Search Results for author:
Found 12 papers, 6 papers with code
Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space
To characterize the function space explored by neural networks (NNs) is an important aspect of learning theory.
A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks
Oversmoothing is a central challenge of building more powerful Graph Neural Networks (GNNs).
A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks
To understand the training dynamics of neural networks (NNs), prior studies have considered the infinite-width mean-field (MF) limit of two-layer NN, establishing theoretical guarantees of its convergence under gradient flow training as well as its approximation and generalization capabilities.
On Feature Learning in Neural Networks with Global Convergence Guarantees
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
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
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.
A Dynamical Central Limit Theorem for Shallow Neural Networks
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?
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
We propose Symplectic Recurrent Neural Networks (SRNNs) as learning algorithms that capture the dynamics of physical systems from observed trajectories.
On the equivalence between graph isomorphism testing and function approximation with GNNs
We further develop a framework of the expressive power of GNNs that incorporates both of these viewpoints using the language of sigma-algebra, through which we compare the expressive power of different types of GNNs together with other graph isomorphism tests.
Ranked #27 on
Graph Regression
on ZINC-500k
Community Detection with Graph Neural Networks
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.
Supervised Community Detection with Line Graph Neural Networks
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)
