Search Results for author: Ningyuan Huang

Found 10 papers, 6 papers with code

Approximately Equivariant Graph Networks

1 code implementation NeurIPS 2023 Ningyuan Huang, Ron Levie, Soledad Villar

However, these two symmetries are fundamentally different: The translation equivariance of CNNs corresponds to symmetries of the fixed domain acting on the image signals (sometimes known as active symmetries), whereas in GNNs any permutation acts on both the graph signals and the graph domain (sometimes described as passive symmetries).

Image Inpainting Pose Estimation +1

Fine-grained Expressivity of Graph Neural Networks

1 code implementation NeurIPS 2023 Jan Böker, Ron Levie, Ningyuan Huang, Soledad Villar, Christopher Morris

In particular, we characterize the expressive power of MPNNs in terms of the tree distance, which is a graph distance based on the concept of fractional isomorphisms, and substructure counts via tree homomorphisms, showing that these concepts have the same expressive power as the $1$-WL and MPNNs on graphons.

A Spectral Analysis of Graph Neural Networks on Dense and Sparse Graphs

1 code implementation6 Nov 2022 Luana Ruiz, Ningyuan Huang, Soledad Villar

In this work we propose a random graph model that can produce graphs at different levels of sparsity.

Community Detection Node Classification

Deep Learning is Provably Robust to Symmetric Label Noise

no code implementations26 Oct 2022 Carey E. Priebe, Ningyuan Huang, Soledad Villar, Cong Mu, Li Chen

We conjecture that for general label noise, mitigation strategies that make use of the noisy data will outperform those that ignore the noisy data.

Memorization

Endowing Language Models with Multimodal Knowledge Graph Representations

1 code implementation27 Jun 2022 Ningyuan Huang, Yash R. Deshpande, Yibo Liu, Houda Alberts, Kyunghyun Cho, Clara Vania, Iacer Calixto

We use the recently released VisualSem KG as our external knowledge repository, which covers a subset of Wikipedia and WordNet entities, and compare a mix of tuple-based and graph-based algorithms to learn entity and relation representations that are grounded on the KG multimodal information.

Multilingual Named Entity Recognition named-entity-recognition +2

Deep Learning with Label Noise: A Hierarchical Approach

no code implementations28 May 2022 Li Chen, Ningyuan Huang, Cong Mu, Hayden S. Helm, Kate Lytvynets, Weiwei Yang, Carey E. Priebe

Our hierarchical approach improves upon regular deep neural networks in learning with label noise.

Meta-Learning

A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants

no code implementations18 Jan 2022 Ningyuan Huang, Soledad Villar

Graph neural networks are designed to learn functions on graphs.

Dimensionality reduction, regularization, and generalization in overparameterized regressions

1 code implementation23 Nov 2020 Ningyuan Huang, David W. Hogg, Soledad Villar

This realization brought back the study of linear models for regression, including ordinary least squares (OLS), which, like deep learning, shows a "double-descent" behavior: (1) The risk (expected out-of-sample prediction error) can grow arbitrarily when the number of parameters $p$ approaches the number of samples $n$, and (2) the risk decreases with $p$ for $p>n$, sometimes achieving a lower value than the lowest risk for $p<n$.

Data Poisoning Dimensionality Reduction +1

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