no code implementations • 15 Nov 2023 • Yikun Wang, Rui Zheng, Haoming Li, Qi Zhang, Tao Gui, Fei Liu
Our approach, named RRescue, suggests a promising avenue for enhancing LLMs' contextual understanding via response ranking.
no code implementations • 14 Jun 2023 • Haoming Li, Yu Xiang, Haodong Xu, Wenyong Wang
As a hot topic in recent years, the ability of pedestrians identification based on radar micro-Doppler signatures is limited by the lack of adequate training data.
1 code implementation • 27 Apr 2023 • Burak Bartan, Haoming Li, Harris Teague, Christopher Lott, Bistra Dilkina
The deployment and training of neural networks on edge computing devices pose many challenges.
no code implementations • 17 Oct 2022 • Haoming Li, Xinzhuo Lin, Yang Zhou, Xiang Li, Yuchi Huo, Jiming Chen, Qi Ye
To tackle the challenge, we introduce an intermediate variable for grasp contact areas to constrain the grasp generation; in other words, we factorize the mapping into two sequential stages by assuming that grasping poses are fully constrained given contact maps: 1) we first learn contact map distributions to generate the potential contact maps for grasps; 2) then learn a mapping from the contact maps to the grasping poses.
no code implementations • 14 Apr 2022 • Jiamin Liang, Xin Yang, Yuhao Huang, Haoming Li, Shuangchi He, Xindi Hu, Zejian Chen, Wufeng Xue, Jun Cheng, Dong Ni
Our main contributions include: 1) we present the first work that can synthesize realistic B-mode US images with high-resolution and customized texture editing features; 2) to enhance structural details of generated images, we propose to introduce auxiliary sketch guidance into a conditional GAN.
1 code implementation • 13 Aug 2021 • Haoming Li, Feiyang Pan, Xiang Ao, Zhao Yang, Min Lu, Junwei Pan, Dapeng Liu, Lei Xiao, Qing He
The delayed feedback problem is one of the imperative challenges in online advertising, which is caused by the highly diversified feedback delay of a conversion varying from a few minutes to several days.
no code implementations • 18 Jul 2021 • Feiyang Pan, Haoming Li, Xiang Ao, Wei Wang, Yanrong Kang, Ao Tan, Qing He
The proposed method is efficient as it can make decisions on-the-fly by utilizing only one randomly chosen model, but is also effective as we show that it can be viewed as a non-Bayesian approximation of Thompson sampling.
1 code implementation • 18 Dec 2020 • Anilesh K. Krishnaswamy, Haoming Li, David Rein, Hanrui Zhang, Vincent Conitzer
To this end, we present {\sc IC-LR}, a modification of Logistic Regression that removes the incentive to strategically drop features.
no code implementations • 10 Oct 2020 • Haoming Li, Xin Yang, Jiamin Liang, Wenlong Shi, Chaoyu Chen, Haoran Dou, Rui Li, Rui Gao, Guangquan Zhou, Jinghui Fang, Xiaowen Liang, Ruobing Huang, Alejandro Frangi, Zhiyi Chen, Dong Ni
However, the lack of sharp boundaries in US images still remains an inherent challenge for segmentation.
no code implementations • 1 Apr 2020 • Jiamin Liang, Xin Yang, Haoming Li, Yi Wang, Manh The Van, Haoran Dou, Chaoyu Chen, Jinghui Fang, Xiaowen Liang, Zixin Mai, Guowen Zhu, Zhiyi Chen, Dong Ni
Efficiently synthesizing realistic, editable and high resolution US images can solve the problems.
1 code implementation • 27 May 2019 • Haoming Li, Sujoy Sikdar, Rohit Vaish, Junming Wang, Lirong Xia, Chaonan Ye
Consider the following problem faced by an online voting platform: A user is provided with a list of alternatives, and is asked to rank them in order of preference using only drag-and-drop operations.
1 code implementation • 14 May 2018 • Zhibing Zhao, Haoming Li, Junming Wang, Jeffrey Kephart, Nicholas Mattei, Hui Su, Lirong Xia
We propose a cost-effective framework for preference elicitation and aggregation under the Plackett-Luce model with features.