Patent ID: 11972347
Assignee: TECHNION RESEARCH & DEVELOPMENT FOUNDATION LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 14:
15. A system for detection and classification of findings in digital data, comprising at least one hardware processor configured to:
accessing a non-uniformly quantized neural network data; and
classifying at least one finding detected in the digital data according to the non-uniformly quantized neural network data set in response to receiving the digital data;
wherein the non-uniformly quantized neural network data set is generated by:
receiving digital input data comprising a plurality of training input value sets and a plurality of target value sets;
in each training iteration of a plurality of training iterations:
for each layer, comprising a plurality of weight values, of one or more layers of a plurality of layers of a neural network:
computing a set of transformed values by applying to a plurality of layer values, comprising a plurality of previous output values of a previous layer and the layer's plurality of weight values, one or more emulated non-uniformly quantized transformations by adding to each value of the plurality of layer values one or more uniformly distributed random noise values; and
computing a plurality of output values by applying to the set of transformed values one or more arithmetic operations;
computing a plurality of training output values from a combination of the plurality of output values of a last layer of the plurality of layers; and
updating one or more of the plurality of weight values of the one or more layers to decrease a value of a loss function computed using the plurality of target value sets and plurality of training output values; and
outputting the updated plurality of weight values of the plurality of layers;
applying the one or more emulated non-uniformly quantized transformations to the plurality of layer values to compute a set of transformed values by:
applying to each previous layer output value of the plurality of previous layer output values a first emulated non-uniformly quantized transformation by adding a first uniformly distributed random noise value, having a first distribution having a first variance, to produce a set of transformed output values;
applying to each weight value of the layer's plurality of weight values a second emulated non-uniformly quantized transformation by adding a second uniformly distributed random noise value, having a second distribution having a second variance, to produce a set of transformed weight values; and
combining the set of transformed output values with the set of transformed weight values to produce the set of transformed values.