Deterministic Inference for Machine Learning Models with Variable Behavior

A computer system that performs deterministic inference for a machine-learned model with variable behavior, the computer system comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media that collectively store:
• the machine-learned model, wherein the machine-learned model is configured to variably apply one or more of a plurality of different processing operations when processing an input;
• and
• instructions that, when executed by the one or more processors, cause the computer system to perform inference-time operations, the inference-time operations comprising:
• obtaining input data and one or more seed values associated with the input data;
• processing the input data and the one or more seed values with the machine-learned model to generate one or more inferences from the input data, wherein the one or more seed values deterministically control which of the plurality of different processing operations are applied by the machine-learned model when processing the input data;
• and
• providing the one or more inferences as an output.