1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2026
The Appeal and Reality of Recycling LoRAs with Adaptive Merging
Haokun Liu, Gyung Hyun Je, Marco Ciccone +3
The widespread availability of fine-tuned LoRA modules for open pre-trained models has led to an interest in methods that can adaptively merge LoRAs to improve performance. These m…
cs.LG2025
Efficiently Estimating Data Efficiency for Language Model Fine-tuning
Gyung Hyun Je, Colin Raffel
While large language models (LLMs) demonstrate reasonable zero-shot capability across many downstream tasks, fine-tuning is a common practice to improve their performance. However,…
cs.CV2024★ 1 cited
Constellation Dataset: Benchmarking High-Altitude Object Detection for an Urban Intersection
Mehmet Kerem Turkcan, Sanjeev Narasimhan, Chengbo Zang +6
We introduce Constellation, a dataset of 13K images suitable for research on detection of objects in dense urban streetscapes observed from high-elevation cameras, collected for a…