Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression. - Yufei Cui; Ziquan Liu; Qiao Li 001; Yu Mao 001; Antoni B. Chan; Chun Jason Xue (CoRR, 2021)

The Implicit Biases of Stochastic Gradient Descent on Deep Neural Networks with Batch Normalization. - Ziquan Liu; Yufei Cui; Jia Wan 001; Yu Mao 001; Antoni B. Chan (CoRR, 2021)

Accelerating General-purpose Lossless Compression via Simple and Scalable Parameterization. - Yu Mao 001; Yufei Cui; Tei-Wei Kuo; Chun Jason Xue (ACM Multimedia, 2022)

TRACE: A Fast Transformer-based General-Purpose Lossless Compressor. - Yu Mao 001; Yufei Cui; Tei-Wei Kuo; Chun Jason Xue (WWW, 2022)

A Fast Transformer-based General-Purpose Lossless Compressor. - Yu Mao 001; Yufei Cui; Tei-Wei Kuo; Chun Jason Xue (CoRR, 2022)

Faster and Stronger Lossless Compression with Optimized Autoregressive Framework. - Yu Mao 001; Jingzong Li; Yufei Cui; Chun Jason Xue (DAC, 2023)

Moby: Empowering 2D Models for Efficient Point Cloud Analytics on the Edge. - Jingzong Li; Yik Hong Cai; Libin Liu 001; Yu Mao 001; Chun Jason Xue; Hong Xu 001 (ACM Multimedia, 2023)

2D-Empowered 3D Object Detection on the Edge. - Jingzong Li; Yik Hong Cai; Libin Liu 001; Yu Mao 001; Chun Jason Xue; Hong Xu 001 (CoRR, 2023)

Variational Nested Dropout. - Yufei Cui; Yu Mao 001; Ziquan Liu; Qiao Li 001; Antoni B. Chan; Xue (Steve) Liu; Tei-Wei Kuo; Chun Jason Xue (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models. - Weilan Wang; Yu Mao 001; Dongdong Tang; Hongchao Du; Nan Guan; Chun Jason Xue (EMNLP (Findings), 2024)

STEM: Streaming-Based FPGA Acceleration for Large-Scale Compactions in LSM KV. - Dongdong Tang; Weilan Wang; Yu Mao 001; Jinghuan Yu; Tei-Wei Kuo; Chun Jason Xue (ICDE, 2024)

On the Compressibility of Quantized Large Language Models. - Yu Mao 001; Weilan Wang; Hongchao Du; Nan Guan; Chun Jason Xue (CoRR, 2024)

Pre-processing matters: A segment search method for WSI classification. - Jun Wang; Yufei Cui; Yu Mao 001; Nan Guan; Chun Jason Xue (CoRR, 2024)

IHC Matters: Incorporating IHC analysis to H&E Whole Slide Image Analysis for Improved Cancer Grading via Two-stage Multimodal Bilinear Pooling Fusion. - Jun Wang; Yu Mao 001; Yufei Cui; Nan Guan; Chun Jason Xue (CoRR, 2024)

Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions. - Jun Wang; Yu Mao 001; Nan Guan; Chun Jason Xue (CoRR, 2024)

SHAP-CAT: A interpretable multi-modal framework enhancing WSI classification via virtual staining and shapley-value-based multimodal fusion. - Jun Wang; Yu Mao 001; Nan Guan; Chun Jason Xue (CoRR, 2024)

WISE: A Framework for Gigapixel Whole-Slide-Image Lossless Compression. - Yu Mao 001; Jun Wang; Nan Guan; Chun Jason Xue (CVPR, 2025)

Easz: An Agile Transformer-based Image Compression Framework for Resource-constrained IoTs. - Yu Mao 001; Jingzong Li; Jun Wang; Hong Xu 001; Tei-Wei Kuo; Nan Guan; Chun Jason Xue (DAC, 2025)

DAWN: Accelerating Point Cloud Object Detection via Object-Aware Partitioning and 3D Similarity-Based Filtering. - Dongdong Tang; Yu Mao 001; Weilan Wang; Nan Guan; Tei-Wei Kuo; Chun Jason Xue (DAC, 2025)

Accelerating point cloud analytics on resource-constrained edge devices. - Jingzong Li; Yik Hong Cai; Libin Liu 001; Yu Mao 001; Chun Jason Xue; Hong Xu 001 (Comput. Networks, 2025)

Lossless Compression of Large Language Model-Generated Text via Next-Token Prediction. - Yu Mao 001; Holger Pirk; Chun Jason Xue (CoRR, 2025)