Search Results for author: Zhengdao Chen

Found 9 papers, 6 papers with code

On Feature Learning in Neural Networks with Global Convergence Guarantees

no code implementations22 Apr 2022 Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna

We study the optimization of wide neural networks (NNs) via gradient flow (GF) in setups that allow feature learning while admitting non-asymptotic global convergence guarantees.

On feature learning in shallow and multi-layer neural networks with global convergence guarantees

no code implementations ICLR 2022 Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna

We study the optimization of over-parameterized shallow and multi-layer neural networks (NNs) in a regime that allows feature learning while admitting non-asymptotic global convergence guarantees.

On Graph Neural Networks versus Graph-Augmented MLPs

1 code implementation ICLR 2021 Lei Chen, Zhengdao Chen, Joan Bruna

From the perspective of expressive power, this work compares multi-layer Graph Neural Networks (GNNs) with a simplified alternative that we call Graph-Augmented Multi-Layer Perceptrons (GA-MLPs), which first augments node features with certain multi-hop operators on the graph and then applies an MLP in a node-wise fashion.

Community Detection

A Dynamical Central Limit Theorem for Shallow Neural Networks

no code implementations NeurIPS 2020 Zhengdao Chen, Grant M. Rotskoff, Joan Bruna, Eric Vanden-Eijnden

Furthermore, if the mean-field dynamics converges to a measure that interpolates the training data, we prove that the asymptotic deviation eventually vanishes in the CLT scaling.

Can Graph Neural Networks Count Substructures?

1 code implementation NeurIPS 2020 Zhengdao Chen, Lei Chen, Soledad Villar, Joan Bruna

We also prove positive results for k-WL and k-IGNs as well as negative results for k-WL with a finite number of iterations.

Symplectic Recurrent Neural Networks

1 code implementation ICLR 2020 Zhengdao Chen, Jianyu Zhang, Martin Arjovsky, Léon Bottou

We propose Symplectic Recurrent Neural Networks (SRNNs) as learning algorithms that capture the dynamics of physical systems from observed trajectories.

Community Detection with Graph Neural Networks

2 code implementations ICLR 2018 Zhengdao Chen, Xiang Li, Joan Bruna

This graph inference task can be recast as a node-wise graph classification problem, and, as such, computational detection thresholds can be translated in terms of learning within appropriate models.

Community Detection Graph Classification +1

Supervised Community Detection with Line Graph Neural Networks

4 code implementations ICLR 2019 Zhengdao Chen, Xiang Li, Joan Bruna

We show that, in a data-driven manner and without access to the underlying generative models, they can match or even surpass the performance of the belief propagation algorithm on binary and multi-class stochastic block models, which is believed to reach the computational threshold.

 Ranked #1 on Community Detection on Amazon (Accuracy-NE metric, using extra training data)

Community Detection Graph Classification +1

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