GATED LINEAR NETWORKS
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a neural network system comprising one or more gated linear networks. A system includes: one or more gated linear networks, wherein each gated linear network corresponds to a respective data value in an output data sample and is configured to generate a network probability output that defines a probability distribution over possible values for the corresponding data value, wherein each gated linear network comprises a plurality of layers, wherein the plurality of layers comprises a plurality of gated linear layers, wherein each gated linear layer has one or more nodes, and wherein each node is configured to: receive a plurality of inputs, receive side information for the node; combine the plurality of inputs according to a set of weights defined by the side information, and generate and output a node probability output for the corresponding data value.
1 - 15 . (canceled)
16 . A system comprising:
one or more computers, and
one or more storage devices on which are stored instructions, that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations for implementing a neural network system configured to receive a system input and process the system input to generate a system output,
wherein the neural network system comprises one or more neural networks,
wherein the one or more neural networks comprise one or more gated linear networks,
wherein each gated linear network is used for generating a corresponding data value based on which the system output is generated,
wherein each gated linear network comprises a plurality of layers arranged in a hierarchy of layers,
wherein the plurality of layers comprises a plurality of gated linear layers,
wherein each gated linear layer has one or more nodes, and
wherein each node in each gated linear layer is configured to perform operations comprising:
receiving a plurality of inputs from nodes in a layer below the gated linear layer in the hierarchy of layers;
receiving side information for the node;
combining the plurality of inputs according to a set of weights defined by the side information to generate an initial output;
generating, from the initial output, a node probability output that defines a probability distribution over possible values for the corresponding data value; and
providing as output the node probability output for the corresponding data value.
17 . The system of claim 16 , wherein the system output comprises an image comprising a plurality of pixels.
18 . The system of claim 16 , wherein the system input comprises an input image, and wherein the system output comprises a classification output that classifies the input image into one of a pre-determined plurality of classes.
19 . The system of claim 16 , wherein the system input comprises a sequence of data items, and wherein the system output specifies a probability density function for the sequence of data items.
20 . The system of claim 19 , wherein the sequence of data items represents one of:
a still or moving image;
sound data;
text data;
object position data, environment state data, action data, or a combination thereof; or
atomic position data.
21 . The system of claim 16 , wherein the system output comprises:
control data for controlling an agent moving in a simulated or real-world environment; or
data predicting a future image or video sequence seen by a real or virtual camera associated with a physical object or the agent in the simulated or real-world environment.
22 . The system of claim 16 , wherein the one or more gated linear networks are implemented in parallel across different special-purpose hardware.
23 . A method performed by a neural network system configured to receive a system input and process the system input to generate a system output,
wherein the neural network system comprises one or more neural networks,
wherein the one or more neural networks comprise one or more gated linear networks,
wherein each gated linear network is used for generating a corresponding data value based on which the system output is generated,
wherein each gated linear network comprises a plurality of layers arranged in a hierarchy of layers,
wherein the plurality of layers comprises a plurality of gated linear layers,
wherein each gated linear layer has one or more nodes, and
wherein the method comprises, for each node in each gated linear layer:
receiving a plurality of inputs from nodes in a layer below the gated linear layer in the hierarchy of layers;
receiving side information for the node;
combining the plurality of inputs according to a set of weights defined by the side information to generate an initial output;
generating, from the initial output, a node probability output that defines a probability distribution over possible values for the corresponding data value; and
providing as output the node probability output for the corresponding data value.
24 . The method of claim 23 , wherein the system output comprises an image comprising a plurality of pixels.
25 . The method of claim 23 , wherein the system input comprises an input image, and wherein the system output comprises a classification output that classifies the input image into one of a pre-determined plurality of classes.
26 . The method of claim 23 , wherein the system input comprises a sequence of data items, and wherein the system output specifies a probability density function for the sequence of data items.
27 . The method of claim 26 , wherein the sequence of data items represents one of:
a still or moving image;
sound data;
text data;
object position data, environment state data, action data, or a combination thereof; or
atomic position data.
28 . The method of claim 23 , wherein the system output comprises:
control data for controlling an agent moving in a simulated or real-world environment; or
data predicting a future image or video sequence seen by a real or virtual camera associated with a physical object or the agent in the simulated or real-world environment.
29 . The method of claim 23 , wherein the one or more gated linear networks are implemented in parallel across different special-purpose hardware.
30 . One or more non-transitory computer-readable storage media encoded with instructions that, when executed by one or more computers, causes the one or more computers to perform operations for implementing a neural network system configured to receive a system input and process the system input to generate a system output,
wherein the neural network system comprises one or more neural networks,
wherein the one or more neural networks comprise one or more gated linear networks,
wherein each gated linear network is used for generating a corresponding data value based on which the system output is generated,
wherein each gated linear network comprises a plurality of layers arranged in a hierarchy of layers,
wherein the plurality of layers comprises a plurality of gated linear layers,
wherein each gated linear layer has one or more nodes, and
wherein each node in each gated linear layer is configured to perform operations comprising:
receiving a plurality of inputs from nodes in a layer below the gated linear layer in the hierarchy of layers;
receiving side information for the node;
combining the plurality of inputs according to a set of weights defined by the side information to generate an initial output;
generating, from the initial output, a node probability output that defines a probability distribution over possible values for the corresponding data value; and
providing as output the node probability output for the corresponding data value.
31 . The non-transitory computer-readable storage media of claim 30 , wherein the system output comprises an image comprising a plurality of pixels.
32 . The non-transitory computer-readable storage media of claim 30 , wherein the system input comprises an input image, and wherein the system output comprises a classification output that classifies the input image into one of a pre-determined plurality of classes.
33 . The non-transitory computer-readable storage media of claim 30 , wherein the system input comprises a sequence of data items, and wherein the system output specifies a probability density function for the sequence of data items.
34 . The non-transitory computer-readable storage media of claim 33 , wherein the sequence of data items represents one of:
a still or moving image;
sound data;
text data;
object position data, environment state data, action data, or a combination thereof; or
atomic position data.
35 . The non-transitory computer-readable storage media of claim 30 , wherein the system output comprises:
control data for controlling an agent moving in a simulated or real-world environment; or
data predicting a future image or video sequence seen by a real or virtual camera associated with a physical object or the agent in the simulated or real-world environment.