9 citations · 10 across the 2 of their papers we have counts for
5 papers
On Feature Learning in Neural Networks with Global Convergence Guarantees
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. F…
On Graph Neural Networks versus Graph-Augmented MLPs
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 Percept…
Can Graph Neural Networks Count Substructures?
Zhengdao Chen, Lei Chen, Soledad Villar +1
The ability to detect and count certain substructures in graphs is important for solving many tasks on graph-structured data, especially in the contexts of computational chemistry…
Symplectic Recurrent Neural Networks
Zhengdao Chen, Jianyu Zhang, Martin Arjovsky +1
We propose Symplectic Recurrent Neural Networks (SRNNs) as learning algorithms that capture the dynamics of physical systems from observed trajectories. An SRNN models the Hamilton…
Structure-preserving numerical integrators for Hodgkin-Huxley-type systems
Zhengdao Chen, Baranidharan Raman, Ari Stern
Motivated by the Hodgkin-Huxley model of neuronal dynamics, we study explicit numerical integrators for "conditionally linear" systems of ordinary differential equations. We show t…