Systems and methods for compression and distribution of machine learning models

A computing device comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media that store instructions that, when executed by the one or more processors, cause the computing device to:
• obtain a machine-learned model;
• select a weight to be quantized;
• quantize the weight;
• propagate at least a part of a quantization error to one or more non-quantized weights;
• quantize one or more of the non-quantized weights;
• and
• provide a quantized machine-learned model.

One or more non-transitory computer-readable media that store instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising:

• obtaining a machine-learned model;
• for one or more weights of the machine-learned model:
• selecting a weight to be quantized;
• quantizing the weight;
• propagating at least a part of a quantization error to one or more non-quantized weights;
• and
• providing a quantized machine-learned model.