6 citations · 6 across the 2 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
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…