8 citations · 9 across the 5 of their papers we have counts for
5 papers
Coarse-Grained Boltzmann Generators
Weilong Chen, Bojun Zhao, Jan Eckwert +1
Sampling equilibrium molecular configurations from the Boltzmann distribution is a longstanding challenge. Boltzmann Generators (BGs) address this by combining exact-likelihood gen…
DD-Ranking: Rethinking the Evaluation of Dataset Distillation
Zekai Li, Xinhao Zhong, Samir Khaki +49
In recent years, dataset distillation has provided a reliable solution for data compression, where models trained on the resulting smaller synthetic datasets achieve performance co…
Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation
Boyan Gao, Bo Zhao, Shreyank N Gowda +4
Dataset condensation aims to synthesize datasets with a few representative samples that can effectively represent the original datasets. This enables efficient training and produce…
Image Captions are Natural Prompts for Text-to-Image Models
Shiye Lei, Hao Chen, Sen Zhang +2
With the rapid development of Artificial Intelligence Generated Content (AIGC), it has become a common practice to train models on synthetic data due to data-scarcity and privacy l…
CAFE: Learning to Condense Dataset by Aligning Features
Kai Wang, Bo Zhao, Xiangyu Peng +7
Dataset condensation aims at reducing the network training effort through condensing a cumbersome training set into a compact synthetic one. State-of-the-art approaches largely rel…