3 papers
cs.LG2024
Disentangled and Self-Explainable Node Representation Learning
Simone Piaggesi, André Panisson, Megha Khosla
Node representations, or embeddings, are low-dimensional vectors that capture node properties, typically learned through unsupervised structural similarity objectives or supervised…
cs.IR2023
Efficient Neural Ranking using Forward Indexes and Lightweight Encoders
Jurek Leonhardt, Henrik Müller, Koustav Rudra +3
Dual-encoder-based dense retrieval models have become the standard in IR. They employ large Transformer-based language models, which are notoriously inefficient in terms of resourc…
cs.LG2023
DINE: Dimensional Interpretability of Node Embeddings
Simone Piaggesi, Megha Khosla, André Panisson +1
Graphs are ubiquitous due to their flexibility in representing social and technological systems as networks of interacting elements. Graph representation learning methods, such as…