Designing architectures and local losses for truncated credit assignment

Determine architectures and per-layer local loss functions that effectively guide truncated credit assignment in deep neural networks, so that global backpropagation can be replaced by layer-wise or truncated variants without degrading performance.

Background

The thesis discusses alternatives to global backpropagation for credit assignment, noting recent success with truncated layer-wise backpropagation approaches. However, achieving comparable performance with truncated methods requires appropriate architectural choices and local objectives that steer learning effectively.

This open question targets the co-design of network topology and local losses to enable scalable training without full global error propagation, aligning with the thesis’ broader theme of locality and hardware-awareness.