no code implementations • 11 Mar 2024 • Jinghan Huang, Qiufeng Chen, Yijun Bian, Pengli Zhu, Nanguang Chen, Moo K. Chung, Anqi Qiu
Additionally, we propose a pooling operator to coarsen $k$-simplices, combining features through simplicial attention mechanisms of self-attention and cross-attention via transformers and SP operators, capturing topological interconnections across multiple dimensions of simplices.
no code implementations • 25 Jan 2023 • Yijun Bian, Kun Zhang, Anqi Qiu, Nanguang Chen
Furthermore, we investigate the properties of the proposed measure and propose first- and second-order oracle bounds to show that fairness can be boosted via ensemble combination with theoretical learning guarantees.
no code implementations • 16 Jun 2022 • Abhijith Sharma, Yijun Bian, Phil Munz, Apurva Narayan
Adversarial attacks in deep learning models, especially for safety-critical systems, are gaining more and more attention in recent years, due to the lack of trust in the security and robustness of AI models.
no code implementations • 30 Oct 2019 • Yijun Bian, Huanhuan Chen
To reveal the effect of diversity on the generalization of classification ensembles, we investigate three issues on diversity, i. e., the measurement of diversity, the relationship between the proposed diversity and the generalization error, and the utilization of this relationship for ensemble pruning.
1 code implementation • 1 Oct 2019 • Yijun Bian, Qingquan Song, Mengnan Du, Jun Yao, Huanhuan Chen, Xia Hu
Neural architecture search (NAS) is gaining more and more attention in recent years due to its flexibility and remarkable capability to reduce the burden of neural network design.
1 code implementation • 13 Jun 2018 • Yijun Bian, Yijun Wang, Yaqiang Yao, Huanhuan Chen
Ensemble pruning, selecting a subset of individual learners from an original ensemble, alleviates the deficiencies of ensemble learning on the cost of time and space.