Author name cluster

Jianhui Lin

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

NeurIPS Conference 2025 Conference Paper

Tabula: A Tabular Self-Supervised Foundation Model for Single-Cell Transcriptomics

Jiayuan Ding
Jianhui Lin
Shiyu Jiang
Yixin Wang
Ziyang Miao
Zhaoyu Fang
Jiliang Tang
Min Li

Foundation models (FMs) have shown great promise in single-cell genomics, yet current approaches, such as scGPT, Geneformer, and scFoundation, rely on centralized training and language modeling objectives that overlook the tabular nature of single-cell data and raise significant privacy concerns. We present TABULA, a foundation model designed for single-cell transcriptomics, which integrates a novel tabular modeling objective and federated learning framework to enable privacy-preserving pretraining across decentralized datasets. TABULA directly models the cell-by-gene expression matrix through column-wise gene reconstruction and row-wise cell contrastive learning, capturing both gene-level relationships and cell-level heterogeneity without imposing artificial gene sequence order. Extensive experiments demonstrate the effectiveness of TABULA: despite using only half the pretraining data, TABULA achieves state-of-the-art performance across key tasks, including gene imputation, perturbation prediction, cell type annotation, and multi-omics integration. It is important to note that as public single-cell datasets continue to grow, TABULA provides a scalable and privacy-aware foundation that not only validates the feasibility of federated tabular modeling but also establishes a generalizable framework for training future models under similar privacy-preserving settings.

PDF Details

EAAI Journal 2024 Journal Article

A parallel ensemble optimization and transfer learning based intelligent fault diagnosis framework for bearings

Guiting Tang
Cai Yi
Lei Liu
Du Xu
Qiuyang Zhou
Yongxu Hu
Pengcheng Zhou
Jianhui Lin

Transfer learning (TL) is an important method to accurately identify the bearing health status in cross-domain and ensure the safe operation of machinery. With the advancement in research, it will become a trend to choose different neural networks or optimization functions to improve and re-model fault diagnosis methods. However, the variants of these fault diagnostic methods are less capable of generalizing input dimensions and do not significantly increase demand for machinery expertise. The idea of ensemble learning solves the problem of low generalization. In this research, a parallel ensemble optimization loss function and multi-source TL based model are proposed to solve the problem of unknown distribution difference between source domain and target domain, thus improving the generalization of optimization objectives. Firstly, based on the signal demodulation method, an adaptive input module is constructed to automatically select the input length from the original vibration signal. Secondly, a TL network with low-dimensional features reuse is constructed to achieve weight and bias sharing. Thirdly, a parallel ensemble optimization loss function is developed to align the data whose distribution is unknown between source and target domains. Finally, two cases with multi-source, unsupervised, and cross-domain TL are used to verify the performance of the proposed method. The average accuracy in case 1 and case 2 is 99. 81 % and 99. 17 % respectively. It is proved that the proposed method can not only get rid of the limitation of manual input length setting, but also overcome the limitation of optimization function, which is more effective than the existing intelligent fault diagnosis models.

Details DOI

EAAI Journal 2023 Journal Article

A novel transfer learning network with adaptive input length selection and lightweight structure for bearing fault diagnosis

Guiting Tang
Cai Yi
Lei Liu
Xingguo Yang
Du Xu
Qiuyang Zhou
Jianhui Lin

In recent years, great progress has been made in intelligent bearing fault diagnosis based on transfer learning (TL). However, the huge number of parameters is ignored when using large convolutional neural network (CNN), and the input length of different bearings are almost not take into account. The high-energy hardware economic cost and time consumption caused by slow operation of large CNN have brought great difficulties to the engineering practice. Therefore, inspired by envelope demodulation and lightweight network signal processing methods, a novel lightweight TL network is proposed, which can adaptively select the input length (IL) and accurately identify the bearing health states under different work conditions. Firstly, an innovative adaptive IL selection strategy considering bearing differences is proposed to replace manually fixed IL. Secondly, a TL network containing group convolution and instance normalization is constructed to make the network lightweight and operate faster. Thirdly, maximum mean discrepancy is introduced to align the feature distribution between source domain and target domain. Lastly, 81 tasks are carried out on the across-domain datasets to validate the practicability of the proposed method. The results between accuracy and lightweight demonstrate that the proposed method is superior to other four state-of-the-art TL CNN, including three TL CNN and a lightweight model, under identical conditions.

Details DOI

EAAI Journal 2023 Journal Article

Unsupervised transfer learning for intelligent health status identification of bearing in adaptive input length selection

Guiting Tang
Lei Liu
Yirong Liu
Cai Yi
Yongxu Hu
Du Xu
Qiuyang Zhou
Jianhui Lin

Input length (IL) is an important element in transfer learning (TL) network for intelligent health status identification of bearing (IHSIB). However, fixed IL are used in most studies. In this paper, a TL network via adaptive IL selection module for IHSIB (AILTLN) is proposed, which includes adaptive IL module, feature extractor module, health status identification module, and domain discriminator module. Firstly, an adaptive IL selection module based on envelope spectrum analysis is proposed. The module varies with bearing structure, motor speed, and sampling frequency. Secondly, group convolution, transposed convolution, and instant normalization are constructed in feature extractor. Thirdly, softmax cross-entropy loss function and maximum mean discrepancy are used for health status identification and domain alignment. The TL results of open bearing dataset and high-speed train bearing experiment show that AILTLN is better than the other existing methods in the TL of IHSIB. The ablation study shows the reuse of low-dimensional features and the adaptive IL help to improve to the accuracy of the proposed method.

Details DOI