68 citations · 72 across the 6 of their papers we have counts for
5 papers
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…
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…
Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk?
Iyiola E. Olatunji, Anmar Hizber, Oliver Sihlovec +1
Graph neural networks (GNNs) have shown promising results on real-life datasets and applications, including healthcare, finance, and education. However, recent studies have shown t…
Privacy and Transparency in Graph Machine Learning: A Unified Perspective
Megha Khosla
Graph Machine Learning (GraphML), whereby classical machine learning is generalized to irregular graph domains, has enjoyed a recent renaissance, leading to a dizzying array of mod…
Macroscopic quantum information processing using spin coherent states
Tim Byrnes, Daniel Rosseau, Megha Khosla +6
Previously a new scheme of quantum information processing based on spin coherent states of two component Bose-Einstein condensates was proposed (Byrnes {\it et al.} Phys. Rev. A 85…