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Found 5 papers, 4 papers with code
Efficient Generator of Mathematical Expressions for Symbolic Regression
We empirically show that HVAE can be trained efficiently with small corpora of mathematical expressions and can accurately encode expressions into a smooth low-dimensional latent space.
Link Analysis meets Ontologies: Are Embeddings the Answer?
The increasing amounts of semantic resources offer valuable storage of human knowledge; however, the probability of wrong entries increases with the increased size.
Transfer Learning for Node Regression Applied to Spreading Prediction
This work is one of the first to explore transferability of the learned representations for the task of node regression; we show there exist pairs of networks with similar structure between which the trained models can be transferred (zero-shot), and demonstrate their competitive performance.
Predicting Generalization in Deep Learning via Metric Learning -- PGDL Shared task
The competition "Predicting Generalization in Deep Learning (PGDL)" aims to provide a platform for rigorous study of generalization of deep learning models and offer insight into the progress of understanding and explaining these models.
SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations
Learning from complex real-life networks is a lively research area, with recent advances in learning information-rich, low-dimensional network node representations.
Ranked #17 on
Node Classification
on Coauthor CS
