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公开(公告)号：US11348203B2
公开(公告)日：2022-05-31
申请号：US16927490
申请日：2020-07-13
IPC分类号： G06T3/40
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output images. One of the methods includes obtaining data specifying (i) a partitioning of the H by W pixel grid of the output image into K disjoint, interleaved sub-images and (ii) an ordering of the sub-images; and generating intensity values sub-image by sub-image, comprising: for each particular color channel for each particular pixel in each particular sub-image, generating, using a generative neural network, the intensity value for the particular color channel conditioned on intensity values for (i) any pixels that are in sub-images that are before the particular sub-image in the ordering, (ii) any pixels within the particular sub-image that are before the particular pixel in a raster-scan order over the output image, and (iii) the particular pixel for any color channels that are before the particular color channel in a color channel order.
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公开(公告)号：US10713755B2
公开(公告)日：2020-07-14
申请号：US16586848
申请日：2019-09-27
IPC分类号： G06T3/40
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output images. One of the methods includes obtaining data specifying (i) a partitioning of the H by W pixel grid of the output image into K disjoint, interleaved sub-images and (ii) an ordering of the sub-images; and generating intensity values sub-image by sub-image, comprising: for each particular color channel for each particular pixel in each particular sub-image, generating, using a generative neural network, the intensity value for the particular color channel conditioned on intensity values for (i) any pixels that are in sub-images that are before the particular sub-image in the ordering, (ii) any pixels within the particular sub-image that are before the particular pixel in a raster-scan order over the output image, and (iii) the particular pixel for any color channels that are before the particular color channel in a color channel order.
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公开(公告)号：US20210256375A1
公开(公告)日：2021-08-19
申请号：US17169083
申请日：2021-02-05
摘要： A computer-implemented method for training a recurrent neural network using forward propagation rather than back propagation through time. The method is particularly suited to training sparse recurrent neural networks, and may be implemented on specialized hardware.
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公开(公告)号：US20240282094A1
公开(公告)日：2024-08-22
申请号：US18568561
申请日：2022-06-08
发明人： Maria Rafailia Tsimpoukelli , Jacob Lee Menick , Serkan Cabi , Felix George Hill , Seyed Mohammadali Eslami , Oriol Vinyals
IPC分类号： G06V10/82 , G06F40/284 , G06V20/70
CPC分类号： G06V10/82 , G06F40/284 , G06V20/70
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing multi-modal inputs using language models. In particular, the inputs include an image, and the image is encoded by an image encoder neural network to generate a sequence of image embeddings representing the image. The sequence of image embeddings is provided as at least part of an input sequence to that is processed by a language model neural network.
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5.发明授权公开(公告)号：US10936949B2
公开(公告)日：2021-03-02
申请号：US16508042
申请日：2019-07-10
发明人： Marc Gendron-Bellemare , Jacob Lee Menick , Alexander Benjamin Graves , Koray Kavukcuoglu , Remi Munos
摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model. In one aspect, a method includes receiving training data for training the machine learning model on a plurality of tasks, where each task includes multiple batches of training data. A task is selected in accordance with a current task selection policy. A batch of training data is selected from the selected task. The machine learning model is trained on the selected batch of training data to determine updated values of the model parameters. A learning progress measure that represents a progress of the training of the machine learning model as a result of training the machine learning model on the selected batch of training data is determined. The current task selection policy is updated using the learning progress measure.
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公开(公告)号：US11977983B2
公开(公告)日：2024-05-07
申请号：US17020248
申请日：2020-09-14
发明人： Mohammad Gheshlaghi Azar , Meire Fortunato , Bilal Piot , Olivier Claude Pietquin , Jacob Lee Menick , Volodymyr Mnih , Charles Blundell , Remi Munos
摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting an action to be performed by a reinforcement learning agent. The method includes obtaining an observation characterizing a current state of an environment. For each layer parameter of each noisy layer of a neural network, a respective noise value is determined. For each layer parameter of each noisy layer, a noisy current value for the layer parameter is determined from a current value of the layer parameter, a current value of a corresponding noise parameter, and the noise value. A network input including the observation is processed using the neural network in accordance with the noisy current values to generate a network output for the network input. An action is selected from a set of possible actions to be performed by the agent in response to the observation using the network output.
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7.发明申请公开(公告)号：US20210150355A1
公开(公告)日：2021-05-20
申请号：US17159961
申请日：2021-01-27
发明人： Marc Gendron-Bellemare , Jacob Lee Menick , Alexander Benjamin Graves , Koray Kavukcuoglu , Remi Munos
摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model. In one aspect, a method includes receiving training data for training the machine learning model on a plurality of tasks, where each task includes multiple batches of training data. A task is selected in accordance with a current task selection policy. A batch of training data is selected from the selected task. The machine learning model is trained on the selected batch of training data to determine updated values of the model parameters. A learning progress measure that represents a progress of the training of the machine learning model as a result of training the machine learning model on the selected batch of training data is determined. The current task selection policy is updated using the learning progress measure.
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公开(公告)号：US20210065012A1
公开(公告)日：2021-03-04
申请号：US17020248
申请日：2020-09-14
发明人： Mohammad Gheshlaghi Azar , Meire Fortunato , Bilal Piot , Olivier Claude Pietquin , Jacob Lee Menick , Volodymyr Mnih , Charles Blundell , Remi Munos
摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting an action to be performed by a reinforcement learning agent. The method includes obtaining an observation characterizing a current state of an environment. For each layer parameter of each noisy layer of a neural network, a respective noise value is determined. For each layer parameter of each noisy layer, a noisy current value for the layer parameter is determined from a current value of the layer parameter, a current value of a corresponding noise parameter, and the noise value. A network input including the observation is processed using the neural network in accordance with the noisy current values to generate a network output for the network input. An action is selected from a set of possible actions to be performed by the agent in response to the observation using the network output.
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公开(公告)号：US20210004677A1
公开(公告)日：2021-01-07
申请号：US16767010
申请日：2019-02-11
摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an encoder neural network, a decoder neural network, and a prior neural network, and using the trained networks for generative modeling, data compression, and data decompression. In one aspect, a method comprises: providing a given observation as input to the encoder neural network to generate parameters of an encoding probability distribution; determining an updated code for the given observation; selecting a code that is assigned to an additional observation; providing the code assigned to the additional observation as input to the prior neural network to generate parameters of a prior probability distribution; sampling latent variables from the encoding probability distribution; providing the latent variables as input to the decoder neural network to generate parameters of an observation probability distribution; and determining gradients of a loss function.
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公开(公告)号：US20190332938A1
公开(公告)日：2019-10-31
申请号：US16508042
申请日：2019-07-10
发明人： Marc Gendron-Bellemare , Jacob Lee Menick , Alexander Benjamin Graves , Koray Kavukcuoglu , Remi Munos
摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model. In one aspect, a method includes receiving training data for training the machine learning model on a plurality of tasks, where each task includes multiple batches of training data. A task is selected in accordance with a current task selection policy. A batch of training data is selected from the selected task. The machine learning model is trained on the selected batch of training data to determine updated values of the model parameters. A learning progress measure that represents a progress of the training of the machine learning model as a result of training the machine learning model on the selected batch of training data is determined. The current task selection policy is updated using the learning progress measure.
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