4 citations · 8 across the 9 of their papers we have counts for
5 papers · 1 filter
From Seeds to Semantics: Measuring Semantic Accessibility in Deterministic Diffusion Models
Wei Wei, Yizhou Zeng, Kuntian Chen +3
Diffusion models generate samples through a sequence of learned denoising steps, and recent work has studied how semantic structure appears along this sampling process. We study th…
Neural (Tangent Kernel) Collapse
Mariia Seleznova, Dana Weitzner, Raja Giryes +2
This work bridges two important concepts: the Neural Tangent Kernel (NTK), which captures the evolution of deep neural networks (DNNs) during training, and the Neural Collapse (NC)…
Robust Implicit Regularization via Weight Normalization
Hung-Hsu Chou, Holger Rauhut, Rachel Ward
Overparameterized models may have many interpolating solutions; implicit regularization refers to the hidden preference of a particular optimization method towards a certain interp…
Gradient Descent for Deep Matrix Factorization: Dynamics and Implicit Bias towards Low Rank
Hung-Hsu Chou, Carsten Gieshoff, Johannes Maly +1
In deep learning, it is common to use more network parameters than training points. In such scenarioof over-parameterization, there are usually multiple networks that achieve zero…
Overparameterization and generalization error: weighted trigonometric interpolation
Yuege Xie, Hung-Hsu Chou, Holger Rauhut +1
Motivated by surprisingly good generalization properties of learned deep neural networks in overparameterized scenarios and by the related double descent phenomenon, this paper ana…