3 papers
cs.CL2026
A Benchmark Construction and Evaluation Framework for Specialist Domains: Case Study on Defense-related Documents
Bao Gia Doan, Aditya Joshi, Pantelis Elinas +4
RAG-based question-answering (QA) in specialist domains faces a cold-start problem: lack of evaluative benchmarks and absence of labeled data for post-training. We present DoRA (Do…
cs.LG2022
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision
Pantelis Elinas, Edwin V. Bonilla
Learning useful node and graph representations with graph neural networks (GNNs) is a challenging task. It is known that deep GNNs suffer from over-smoothing where, as the number o…
cs.LG2019
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
Pantelis Elinas, Edwin V. Bonilla, Louis Tiao
We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial a…