2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.CV2025
AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training
Feiyang Kang, Nadine Chang, Maying Shen +4
The computational burden and inherent redundancy of large-scale datasets challenge the training of contemporary machine learning models. Data pruning offers a solution by selecting…
cs.LG2025
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…
cs.GT2024★ 2 cited
Pricing and Competition for Generative AI
Rafid Mahmood
Compared to classical machine learning (ML) models, generative models offer a new usage paradigm where (i) a single model can be used for many different tasks out-of-the-box; (ii)…