Jun S. Liu — accepted AI-conference papers

2025

JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics

Yuanchuan Guo, Jun S. Liu, Huimin Cheng, Ying Ma

NeurIPS 2025poster

As spatial transcriptomics (ST) datasets increasingly span multiple adjacent or replicated slices, effective joint analysis across slices is needed to reconstruct tissue structures and identify consistent spatial gene expression patterns. This requires resolving spatial correspondences between slice…

Cited by 0 Source ↗Scholar ↗

2020

Probabilistic Connection Importance Inference and Lossless Compression of Deep Neural Networks

Xin Xing, Long Sha, Pengyu Hong, Zuofeng Shang, Jun S. Liu

ICLR 2020poster

Deep neural networks (DNNs) can be huge in size, requiring a considerable a mount of energy and computational resources to operate, which limits their applications in numerous scenarios. It is thus of interest to compress DNNs while maintaining their performance levels. We here propose a probabilis…

Cited by 8 Source ↗Scholar ↗