6 citations · 7 across the 3 of their papers we have counts for
3 papers
quant-ph2023
Evaluating quantum generative models via imbalanced data classification benchmarks
Graham R. Enos, Matthew J. Reagor, Eric Hulburd
A limited set of tools exist for assessing whether the behavior of quantum machine learning models diverges from conventional models, outside of abstract or theoretical settings. W…
cs.CL2020★ 6 cited
Exploring BERT Parameter Efficiency on the Stanford Question Answering Dataset v2.0
Eric Hulburd
In this paper we explore the parameter efficiency of BERT arXiv:1810.04805 on version 2.0 of the Stanford Question Answering dataset (SQuAD2.0). We evaluate the parameter efficienc…
cs.CL2019★ 1 cited
Exploring Neural Net Augmentation to BERT for Question Answering on SQUAD 2.0
Suhas Gupta
Enhancing machine capabilities to answer questions has been a topic of considerable focus in recent years of NLP research. Language models like Embeddings from Language Models (ELM…