no code implementations • EMNLP (sustainlp) 2021 • Zachary Zhou, Jeffery Kline, Devin Conathan, Glenn Fung
We address the problem of link prediction between entities and relations of knowledge graphs.
no code implementations • 2 Nov 2022 • Zachary Zhou, Alisha Zachariah, Devin Conathan, Jeffery Kline
We report empirical results of DEA applied to 14 different language models that have a variety of architectures, and we show that DEA can be used to identify a subset of models that effectively balance resource demands against performance.
1 code implementation • 21 Jul 2022 • Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn M Fung, Vikas Singh
Transformers have emerged as a preferred model for many tasks in natural langugage processing and vision.
no code implementations • NeurIPS Workshop TDA_and_Beyond 2020 • Eric Bunch, Jeffery Kline, Daniel Dickinson, Suhaas Bhat, Glenn Fung
Metric space magnitude, an active field of research in algebraic topology, is a scalar quantity that summarizes the effective number of distinct points that live in a general metric space.
no code implementations • 24 Jun 2020 • Eric Bunch, Daniel Dickinson, Jeffery Kline, Glenn Fung
In a more general setting, the magnitude of a metric space is a real number that aims to quantify the effective number of distinct points in the space.