Risk Analysis for Oil and Gas Exploration Project. - Jinlan Liu; Yin Bai; Ying Yu (CIMCA/IAWTIC, 2006)

Fuzzy Influence Diagrams: An Approach to Customer Satisfaction Measurement. - Na An; Jinlan Liu; Yin Bai (FSKD (4), 2007)

Fuzzy Clustering Graph for Graded-Evaluation of Hydrocarbon Reserves Value. - Jinlan Liu; Yuan Li; Xiaojun Zhou (FSKD (4), 2007)

A New Approach to Forecast Crude Oil Price Based on Fuzzy Neural Network. - Jinlan Liu; Yin Bai; Bin Li (FSKD (3), 2007)

Using DEA and Grey Markovian Model to Measure the Goal Cost of Petroleum Enterprise. - Jinlan Liu; Meng-dong Wang; Yin Bai (International Conference on Computational Science (3), 2007)

A Hybrid Immune Genetic Algorithm Approach to Optimize the Integrated Forward/Reverse Logistics Network for 3PLs. - Jinlan Liu; Ni Jian (ICNC (3), 2007)

Split-Bregman Algorithm with Attenuation Correction for L-Shell Polychromatic X-ray Fluorescence Computed Tomography. - Long Liu; Bo Ma; Xiaowei Liu; Jinlan Liu; Ning Ding (J. Medical Imaging Health Informatics, 2020)

Decreasing scaling transition from adaptive gradient descent to stochastic gradient descent. - Kun Zeng; Jinlan Liu; Zhixia Jiang; Dongpo Xu (CoRR, 2021)

Scaling transition from momentum stochastic gradient descent to plain stochastic gradient descent. - Kun Zeng; Jinlan Liu; Zhixia Jiang; Dongpo Xu (CoRR, 2021)

SGD-rα: A real-time α-suffix averaging method for SGD with biased gradient estimates. - Jianqi Luo; Jinlan Liu; Dongpo Xu; Huisheng Zhang (Neurocomputing, 2022)

Convergence analysis of AdaBound with relaxed bound functions for non-convex optimization. - Jinlan Liu; Jun Kong; Dongpo Xu; Miao Qi; Yinghua Lu (Neural Networks, 2022)

UAdam: Unified Adam-Type Algorithmic Framework for Non-Convex Stochastic Optimization. - Yiming Jiang 013; Jinlan Liu; Dongpo Xu; Danilo P. Mandic (CoRR, 2023)

Last-iterate convergence analysis of stochastic momentum methods for neural networks. - Jinlan Liu; Dongpo Xu; Yinghua Lu; Jun Kong; Danilo P. Mandic (Neurocomputing, 2023)

Stochastic momentum methods for non-convex learning without bounded assumptions. - Yuqing Liang; Jinlan Liu; Dongpo Xu (Neural Networks, 2023)

Optimization Algorithm for Emission Reduction Schemes Based on Carbon Footprint Prediction. - Hongliang Sun; Feifei Wang; Meng Wang; Jinlan Liu; Qiao Guan (ICSS, 2024)

Learning Dynamic Knowledge Graph Embedding in Evolving Service Ecosystems via Meta-Learning. - Hongliang Sun; Jinlan Liu; Can Wang; Dianbo Sui; Zhiying Tu; Xiaofei Xu (ICWS, 2024)

ColorMesh: Surface and texture reconstruction of large-scale scenes from unstructured colorful point clouds with adaptive automatic viewpoint selection. - Mubai Li; Zhenxin Zhang; Siyun Chen; Liqiang Zhang; Zhihua Xu; Xiaoxu Ren; Jinlan Liu; Peng Sun (Int. J. Appl. Earth Obs. Geoinformation, 2024)

UAdam: Unified Adam-Type Algorithmic Framework for Nonconvex Optimization. - Yiming Jiang 013; Jinlan Liu; Dongpo Xu; Danilo P. Mandic (Neural Comput., 2024)

Shuffling-type gradient method with bandwidth-based step sizes for finite-sum optimization. - Yuqing Liang; Yang Yang; Jinlan Liu; Dongpo Xu (Neural Networks, 2024)

An Iterative Algorithm for Quaternion Eigenvalue Problems in Signal Processing. - Qiankun Diao; Jinlan Liu; Naimin Zhang; Dongpo Xu (IEEE Signal Process. Lett., 2024)

mPage: Probabilistic Gradient Estimator With Momentum for Non-Convex Optimization. - Yuqing Liang; Hui Su; Jinlan Liu; Dongpo Xu (IEEE Trans. Signal Process., 2024)

DMAdam: Dual averaging enhanced adaptive gradient method for deep neural networks. - Wenhan Jiang; Jinlan Liu; Naimin Zhang; Dongpo Xu (Knowl. Based Syst., 2025)

Sharpness-Aware Minimization method with momentum acceleration for deep neural networks. - Helei Kang; Yiming Jiang 013; Jinlan Liu; Dongpo Xu (Knowl. Based Syst., 2025)

AdaVAM: Adaptive variance-aware momentum for accelerating deep neural network training. - Jinlan Liu; Wenhan Jiang; Xin Deng; Dongpo Xu (Knowl. Based Syst., 2025)

On the Convergence of Decentralized Stochastic Gradient Descent With Biased Gradients. - Yiming Jiang 013; Helei Kang; Jinlan Liu; Dongpo Xu (IEEE Trans. Signal Process., 2025)

Convergence of Adam for non-convex objectives: relaxed hyperparameters and non-ergodic case. - Yuqing Liang; Meixuan He; Jinlan Liu; Dongpo Xu (Mach. Learn., 2025)

A robust stochastic quasi-Newton algorithm for non-convex machine learning. - Hanger Liu; Yuqing Liang; Jinlan Liu; Dongpo Xu (Appl. Intell., 2025)