no code implementations • 1 Nov 2023 • Zhihong Zeng, Zongji Wang, Yuanben Zhang, Weinan Cai, Zehao Cao, Lili Zhang, Yan Guo, Yanhong Zhang, Junyi Liu
To obtain illuminating conditions from the mixture soup, the system successfully separates the interaction between objects and scene environment by intrinsic decomposition method.
1 code implementation • 31 Oct 2023 • Junyi Liu
The recommendation model known as LFM (Latent Factor Model), which captures latent features through matrix factorization and gradient descent to fit user preferences, has given rise to various recommendation algorithms that bring new improvements in recommendation accuracy.
no code implementations • 24 Oct 2023 • Junyi Liu, Liangzhi Li, Tong Xiang, Bowen Wang, Yiming Qian
Our summarization compression can reduce 65% of the retrieval token size with further 0. 3% improvement on the accuracy; semantic compression provides a more flexible way to trade-off the token size with performance, for which we can reduce the token size by 20% with only 1. 6% of accuracy drop.
no code implementations • 17 Aug 2023 • Song Zhang, Wenjia Xu, Zhiwei Wei, Lili Zhang, Yang Wang, Junyi Liu
Moreover, our method also achieves the lowest $e_{1}$ and $e_{3}$ on the BlendedMVS dataset and the highest Acc and $F_{1}$-score on the ETH 3D dataset, surpassing all listed methods. Project website: https://github. com/zs670980918/ARAI-MVSNet
no code implementations • 26 Apr 2023 • Yiyang Zhang, Junyi Liu, Xiaobo Zhao
Focusing on stochastic programming (SP) with covariate information, this paper proposes an empirical risk minimization (ERM) method embedded within a nonconvex piecewise affine decision rule (PADR), which aims to learn the direct mapping from features to optimal decisions.
no code implementations • 1 Sep 2022 • Junyi Liu, Yifu Tang, Haimeng Zhao, Xieheng Wang, Fangyu Li, Jingyi Zhang
In order to train a global multi-class classifier without sharing the raw data across all nodes, the main result of our study is designing a multi-node multi-class classification ensemble approach.
no code implementations • 21 Oct 2019 • Yiren Zhao, Xitong Gao, Xuan Guo, Junyi Liu, Erwei Wang, Robert Mullins, Peter Y. K. Cheung, George Constantinides, Cheng-Zhong Xu
Furthermore, we show how Tomato produces implementations of networks with various sizes running on single or multiple FPGAs.
1 code implementation • 8 Jan 2019 • Qisheng Wang, Junyi Liu, Mingsheng Ying
In this paper, we introduce the model of quantum Mealy machines and study the equivalence checking and minimisation problems of them.
Formal Languages and Automata Theory Quantum Physics