Shunbao Zhang
Lanzhou University · School of Information Science and Engineering
I work on why deep networks generalise and stay robust. My current focus is neurodynamics-inspired optimisers that reshape the loss landscape toward flat minima.
News
- 2026.02 — Paper accepted at CAAI Transactions on Intelligence Technology: cascaded inertia SGD with learned hyperparameters.
- 2025.09 — Started my MSc in Software Engineering at Lanzhou University.
- 2024.10 — Paper published in Computer Networks, on access strategies for mmWave cell-free networks.
Selected publications
-
Enhancing Generalisation via Cascaded Inertia SGD With Learnt HyperparametersVol. 11, No. 4, pp. 1019-1027A second-order inertia term built from neurodynamics drives SGD toward flat minima, with its coefficients learned offline by a lightweight network.
-
Access strategies in mmWave cell-free network: A matching and auction theory based approachVol. 252, Article 110659Joint user association and resource allocation in mmWave cell-free networks, solved with matching theory and auction mechanisms.
-
Meta Integration-Enhanced OptimizerMulti-order integral shaping of the loss with hypergradient-driven coefficient updates. Manuscript in preparation.
Research
-
DIESGD - cascaded inertia optimiserNeurodynamics-derived second-order inertia that steers training into flat minima.
-
MIEO - meta integration-enhanced optimiserStacking several integral orders, with coefficients updated online by a hypergradient controller.