Temporal difference estimation in an artificial neural network
AssigneeQUALCOMM Incorporated
InventorsPeter O'CONNOR, Max WELLING
Life of this patent
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Abstract
A method of computation in a deep neural network includes discretizing input signals and computing a temporal difference of the discrete input signals to produce a discretized temporal difference. The method also includes applying weights of a first layer of the deep neural network to the discretized temporal difference to create an output of a weight matrix. The output of the weight matrix is temporally summed with a previous output of the weight matrix. An activation function is applied to the temporally summed output to create a next input signal to a next layer of the deep neural network.
Claims24
A method of computation in a deep neural network, comprising:
- discretizing a first input signal and a second input signal;
- computing a temporal difference of the discrete first input signal and the discrete second input signal to produce a discretized temporal difference;
- applying weights of a first layer of the deep neural network to the discretized temporal difference to create an output of a weight matrix;
- temporally summing the output of the weight matrix with a previous output of the weight matrix;
- and
- applying an activation function to the temporally summed output to create a next input signal to a next layer of the deep neural network.
An apparatus for computation in a deep neural network, comprising:
- a memory;
- and
- at least one processor coupled to the memory, the at least one processor configured:
- to discretize a first input signal and a second input signal;
- to compute a temporal difference of the discrete first input signal and the discrete second input signal to produce a discretized temporal difference;
- to apply weights of a first layer of the deep neural network to the discretized temporal difference to create an output of a weight matrix;
- to temporally sum the output of the weight matrix with a previous output of the weight matrix;
- and
- to apply an activation function to the temporally summed output to create a next input signal to a next layer of the deep neural network.
An apparatus for computation in a deep neural network, comprising:
- means for discretizing a first input signal and a second input signal;
- means for computing a temporal difference of the discrete first input signal and the discrete second input signal to produce a discretized temporal difference;
- means for applying weights of a first layer of the deep neural network to the discretized temporal difference to create an output of a weight matrix;
- means for temporally summing the output of the weight matrix with a previous output of the weight matrix;
- and
- means for applying an activation function to the temporally summed output to create a next input signal to a next layer of the deep neural network.
A non-transitory computer readable medium having encoded thereon program code for computation in a deep neural network, the program code being executed by a processor and comprising:
- program code to discretize a first input signal and a second input signal;
- program code to compute a temporal difference of the discrete first input signal and the discrete second input signal to produce a discretized temporal difference;
- program code to apply weights of a first layer of the deep neural network to the discretized temporal difference to create an output of a weight matrix;
- program code to temporally sum the output of the weight matrix with a previous output of the weight matrix;
- and
- program code to apply an activation function to the temporally summed output to create a next input signal to a next layer of the deep neural network.
Dependencies are inferred from the claim text.