framework
active
framework:equal-weightingEqual Weighting
Baseline MTL approach minimizing sum of task losses with equal weights; suffers from task balancing
Neighborhood — ranked by edge-count
Questions (1)
question
- task balancing problemcontradictsCore challenge where disparity in loss and gradient scales among tasks leads to performance compromises
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Baseline that minimizes sum of task losses with equal weights.
- Editing network weights to test predictions about circuit function; proposed as falsifiability test for circuit claims
- The space of the model's parameter matrices, where VPD operations take place.
- Logit weight contributions from a feature that arise due to superposition with other features, not from the feature's own causal role
- Coefficient weighting each task loss in the MTL objective.
- Loss balancing using homoscedastic uncertainty.
- Load-bearing claim about the tractability of circuit analysis; central thesis of Claim 2