no code implementations • 12 Mar 2024 • Yingtao Ren, Xiaomin Zhu, Kaiyuan Bai, Runtong Zhang
In this paper, we propose the intuitionistic fuzzy random forest (IFRF), a new random forest ensemble of intuitionistic fuzzy decision trees (IFDT).
no code implementations • 14 Feb 2024 • Chenxi Lin, Jiayu Ren, Guoxiu He, Zhuoren Jiang, Haiyan Yu, Xiaomin Zhu
Moreover, TEAROOM comprises a self-motivation strategy for another LLM equipped with a trainable adapter and a linear layer.
no code implementations • 22 Nov 2023 • Yang Li, Qi'ao Zhao, Chen Lin, Zhenjie Zhang, Xiaomin Zhu
(2) The diverse semantics of side information that describes items and users from multi-level in a context different from recommendation systems.
no code implementations • 31 Oct 2019 • Zhun Fan, Zhaojun Wang, Xiaomin Zhu, Bingliang Hu, Anmin Zou, Dongwei Bao
Most existing swarm pattern formation methods depend on a predefined gene regulatory network (GRN) structure that requires designers' priori knowledge, which is difficult to adapt to complex and changeable environments.
no code implementations • 13 Nov 2018 • Ji Wang, Weidong Bao, Lichao Sun, Xiaomin Zhu, Bokai Cao, Philip S. Yu
To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA.
no code implementations • 10 Sep 2018 • Ji Wang, Jian-Guo Zhang, Weidong Bao, Xiaomin Zhu, Bokai Cao, Philip S. Yu
To benefit from the cloud data center without the privacy risk, we design, evaluate, and implement a cloud-based framework ARDEN which partitions the DNN across mobile devices and cloud data centers.
no code implementations • 10 Sep 2018 • Ji Wang, Bokai Cao, Philip S. Yu, Lichao Sun, Weidong Bao, Xiaomin Zhu
In this paper, we provide an overview of the current challenges and representative achievements about pushing deep learning on mobile devices from three aspects: training with mobile data, efficient inference on mobile devices, and applications of mobile deep learning.