no code implementations • 5 Jan 2024 • Andrew Baumgartner, Sui Huang, Jennifer Hadlock, Cory Funk
Dimensional reduction techniques have long been used to visualize the structure and geometry of high dimensional data.
1 code implementation • 29 Nov 2023 • Karthik Soman, Peter W Rose, John H Morris, Rabia E Akbas, Brett Smith, Braian Peetoom, Catalina Villouta-Reyes, Gabriel Cerono, Yongmei Shi, Angela Rizk-Jackson, Sharat Israni, Charlotte A Nelson, Sui Huang, Sergio E Baranzini
Large Language Models (LLMs) are being adopted at an unprecedented rate, yet still face challenges in knowledge-intensive domains like biomedicine.
no code implementations • 10 Jan 2023 • Yue Wang, Joseph X. Zhou, Edoardo Pedrini, Irit Rubin, May Khalil, Roberto Taramelli, Hong Qian, Sui Huang
Recent studies at individual cell resolution have revealed phenotypic heterogeneity in nominally clonal tumor cell populations.
no code implementations • NeurIPS 2018 • Wenbo Guo, Sui Huang, Yunzhe Tao, Xinyu Xing, Lin Lin
The empirical results indicate that our proposed approach not only outperforms the state-of-the-art techniques in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of the target ML models.
no code implementations • 7 Nov 2018 • Wenbo Guo, Sui Huang, Yunzhe Tao, Xinyu Xing, Lin Lin
The empirical results indicate that our proposed approach not only outperforms the state-of-the-art techniques in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of the target ML models.
no code implementations • 17 Nov 2017 • Tiezheng Ge, Liqin Zhao, Guorui Zhou, Keyu Chen, Shuying Liu, Huimin Yi, Zelin Hu, Bochao Liu, Peng Sun, Haoyu Liu, Pengtao Yi, Sui Huang, Zhiqiang Zhang, Xiaoqiang Zhu, Yu Zhang, Kun Gai
So we propose to model user preference jointly with user behavior ID features and behavior images.
no code implementations • 23 May 2017 • Wenbo Guo, Kaixuan Zhang, Lin Lin, Sui Huang, Xinyu Xing
Our results indicate that the proposed approach not only outperforms the state-of-the-art technique in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of a learning model.