5 papers
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…
Cross-lingual brain-language model alignment is robust but challenges hierarchical and computational accounts
Ni Yang, Rui He, Philipp Homan +3
Brain-language model alignment is often interpreted as evidence that transformer models implement computations similar to those of the human brain. This assumes that neural predict…
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…
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…
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…