1 code implementation • NeurIPS 2023 • Andong Wang, Chao Li, Mingyuan Bai, Zhong Jin, Guoxu Zhou, Qibin Zhao
Our analysis indicates that the transformed low-rank parameterization can promisingly enhance robust generalization for t-NNs.
1 code implementation • 18 Oct 2022 • Jie Chen, Shouzhen Chen, Mingyuan Bai, Junbin Gao, Junping Zhang, Jian Pu
Then, we introduce a novel structure-mixing knowledge distillation strategy to enhance the learning ability of MLPs for structure information.
no code implementations • 22 Jul 2021 • Mingyuan Bai, S. T. Boris Choy, Junping Zhang, Junbin Gao
In this paper, we propose a neural ODE model for evolutionary subspace clustering to overcome this limitation and a new objective function with subspace self-expressiveness constraint is introduced.
no code implementations • 28 Apr 2021 • Jie Chen, Shouzhen Chen, Mingyuan Bai, Jian Pu, Junping Zhang, Junbin Gao
In this paper, we consider the label dependency of graph nodes and propose a decoupling attention mechanism to learn both hard and soft attention.
no code implementations • 14 Aug 2019 • Mingyuan Bai, S. T. Boris Choy, Xin Song, Junbin Gao
Thus, we propose a tensor-train parameterization for ultra dimensionality reduction (TTPUDR) in which the traditional LPP mapping is tensorized in terms of tensor-trains and the LPP objective is replaced with the Frobenius norm to increase the robustness of the model.
no code implementations • 1 Aug 2017 • Mingyuan Bai, Boyan Zhang, Junbin Gao
In this paper, we propose a new variant of tensorial neural networks which directly take tensorial time series data as inputs.