38 citations · 82 across the 18 of their papers we have counts for
7 papers · 1 filter
A Spectral Analysis of Graph Neural Networks on Dense and Sparse Graphs
Luana Ruiz, Ningyuan Huang, Soledad Villar
In this work we propose a random graph model that can produce graphs at different levels of sparsity. We analyze how sparsity affects the graph spectra, and thus the performance of…
Deep Learning is Provably Robust to Symmetric Label Noise
Carey E. Priebe, Ningyuan Huang, Soledad Villar +2
Deep neural networks (DNNs) are capable of perfectly fitting the training data, including memorizing noisy data. It is commonly believed that memorization hurts generalization. The…
From Local to Global: Spectral-Inspired Graph Neural Networks
Ningyuan Huang, Soledad Villar, Carey E. Priebe +4
Graph Neural Networks (GNNs) are powerful deep learning methods for Non-Euclidean data. Popular GNNs are message-passing algorithms (MPNNs) that aggregate and combine signals in a…
Endowing Language Models with Multimodal Knowledge Graph Representations
Ningyuan Huang, Yash R. Deshpande, Yibo Liu +4
We propose a method to make natural language understanding models more parameter efficient by storing knowledge in an external knowledge graph (KG) and retrieving from this KG usin…
Deep Learning with Label Noise: A Hierarchical Approach
Li Chen, Ningyuan Huang, Cong Mu +4
Deep neural networks are susceptible to label noise. Existing methods to improve robustness, such as meta-learning and regularization, usually require significant change to the net…
Prospective Learning: Principled Extrapolation to the Future
Ashwin De Silva, Rahul Ramesh, Lyle Ungar +40
Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the…
A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants
Ningyuan Huang, Soledad Villar
Graph neural networks are designed to learn functions on graphs. Typically, the relevant target functions are invariant with respect to actions by permutations. Therefore the desig…