12 citations · 14 across the 4 of their papers we have counts for
4 papers · 1 filter
Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor Generalization
Davide Buffelli, Jamie McGowan, Wangkun Xu +4
Second-order optimization has been shown to accelerate the training of deep neural networks in many applications, often yielding faster progress per iteration on the training loss…
The Deep Equilibrium Algorithmic Reasoner
Dobrik Georgiev, Pietro Liò, Davide Buffelli
Recent work on neural algorithmic reasoning has demonstrated that graph neural networks (GNNs) could learn to execute classical algorithms. Doing so, however, has always used a rec…
SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks
Davide Buffelli, Pietro Liò, Fabio Vandin
In the past few years, graph neural networks (GNNs) have become the de facto model of choice for graph classification. While, from the theoretical viewpoint, most GNNs can operate…
Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach
Davide Buffelli, Fabio Vandin
Graph Neural Networks (GNNs) have become the state-of-the-art method for many applications on graph structured data. GNNs are a model for graph representation learning, which aims…