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08/21/2023
Approximately Equivariant Graph Networks
Graph neural networks (GNNs) are commonly described as being permutation...
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06/06/2023
Fine-grained Expressivity of Graph Neural Networks
Numerous recent works have analyzed the expressive power of message-pass...
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11/06/2022
Graph Neural Networks for Community Detection on Sparse Graphs
Spectral methods provide consistent estimators for community detection i...
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10/26/2022
Deep Learning is Provably Robust to Symmetric Label Noise
Deep neural networks (DNNs) are capable of perfectly fitting the trainin...
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09/24/2022
From Local to Global: Spectral-Inspired Graph Neural Networks
Graph Neural Networks (GNNs) are powerful deep learning methods for Non-...
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06/27/2022
Endowing Language Models with Multimodal Knowledge Graph Representations
We propose a method to make natural language understanding models more p...
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05/28/2022
Deep Learning with Label Noise: A Hierarchical Approach
Deep neural networks are susceptible to label noise. Existing methods to...
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01/18/2022
A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants
Graph neural networks are designed to learn functions on graphs. Typical...
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11/23/2020
Dimensionality reduction, regularization, and generalization in overparameterized regressions
Overparameterization in deep learning is powerful: Very large models fit...
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10/25/2020