Thanos studies how multiple weights can be removed while compensating for their joint effect on a layer’s output. Instead of treating each removed weight in isolation, the method uses Hessian-derived second-order information to coordinate weight updates.
The project explores efficient, block-wise layer optimization for both unstructured and structured sparsity. It is presented here as an active research project; no publication status or benchmark claims are implied.