Ya-Wei Eileen Lin

Transformers learn translation, rotation, and scale invariance through distinct, identifiable mechanisms, paving the way for structured equivariant function learning.

Merging transformers sequentially along their execution path, rather than layer-by-layer, dramatically reduces error accumulation and unlocks principled multi-task capacity expansion.