Search Results for author: Derek Lim

Found 10 papers, 9 papers with code

Sign and Basis Invariant Networks for Spectral Graph Representation Learning

1 code implementation25 Feb 2022 Derek Lim, Joshua Robinson, Lingxiao Zhao, Tess Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka

They are also theoretically strong for graph representation learning -- they can approximate any spectral graph convolution, can compute spectral invariants that go beyond message passing neural networks, and can provably simulate previously proposed graph positional encodings.

Graph Regression Graph Representation Learning

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

2 code implementations NeurIPS 2021 Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, Ser-Nam Lim

Many widely used datasets for graph machine learning tasks have generally been homophilous, where nodes with similar labels connect to each other.

Graph Learning

Equivariant Subgraph Aggregation Networks

1 code implementation ICLR 2022 Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron

Thus, we propose to represent each graph as a set of subgraphs derived by some predefined policy, and to process it using a suitable equivariant architecture.

Equivariant Manifold Flows

1 code implementation NeurIPS 2021 Isay Katsman, Aaron Lou, Derek Lim, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa

Tractably modelling distributions over manifolds has long been an important goal in the natural sciences.

New Benchmarks for Learning on Non-Homophilous Graphs

1 code implementation3 Apr 2021 Derek Lim, Xiuyu Li, Felix Hohne, Ser-Nam Lim

Much data with graph structures satisfy the principle of homophily, meaning that connected nodes tend to be similar with respect to a specific attribute.

Fraud Detection Graph Representation Learning +1

Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results

no code implementations6 Dec 2020 Behrooz Tahmasebi, Derek Lim, Stefanie Jegelka

While message passing Graph Neural Networks (GNNs) have become increasingly popular architectures for learning with graphs, recent works have revealed important shortcomings in their expressive power.

Doubly Stochastic Subspace Clustering

1 code implementation30 Nov 2020 Derek Lim, René Vidal, Benjamin D. Haeffele

Many state-of-the-art subspace clustering methods follow a two-step process by first constructing an affinity matrix between data points and then applying spectral clustering to this affinity.

Image Clustering

Neural Manifold Ordinary Differential Equations

2 code implementations NeurIPS 2020 Aaron Lou, Derek Lim, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa

To better conform to data geometry, recent deep generative modelling techniques adapt Euclidean constructions to non-Euclidean spaces.

Density Estimation

Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform

1 code implementation15 Jun 2020 Derek Lim, Austin R. Benson

For example, expertise on song annotations follows a "U shape" where experts are both early and late contributors with non-experts contributing intermediately; we develop a user utility model that captures such behavior.

The Doubly Stochastic Single Eigenvalue Problem: A Computational Approach

1 code implementation9 Aug 2019 Amit Harlev, Charles R. Johnson, Derek Lim

The problem of determining $DS_n$, the complex numbers that occur as an eigenvalue of an $n$-by-$n$ doubly stochastic matrix, has been a target of study for some time.

Spectral Theory 15-04, 15A18, 15A29, 15B51

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