix and an output prefix. For each possible combination of input and output prefix length, the method extends any current hypothesis that could reach the possible combination to generate respective extended hypotheses for each such current hypothesis; determines a respective direct score for each extended hypothesis using a direct model; determines a first number of highest-scoring hypotheses according to the direct scores; rescores the first number of highest-scoring hypotheses using a noisy channel model to generate a reduced number of hypotheses; and adds the reduced number of hypotheses to the set of current hypotheses.

公开(公告)号：US10572603B2

公开(公告)日：2020-02-25

申请号：US16403281

申请日：2019-05-03

Applicant: DeepMind Technologies Limited

Inventor： Lei Yu ,  Christopher James Dyer ,  Tomas Kocisky ,  Philip Blunsom

IPC: G06F17/28 ,  G06F17/18 ,  G06N3/04 ,  G06F17/22

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a target sequence from an input sequence. In one aspect, a method comprises maintaining a set of current hypotheses, wherein each current hypothesis comprises an input prefix and an output prefix. For each possible combination of input and output prefix length, the method extends any current hypothesis that could reach the possible combination to generate respective extended hypotheses for each such current hypothesis; determines a respective direct score for each extended hypothesis using a direct model; determines a first number of highest-scoring hypotheses according to the direct scores; rescores the first number of highest-scoring hypotheses using a noisy channel model to generate a reduced number of hypotheses; and adds the reduced number of hypotheses to the set of current hypotheses.

公开(公告)号：US20240169211A1

公开(公告)日：2024-05-23

申请号：US18388180

申请日：2023-11-08

Applicant: DeepMind Technologies Limited

Inventor： Domenic Joseph Donato ,  Christopher James Dyer ,  Lei Yu ,  Wang Ling

IPC: G06N3/092 ,  G06N3/0985

CPC classification number: G06N3/092 ,  G06N3/0985

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network to perform a machine learning task through reinforcement learning. In one aspect, the training uses importance weights generated using standardized absolute deviations of quality scores generated by the neural network for candidate network outputs.

公开(公告)号：US11423237B2

公开(公告)日：2022-08-23

申请号：US16746012

申请日：2020-01-17

Applicant: DeepMind Technologies Limited

Inventor： Lei Yu ,  Christopher James Dyer ,  Tomas Kocisky ,  Philip Blunsom

IPC: G06F40/30 ,  G06F40/16 ,  G06F40/44 ,  G06F17/18 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a target sequence from an input sequence. In one aspect, a method comprises maintaining a set of current hypotheses, wherein each current hypothesis comprises an input prefix and an output prefix. For each possible combination of input and output prefix length, the method extends any current hypothesis that could reach the possible combination to generate respective extended hypotheses for each such current hypothesis; determines a respective direct score for each extended hypothesis using a direct model; determines a first number of highest-scoring hypotheses according to the direct scores; rescores the first number of highest-scoring hypotheses using a noisy channel model to generate a reduced number of hypotheses; and adds the reduced number of hypotheses to the set of current hypotheses.

公开(公告)号：US20190258718A1

公开(公告)日：2019-08-22

申请号：US16403281

申请日：2019-05-03

Applicant: DeepMind Technologies Limited

Inventor： Lei Yu ,  Christopher James Dyer ,  Tomas Kocisky ,  Philip Blunsom

IPC: G06F17/28 ,  G06N3/04 ,  G06F17/18

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a target sequence from an input sequence. In one aspect, a method comprises maintaining a set of current hypotheses, wherein each current hypothesis comprises an input prefix and an output prefix. For each possible combination of input and output prefix length, the method extends any current hypothesis that could reach the possible combination to generate respective extended hypotheses for each such current hypothesis; determines a respective direct score for each extended hypothesis using a direct model; determines a first number of highest-scoring hypotheses according to the direct scores; rescores the first number of highest-scoring hypotheses using a noisy channel model to generate a reduced number of hypotheses; and adds the reduced number of hypotheses to the set of current hypotheses.