197 citations · 367 across the 23 of their papers we have counts for
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On the Generalization Power of the Overfitted Three-Layer Neural Tangent Kernel Model
Peizhong Ju, Xiaojun Lin, Ness B. Shroff
In this paper, we study the generalization performance of overparameterized 3-layer NTK models. We show that, for a specific set of ground-truth functions (which we refer to as the…
SeedGNN: Graph Neural Networks for Supervised Seeded Graph Matching
Liren Yu, Jiaming Xu, Xiaojun Lin
There is a growing interest in designing Graph Neural Networks (GNNs) for seeded graph matching, which aims to match two unlabeled graphs using only topological information and a s…
Student-Teacher Learning from Clean Inputs to Noisy Inputs
Guanzhe Hong, Zhiyuan Mao, Xiaojun Lin +1
Feature-based student-teacher learning, a training method that encourages the student's hidden features to mimic those of the teacher network, is empirically successful in transfer…
On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models
Peizhong Ju, Xiaojun Lin, Ness B. Shroff
In this paper, we study the generalization performance of min -norm overfitting solutions for the neural tangent kernel (NTK) model of a two-layer neural network with ReLU…
Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree
Peizhong Ju, Xiaojun Lin, Jia Liu
Recently, there have been significant interests in studying the so-called "double-descent" of the generalization error of linear regression models under the overparameterized and o…