Search Results for author:
Found 8 papers, 5 papers with code
Lying Graph Convolution: Learning to Lie for Node Classification Tasks
In this paper, we introduce Lying-GCN, a new DGN inspired by opinion dynamics that can adaptively work in both the heterophilic and the homophilic setting.
Investigating the Interplay between Features and Structures in Graph Learning
Our findings reveal that previously defined metrics are not adequate when we relax the above assumption.
The Infinite Contextual Graph Markov Model
The Contextual Graph Markov Model is a deep, unsupervised, and probabilistic model for graphs that is trained incrementally on a layer-by-layer basis.
Learning from Non-Binary Constituency Trees via Tensor Decomposition
Finally, we introduce a Tree-LSTM model which takes advantage of this composition function and we experimentally assess its performance on different NLP tasks.
Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data
The paper introduces two new aggregation functions to encode structural knowledge from tree-structured data.
Generalising Recursive Neural Models by Tensor Decomposition
This approximation allows limiting the parameters space size, decoupling it from its strict relation with the size of the hidden encoding space.
Bayesian Tensor Factorisation for Bottom-up Hidden Tree Markov Models
Bottom-Up Hidden Tree Markov Model is a highly expressive model for tree-structured data.
Learning Tree Distributions by Hidden Markov Models
Hidden tree Markov models allow learning distributions for tree structured data while being interpretable as nondeterministic automata.
