5 papers
Noise-Driven Exploration and Transient Freezing Select Flat Minima in Stochastic Gradient Descent
Ning Yang, Yikuan Zhang, Qi Ouyang +2
Stochastic gradient descent (SGD) is central to deep learning, yet the dynamical origin of its preference for flatter, more generalizable solutions remains unclear. Here, by analyz…
On the Superlinear Relationship between SGD Noise Covariance and Loss Landscape Curvature
Yikuan Zhang, Ning Yang, Yuhai Tu
Stochastic Gradient Descent (SGD) introduces anisotropic noise that is correlated with the local curvature of the loss landscape, thereby biasing optimization toward flat minima. P…
Cross-lingual robustness of LLM-brain alignment and its computational roots
Ni Yang, Rui He, Philipp Homan +3
Large language models (LLMs) reliably predict neural activity during language comprehension and transformer depth has been interpreted as mirroring hierarchical cortical organizati…
A Survey of Advancing Audio Super-Resolution and Bandwidth Extension from Discriminative to Generative Models
Ningyuan Yang, Yize Li, Diego A. Cuji +4
Audio super-resolution (SR), also referred to as bandwidth extension (BWE), aims to reconstruct high-fidelity signals from low-resolution (LR) or band-limited (BL) observations, an…
Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment
Guanmeng Xian, Ning Yang, Philip S. Yu
Adversarial training is effective on balanced datasets, but its robustness degrades under longtailed class distributions, where tail classes suffer high robust error and unstable d…