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H maps the model’s state into the representation z that will be measured. Its output could be a parameter-derived coordinate, activation summary, or another declared measurement. The chosen H, its units, and its invariances must be fixed before comparing z with a reference.
Terms & definitions
| Term | Meaning in this source |
|---|---|
| z_t | Measured representation |
| H | Specified extraction map |
| S_t | Bookkeeping state |
How it works
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Original context
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(2.1) Here θt denotes parameters, mt persistent memory, ct runtime state and context, and ot observations. The tuple specifies what a prospective study must distinguish; it does not claim that the present dataset contains every component. During inference with frozen parameters, θt remains fixed while context and activity can change. During training, an optimizer can change trainable parameters. Resetting context, deleting external memory, and restoring a checkpoint therefore intervene on different objects. Let zt=H(St) denote an internally maintained estimate or report. Let rt=ρ(St) denote a reference for the same target, obtained by a prespecified measurement procedure that does not infer correctness from zt. In this notation, independently collected reference measurements are part of the observation record. Both maps must return objects in a common comparison space. An estimated hand position and a measured hand position can be compared in shared coordinates; a physical robot and a sentence about it cannot be compared merely because both are called the self. For a specified nonnegative discrepancy function d, I distinguish discrepancy from the current reference and displacement from the first declared representation: δt=d(zt,rt), δt0=d(zt,ztD). (2.2)
Exact original notation
{z}_{t}=H({S}_{t})