41 citations · 41 across the 6 of their papers we have counts for
6 papers
TACOS: Open Tagging and Comparative Scoring for Instruction Fine-Tuning Data Selection
Xixiang He, Hao Yu, Qiyao Sun +4
Instruction Fine-Tuning (IFT) is crucial for aligning large language models (LLMs) with human preferences, and selecting a small yet representative subset from massive data signifi…
Efficient and Scalable Chinese Vector Font Generation via Component Composition
Jinyu Song, Weitao You, Shuhui Shi +3
Chinese vector font generation is challenging due to the complex structure and huge amount of Chinese characters. Recent advances remain limited to generating a small set of charac…
DARTS Once More: Enhancing Differentiable Architecture Search by Masked Image Modeling
Bicheng Guo, Shuxuan Guo, Miaojing Shi +4
Differentiable architecture search (DARTS) has been a mainstream direction in automatic machine learning. Since the discovery that original DARTS will inevitably converge to poor a…
Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions
Shuxuan Guo, Yinlin Hu, Jose M. Alvarez +1
Knowledge distillation facilitates the training of a compact student network by using a deep teacher one. While this has achieved great success in many tasks, it remains completely…
Distilling Image Classifiers in Object Detectors
Shuxuan Guo, Jose M. Alvarez, Mathieu Salzmann
Knowledge distillation constitutes a simple yet effective way to improve the performance of a compact student network by exploiting the knowledge of a more powerful teacher. Nevert…
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks
Shuxuan Guo, Jose M. Alvarez, Mathieu Salzmann
We introduce an approach to training a given compact network. To this end, we leverage over-parameterization, which typically improves both neural network optimization and generali…