2 citations · 2 across the 10 of their papers we have counts for
4 papers · 1 filter
Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development
Nadine Chang, Maying Shen, Jialiang Wang +2
Many modern AI systems are designed to operate under diverse, open-ended, use-cases. To help generalize deployed systems, many deployed-system maintenance pipelines use a reactive…
Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems
Tolga Dimlioglu, Nadine Chang, Maying Shen +2
Large-scale deep learning models for physical AI applications depend on diverse training data collection efforts. These models and correspondingly, the training data, must address…
Minority Reports: Balancing Cost and Quality in Ground Truth Data Annotation
Hsuan Wei Liao, Christopher Klugmann, Daniel Kondermann +1
High-quality data annotation is an essential but laborious and costly aspect of developing machine learning-based software. We explore the inherent tradeoff between annotation accu…
AutoScale: Scale-Aware Data Mixing for Pre-Training LLMs
Feiyang Kang, Yifan Sun, Bingbing Wen +4
Domain reweighting is an emerging research area aimed at adjusting the relative weights of different data sources to improve the effectiveness and efficiency of LLM pre-training. W…