Search Results for author: Xiaomin Zhu

Found 8 papers, 0 papers with code

A New Random Forest Ensemble of Intuitionistic Fuzzy Decision Trees

no code implementations12 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).

Classification Ensemble Learning +2

Tree-Based Hard Attention with Self-Motivation for Large Language Models

no code implementations14 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.

Hard Attention

GENET: Unleashing the Power of Side Information for Recommendation via Hypergraph Pre-training

no code implementations22 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.

Sequential Recommendation

An Automatic Design Framework of Swarm Pattern Formation based on Multi-objective Genetic Programming

no code implementations31 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.

Private Model Compression via Knowledge Distillation

no code implementations13 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.

Knowledge Distillation Model Compression +1

Deep Learning Towards Mobile Applications

no code implementations10 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.

BIG-bench Machine Learning

Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud

no code implementations10 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.

Privacy Preserving

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