4 citations · 7 across the 2 of their papers we have counts for
2 papers
cs.AI2024★ 4 cited
Tiny Models are the Computational Saver for Large Models
Qingyuan Wang, Barry Cardiff, Antoine Frappé +2
This paper introduces TinySaver, an early-exit-like dynamic model compression approach which employs tiny models to substitute large models adaptively. Distinct from traditional co…
cs.LG2024★ 3 cited
DyCE: Dynamically Configurable Exiting for Deep Learning Compression and Real-time Scaling
Qingyuan Wang, Barry Cardiff, Antoine Frappé +2
Conventional deep learning (DL) model compression and scaling methods focus on altering the model's components, impacting the results across all samples uniformly. However, since s…