UniCompress: Enhancing Multi-Data Medical Image Compression with Knowledge Distillation

no code implementations • 27 May 2024 • Runzhao Yang, Yinda Chen, Zhihong Zhang, Xiaoyu Liu, Zongren Li, Kunlun He, Zhiwei Xiong, Jinli Suo, Qionghai Dai

In the field of medical image compression, Implicit Neural Representation (INR) networks have shown remarkable versatility due to their flexible compression ratios, yet they are constrained by a one-to-one fitting approach that results in lengthy encoding times.

CUTS+: High-dimensional Causal Discovery from Irregular Time-series

1 code implementation • 10 May 2023 • Yuxiao Cheng, Lianglong Li, Tingxiong Xiao, Zongren Li, Qin Zhong, Jinli Suo, Kunlun He

Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios.

Causal Discovery Decision Making +3

CUTS: Neural Causal Discovery from Irregular Time-Series Data

1 code implementation • 15 Feb 2023 • Yuxiao Cheng, Runzhao Yang, Tingxiong Xiao, Zongren Li, Jinli Suo, Kunlun He, Qionghai Dai

Causal discovery from time-series data has been a central task in machine learning.

Causal Discovery Irregular Time Series +2