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Found 10 papers, 6 papers with code
The Deep Equilibrium Algorithmic Reasoner
Recent work on neural algorithmic reasoning has demonstrated that graph neural networks (GNNs) could learn to execute classical algorithms.
Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems?
In the past few years, deep learning methods have attracted a lot of research, and are now heavily used in modern real-world recommender systems.
Extending Logic Explained Networks to Text Classification
Recently, Logic Explained Networks (LENs) have been proposed as explainable-by-design neural models providing logic explanations for their predictions.
Scalable Regularization of Scene Graph Generation Models using Symbolic Theories
Our work introduces a regularization technique for injecting symbolic background knowledge into neural SGG models that overcomes the limitations of prior art.
SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks
Previous works have tried to tackle this issue in graph classification by providing the model with inductive biases derived from assumptions on the generative process of the graphs, or by requiring access to graphs from the test domain.
Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach
While this approach achieves great results in the single-task setting, the generation of node embeddings that can be used to perform multiple tasks (with performance comparable to single-task models) is still an open problem.
Are Graph Convolutional Networks Fully Exploiting the Graph Structure?
Graph Convolutional Networks (GCNs) represent the state-of-the-art for many graph related tasks.
A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings
We show that the embeddings produced by our method can be used to perform multiple tasks with comparable or higher performance than classically trained models.
The Impact of Global Structural Information in Graph Neural Networks Applications
Graph Neural Networks (GNNs) rely on the graph structure to define an aggregation strategy where each node updates its representation by combining information from its neighbours.
Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation
We propose a simple and effective transfer-learning based strategy to adapt a model to a specific user, providing an average increment of $6\%$ on the F1 score on the predictions for that user.
