2 citations · 3 across the 5 of their papers we have counts for
8 papers
BN-Pool: Bayesian Nonparametric Pooling for Graphs
Daniele Castellana, Filippo Maria Bianchi
We introduce BN-Pool, the first clustering-based pooling method for Graph Neural Networks that adaptively determines the number of supernodes in a coarsened graph. BN-Pool leverage…
Lying Graph Convolution: Learning to Lie for Node Classification Tasks
Daniele Castellana
In the context of machine learning for graphs, many researchers have empirically observed that Deep Graph Networks (DGNs) perform favourably on node classification tasks when the g…
Investigating the Interplay between Features and Structures in Graph Learning
Daniele Castellana, Federico Errica
In the past, the dichotomy between homophily and heterophily has inspired research contributions toward a better understanding of Deep Graph Networks' inductive bias. In particular…
Learning from Non-Binary Constituency Trees via Tensor Decomposition
Daniele Castellana, Davide Bacciu
Processing sentence constituency trees in binarised form is a common and popular approach in literature. However, constituency trees are non-binary by nature. The binarisation proc…
Generalising Recursive Neural Models by Tensor Decomposition
Daniele Castellana, Davide Bacciu
Most machine learning models for structured data encode the structural knowledge of a node by leveraging simple aggregation functions (in neural models, typically a weighted sum) o…
Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data
Daniele Castellana, Davide Bacciu
The paper introduces two new aggregation functions to encode structural knowledge from tree-structured data. They leverage the Canonical and Tensor-Train decompositions to yield ex…
Bayesian Tensor Factorisation for Bottom-up Hidden Tree Markov Models
Daniele Castellana, Davide Bacciu
Bottom-Up Hidden Tree Markov Model is a highly expressive model for tree-structured data. Unfortunately, it cannot be used in practice due to the intractable size of its state-tran…
Learning Tree Distributions by Hidden Markov Models
Davide Bacciu, Daniele Castellana
Hidden tree Markov models allow learning distributions for tree structured data while being interpretable as nondeterministic automata. We provide a concise summary of the main app…