PROCESSING AND GENERATING SETS USING RECURRENT NEURAL NETWORKS
First Claim
1. A neural network system implemented by one or more computers, the neural network system comprising:
- a read neural network configured to;
receive an input set comprising a plurality of inputs, andprocess each input in the input set to generate a respective memory vector for each input;
a process neural network configured to;
process the respective memory vector for each of the inputs to generate an order-invariant numeric embedding for the input set, wherein the order-invariant numeric embedding is permutation invariant to the inputs in the input set; and
a write neural network configured to;
process the order-invariant numeric embedding to generate a neural network output for the input set.
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Abstract
In one aspect, this specification describes a recurrent neural network system implemented by one or more computers that is configured to process input sets to generate neural network outputs for each input set. The input set can be a collection of multiple inputs for which the recurrent neural network should generate the same neural network output regardless of the order in which the inputs are arranged in the collection. The recurrent neural network system can include a read neural network, a process neural network, and a write neural network. In another aspect, this specification describes a system implemented as computer programs on one or more computers in one or more locations that is configured to train a recurrent neural network that receives a neural network input and sequentially emits outputs to generate an output sequence for the neural network input.
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Citations
20 Claims
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1. A neural network system implemented by one or more computers, the neural network system comprising:
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a read neural network configured to; receive an input set comprising a plurality of inputs, and process each input in the input set to generate a respective memory vector for each input; a process neural network configured to; process the respective memory vector for each of the inputs to generate an order-invariant numeric embedding for the input set, wherein the order-invariant numeric embedding is permutation invariant to the inputs in the input set; and a write neural network configured to; process the order-invariant numeric embedding to generate a neural network output for the input set. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method of training a recurrent neural network having a plurality of parameters that receives a neural network input and sequentially emits outputs to generate an output sequence for the neural network input, the method comprising:
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receiving first training data for training the recurrent neural network, the first training data comprising a plurality of training example pairs, each training example pair comprising a training input and a target output set for the training input, the training output set having a plurality of target outputs; and training the recurrent neural network on each of the training example pairs in the first training data, wherein training the recurrent neural network comprises, for each training example pair; selecting a particular order for the target outputs from the target output set in the training example pair; and training the recurrent neural network to generate an output sequence for the training input in the training example pair that matches a sequence having the target outputs from the target output set arranged according to the particular order. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15, 16)
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17. A system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
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receiving first training data for training the recurrent neural network, the first training data comprising a plurality of training example pairs, each training example pair comprising a training input and a target output set for the training input, the training output set having a plurality of target outputs; and training the recurrent neural network on each of the training example pairs in the first training data, wherein training the recurrent neural network comprises, for each training example pair; selecting a particular order for the target outputs from the target output set in the training example pair; and training the recurrent neural network to generate an output sequence for the training input in the training example pair that matches a sequence having the target outputs from the target output set arranged according to the particular order. - View Dependent Claims (18, 19, 20)
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Specification