Read the equation
Adds the layer’s per-aspect losses with λ weights. Every aspect can contribute to the same layer objective, avoiding an implicit assumption that each layer observes only one isolated part of the environment.
Terms & definitions
The recovered passage does not supply a separate symbol table. Read the source context for the notation used in this version; familiar letters may carry different meanings in other builds.
How it works
Read L_\ell^\*(S_\ell', D_\ell'; U, E) as the quantity being defined or updated by this relation.
Adds the indexed contributions. The summation limits determine which components participate.
Use this version’s variable definitions and update order. The same symbol may be assigned a different meaning in another build.
Assumptions & limits
Source recovery establishes that the expression appears in this record. Its mathematical validity, implementation behavior, and interpretation must be assessed separately. See the linked source and research notes for the checks available for this version.
Original context
Read the surrounding source passage
• only that aspect’s slice E^{(a)}.
Now combine all aspects for one layer:
L_\ell^\*(S_\ell', D_\ell'; U, E) =
\sum_{a=1}^K \lambda_{\ell a} \, L_{\ell,a}^\*(S_\ell', D_\ell'; U, E^{(a)})
Here:
• \lambda_{\ell a} \ge 0 are weights: how important aspect a is to layer \ell.
Layer-neuron N_\ell solves:
(S_\ell^\*, D_\ell^\*) = \arg\min_{S_\ell', D_\ell'} L_\ell^\*(S_\ell', D_\ell'; U, E)Exact original notation
L_\ell^\*(S_\ell', D_\ell'; U, E) =
\sum_{a=1}^K \lambda_{\ell a} \, L_{\ell,a}^\*(S_\ell', D_\ell'; U, E^{(a)})Related builds
Sources & provenance
L_\ell^\*(S_\ell', D_\ell'; U, E) = \sum_{a=1}^K \lambda_{\ell a} \, L_{\ell,a}^\*(S_\ell', D_\ell'; U, E^{(a)})
L_\ell^\*(S_\ell', D_\ell'; U, E) = \sum_{a=1}^K \lambda_{\ell a} \, L_{\ell,a}^\*(S_\ell', D_\ell'; U, E^{(a)})