7 citations · 8 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 1 cited
Scalable End-to-End ML Platforms: from AutoML to Self-serve
Igor L. Markov, Pavlos A. Apostolopoulos, Mia R. Garrard +8
ML platforms help enable intelligent data-driven applications and maintain them with limited engineering effort. Upon sufficiently broad adoption, such platforms reach economies of…
cs.CL2020★ 7 cited
Generation-Distillation for Efficient Natural Language Understanding in Low-Data Settings
Luke Melas-Kyriazi, George Han, Celine Liang
Over the past year, the emergence of transfer learning with large-scale language models (LM) has led to dramatic performance improvements across a broad range of natural language u…