most citedSTREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings

1 citations · 1 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2023

Using Early Readouts to Mediate Featural Bias in Distillation

Rishabh Tiwari, Durga Sivasubramanian, Anmol Mekala +2

Deep networks tend to learn spurious feature-label correlations in real-world supervised learning tasks. This vulnerability is aggravated in distillation, where a student model may…

cs.LG2023★ 1 cited

STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings

Nathan Beck, Suraj Kothawade, Pradeep Shenoy +1

Deep neural networks have consistently shown great performance in several real-world use cases like autonomous vehicles, satellite imaging, etc., effectively leveraging large corpo…