Hanxian Huang

Transparent ultrasonic transducer enables compact coaxial photoacoustic microscopy, but the trade-off between optical transparency and sensitivity and electrode nonuniformity results in reduced SNR and resolution. Here, we propose a deep learning-based enhancement framework comprising two task-specific networks for SNR improvement and aberration correction.

ParetoQ: Scaling Laws in Extremely Low-bit LLM Quantization Authors: Zechun Liu , Changsheng Zhao , Hanxian Huang , Sijia Chen , Jing Zhang , Jiawei Zhao , Scott Roy , Lisa Jin , Yunyang Xiong , Yangyang Shi , Lin Xiao , Yuandong Tian , Bilge Soran , Raghuraman Krishnamoorthi , Tijmen Blankevoort , Vikas Chandra Abstract The optimal bit-width for achieving the best trade-off between quantized model size and accuracy has been a subject of ongoing debate.

This paper was presented at the ACM SIGMOD/Principles of Database Systems Conference (opens in new tab) (SIGMOD/PODS 2024), the premier forum on large-scale data management and databases. In today’s fast-paced digital landscape, data analysts are increasingly dependent on analytics dashboards to monitor customer engagement and app performance. However, as data volumes increase, these dashboards can slow down, leading to delays and inefficiencies.