4 papers
Lifting Finite-Sample Gaussian Approximation of Wide Neural Networks to Functional Convergence
Luis Wiederhold, Nil Ayday, Debarghya Ghoshdastidar
Randomly initialised neural networks of width converge to Gaussian processes as , with two types of quantitative bounds characterising this convergence. Finite-sa…
Different Statistical Perspectives for Understanding Generalisation in Graph Neural Networks
Nil Ayday, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar
Graph Neural Networks (GNN) are currently the most popular approach for learning and prediction on graph-structured data and are deployed in various fields, from social network ana…
Gaussian Process Limit Reveals Structural Benefits of Graph Transformers
Nil Ayday, Lingchu Yang, Debarghya Ghoshdastidar
Graph transformers are the state-of-the-art for learning from graph-structured data and are empirically known to avoid several pitfalls of message-passing architectures. However, t…
Exact Generalisation Error Exposes Benchmarks Skew Graph Neural Networks Success (or Failure)
Nil Ayday, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar
Graph Neural Networks (GNNs) have become the standard method for learning from networks across fields ranging from biology to social systems, yet a principled understanding of what…