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Found 7 papers, 1 papers with code
$\nabla τ$: Gradient-based and Task-Agnostic machine Unlearning
In this study, we introduce Gradient-based and Task-Agnostic machine Unlearning ($\nabla \tau$), an optimization framework designed to remove the influence of a subset of training data efficiently.
Link Prediction under Heterophily: A Physics-Inspired Graph Neural Network Approach
Physics-Inspired GNNs such as GRAFF provided a significant contribution to enhance node classification performance under heterophily, thanks to the adoption of physics biases in the message-passing.
A topological description of loss surfaces based on Betti Numbers
In the context of deep learning models, attention has recently been paid to studying the surface of the loss function in order to better understand training with methods based on gradient descent.
Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design
Most of the current hypergraph learning methodologies and benchmarking datasets in the hypergraph realm are obtained by lifting procedures from their graph analogs, leading to overshadowing specific characteristics of hypergraphs.
Combining Distance to Class Centroids and Outlier Discounting for Improved Learning with Noisy Labels
In this paper, we propose a new approach for addressing the challenge of training machine learning models in the presence of noisy labels.
Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels
In practical settings, classification datasets are obtained through a labelling process that is usually done by humans.
NEWRON: A New Generalization of the Artificial Neuron to Enhance the Interpretability of Neural Networks
In this work, we formulate NEWRON: a generalization of the McCulloch-Pitts neuron structure.
