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US 2026/0227957 A1Published application
Flexmax for flexible softmax functions
AssigneeQUALCOMM Incorporated
InventorsJian SHEN, Jamie Menjay LIN
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Abstract
A processor-implemented method for applying a flexible softmax function in machine learning models includes receiving a machine learning model including one or more configurable softmax functions. A base parameter is selected for the one or more configurable softmax functions. During training of the machine learning model, an exponential function of the one or more configurable softmax functions is configured based on the base parameter.
Claims20
An apparatus, comprising:
- at least one memory;
- and
- at least one processor coupled to the at least one memory, the at least one processor configured to:
- receive a machine learning model including one or more configurable softmax functions;
- select a base parameter for the one or more configurable softmax functions;
- and
- configure, during training of the machine learning model, an exponential function of the one or more configurable softmax functions based on the base parameter.
4 more elements
A processor-implemented method performed by at least one processor, the processor-implemented method comprising:
- receiving a machine learning model including one or more configurable softmax functions;
- selecting a base parameter for the one or more configurable softmax functions;
- and
- configuring, during training of the machine learning model, an exponential function of the one or more configurable softmax functions based on the base parameter.
1 more element
An apparatus comprising:
- means for receiving a machine learning model including one or more configurable softmax functions;
- means for selecting a base parameter for the one or more configurable softmax functions;
- and
- means for configuring, during training of the machine learning model, an exponential function of the one or more configurable softmax functions based on the base parameter.
1 more element
Dependencies are inferred from the claim text.