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no code implementations • 5 Feb 2022 • Samantha Chen, Sunhyuk Lim, Facundo Mémoli, Zhengchao Wan, Yusu Wang

The WL distance is polynomial time computable and is also compatible with the WL test in the sense that the former is positive if and only if the WL test can distinguish the two involved graphs.

no code implementations • 28 Jan 2022 • Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Yusu Wang, Chao Chen

In the context of graph learning, topological features based on persistent homology have been used to capture potentially high-order structural information so as to augment existing graph neural network methods.

no code implementations • 28 Jan 2022 • Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess Smidt, Yusu Wang, Jian Tang, Rafael Gómez-Bombarelli

Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and therefore drastically accelerates simulation.

no code implementations • 25 Jan 2022 • Chen Cai, Yusu Wang

Building upon this result, we prove the convergence of $k$-IGN under the model of \citet{ruiz2020graphon}, where we access the edge weight but the convergence error is measured for graphon inputs.

no code implementations • NeurIPS 2021 • Evan McCarty, Qi Zhao, Anastasios Sidiropoulos, Yusu Wang

This leads to a mixed algorithmic-ML framework, which we call NN-Baker that has the capacity to approximately solve a family of graph optimization problems (e. g, maximum independent set and minimum vertex cover) in time linear to input graph size, and only polynomial to approximation parameter.

no code implementations • 12 Apr 2021 • Chen Cai, Nikolaos Vlassis, Lucas Magee, Ran Ma, Zeyu Xiong, Bahador Bahmani, Teng-Fong Wong, Yusu Wang, WaiChing Sun

Comparisons among predictions inferred from training the CNN and those from graph convolutional neural networks (GNN) with and without the equivariant constraint indicate that the equivariant graph neural network seems to perform better than the CNN and GNN without enforcing equivariant constraints.

no code implementations • ICLR 2021 • Xiaoling Hu, Yusu Wang, Li Fuxin, Dimitris Samaras, Chao Chen

In the segmentation of fine-scale structures from natural and biomedical images, per-pixel accuracy is not the only metric of concern.

no code implementations • ICLR 2021 • Chen Cai, Dingkang Wang, Yusu Wang

As large-scale graphs become increasingly more prevalent, it poses significant computational challenges to process, extract and analyze large graph data.

1 code implementation • 23 Jun 2020 • Chen Cai, Yusu Wang

In this paper, we build upon previous results \cite{oono2019graph} to further analyze the over-smoothing effect in the general graph neural network architecture.

no code implementations • 20 Mar 2020 • Dingkang Wang, Lucas Magee, Bing-Xing Huo, Samik Banerjee, Xu Li, Jaikishan Jayakumar, Meng Kuan Lin, Keerthi Ram, Suyi Wang, Yusu Wang, Partha P. Mitra

Neuroscientific data analysis has traditionally relied on linear algebra and stochastic process theory.

no code implementations • 16 Jan 2020 • Chen Cai, Yusu Wang

For shape segmentation and classification, however, we note that persistence pairing shows significant power on most of the benchmark datasets, and improves over both summaries based on merely critical values, and those based on permutation tests.

no code implementations • 15 Sep 2019 • Tamal K. Dey, Jiayuan Wang, Yusu Wang

Next, in a fully automatic framework, we leverage the power of the discrete-Morse based graph reconstruction algorithm to train a CNN from a collection of images without labelled data and use the same algorithm to produce the final output from the segmented images created by the trained CNN.

1 code implementation • NeurIPS 2019 • Qi Zhao, Yusu Wang

However often in practice, the choice of the weight function should depend on the nature of the specific type of data one considers, and it is thus highly desirable to learn a best weight function (and thus metric for persistence diagrams) from labelled data.

Ranked #1 on Graph Classification on NEURON-BINARY

Computational Geometry

2 code implementations • 8 Nov 2018 • Chen Cai, Yusu Wang

We test our baseline representation for the graph classification task on a range of graph datasets.

Ranked #15 on Graph Classification on MUTAG

no code implementations • 27 Jun 2018 • Chao Chen, Xiuyan Ni, Qinxun Bai, Yusu Wang

In particular, our measurement of topological complexity incorporates the importance of topological features (e. g., connected components, handles, and so on) in a meaningful manner, and provides a direct control over spurious topological structures.

1 code implementation • 14 Mar 2018 • Tamal K. Dey, Jiayuan Wang, Yusu Wang

Specifically, first, leveraging existing theoretical understanding of persistence-guided discrete Morse cancellation, we provide a simplified version of the existing discrete Morse-based graph reconstruction algorithm.

Computational Geometry

no code implementations • ICML 2017 • Xiuyan Ni, Novi Quadrianto, Yusu Wang, Chao Chen

Clustering data with both continuous and discrete attributes is a challenging task.

no code implementations • 20 Jun 2017 • Justin Eldridge, Mikhail Belkin, Yusu Wang

Classical matrix perturbation results, such as Weyl's theorem for eigenvalues and the Davis-Kahan theorem for eigenvectors, are general purpose.

no code implementations • NeurIPS 2016 • Justin Eldridge, Mikhail Belkin, Yusu Wang

In this work we develop a theory of hierarchical clustering for graphs.

no code implementations • 21 Jun 2015 • Justin Eldridge, Mikhail Belkin, Yusu Wang

In this paper we identify two limit properties, separation and minimality, which address both over-segmentation and improper nesting and together imply (but are not implied by) Hartigan consistency.

no code implementations • NeurIPS 2014 • Qichao Que, Mikhail Belkin, Yusu Wang

In this paper we propose a framework for supervised and semi-supervised learning based on reformulating the learning problem as a regularized Fredholm integral equation.

no code implementations • NeurIPS 2011 • Xiaoyin Ge, Issam I. Safa, Mikhail Belkin, Yusu Wang

While such data is often high-dimensional, it is of interest to approximate it with a low-dimensional or even one-dimensional space, since many important aspects of data are often intrinsically low-dimensional.

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