Search Results for author:
Found 2 papers, 2 papers with code
Forget Embedding Layers: Representation Learning for Cold-start in Recommender Systems
In this paper, we present FELRec, a large embedding network that refines the existing representations of users and items in a recursive manner, as new information becomes available.
Understanding microbiome dynamics via interpretable graph representation learning
Through our experiments, we show that we can extract graph features such as clusters of nodes or edges that have the highest impact on the model to learn the low-dimensional representation.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
