How neural networks find generalizable solutions: Self-tuned annealing in deep learning. - Yu Feng 004; Yuhai Tu (CoRR, 2020)

Phases of learning dynamics in artificial neural networks: with or without mislabeled data. - Yu Feng 004; Yuhai Tu (CoRR, 2021)

Loss Landscape Dependent Self-Adjusting Learning Rates in Decentralized Stochastic Gradient Descent. - Wei Zhang 022; Mingrui Liu; Yu Feng 004; Xiaodong Cui; Brian Kingsbury; Yuhai Tu (CoRR, 2021)

Phases of learning dynamics in artificial neural networks in the absence or presence of mislabeled data. - Yu Feng 004; Yuhai Tu (Mach. Learn. Sci. Technol., 2021)

The inverse variance-flatness relation in stochastic gradient descent is critical for finding flat minima. - Yu Feng 004; Yuhai Tu (Proc. Natl. Acad. Sci. USA, 2021)

Activity-weight duality in feed-forward neural networks reveals two co-determinants for generalization. - Yu Feng 004; Wei Zhang 022; Yuhai Tu (Nat. Mac. Intell., 2023)

Attractor neural networks with double well synapses. - Yu Feng 004; Nicolas Brunel (PLoS Comput. Biol., 2024)