no code implementations • 25 Mar 2014 • Zhenfang Hu, Gang Pan, Yueming Wang, Zhaohui Wu
The methods include PCA, K-means, Laplacian eigenmap (LE), ratio cut (Rcut), and a new sparse representation method developed by us, called spectral sparse representation (SSR).
no code implementations • 20 Jan 2015 • Yuting Zhang, Kui Jia, Yueming Wang, Gang Pan, Tsung-Han Chan, Yi Ma
By assuming a human face as piece-wise planar surfaces, where each surface corresponds to a facial part, we develop in this paper a Constrained Part-based Alignment (CPA) algorithm for face recognition across pose and/or expression.
no code implementations • 6 Mar 2014 • Zhenfang Hu, Gang Pan, Yueming Wang, Zhaohui Wu
In contrast to most of existing work which deal with the problem by adding some sparsity penalties on various objectives of PCA, in this paper, we propose a new method SPCArt, whose motivation is to find a rotation matrix and a sparse basis such that the sparse basis approximates the basis of PCA after the rotation.
no code implementations • 22 Apr 2022 • Yu Qi, Xinyun Zhu, Kedi Xu, Feixiao Ren, Hongjie Jiang, Junming Zhu, Jianmin Zhang, Gang Pan, Yueming Wang
In this way, DyEnsemble copes with variability in signals and improves the robustness of online control.
no code implementations • 17 Apr 2023 • Di Hong, Jiangrong Shen, Yu Qi, Yueming Wang
A conversion scheme is proposed to obtain competitive accuracy by mapping trained ANNs' parameters to SNNs with the same structures.
no code implementations • 6 Jun 2023 • Jiangrong Shen, Qi Xu, Jian K. Liu, Yueming Wang, Gang Pan, Huajin Tang
To take full advantage of low power consumption and improve the efficiency of these models further, the pruning methods have been explored to find sparse SNNs without redundancy connections after training.
no code implementations • 25 Aug 2023 • Hanwen Wang, Yu Qi, Lin Yao, Yueming Wang, Dario Farina, Gang Pan
Then a human-machine joint learning framework is proposed: 1) for the human side, we model the learning process in a sequential trial-and-error scenario and propose a novel ``copy/new'' feedback paradigm to help shape the signal generation of the subject toward the optimal distribution; 2) for the machine side, we propose a novel adaptive learning algorithm to learn an optimal signal distribution along with the subject's learning process.
1 code implementation • NeurIPS 2019 • Yu Qi, Bin Liu, Yueming Wang, Gang Pan
Brain-computer interfaces (BCIs) have enabled prosthetic device control by decoding motor movements from neural activities.
1 code implementation • 27 Sep 2023 • Jiaxuan Chen, Yu Qi, Yueming Wang, Gang Pan
By doing so, we found that the neural representations of the MindGPT are explainable, which can be used to evaluate the contributions of visual properties to language semantics.
1 code implementation • 6 Jun 2023 • Jiaqi Zhai, Zhaojie Gong, Yueming Wang, Xiao Sun, Zheng Yan, Fu Li, Xing Liu
A key component of retrieval is to model (user, item) similarity, which is commonly represented as the dot product of two learned embeddings.
1 code implementation • 27 Feb 2024 • Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Wang, Rui Li, Xuan Cao, Leon Gao, Zhaojie Gong, Fangda Gu, Michael He, Yinghai Lu, Yu Shi
Large-scale recommendation systems are characterized by their reliance on high cardinality, heterogeneous features and the need to handle tens of billions of user actions on a daily basis.
Ranked #1 on Recommendation Systems on Amazon-Book (HR@10 metric)