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Feng Liu (he/him)
Feng Liu (he/him)
Lecturer, University of Melbourne / Visiting Scientist, RIKEN-AIP / Visiting Fellow, UTS-AAII
Email confirmado em unimelb.edu.au - Página inicial
Título
Citado por
Citado por
Ano
A multi-step wind-speed forecasting model based on WRF ensembles and fuzzy systems
J Zhao, ZH Guo, ZY Su, ZY Zhao, X Xiao, F Liu*
Applied Energy 162, 808-826, 2016
292*2016
Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models
J Wang, Y Song*, F Liu, R Hou
Renewable and Sustainable Energy Reviews 60, 960-981, 2016
2762016
Learning Deep Kernels for Non-Parametric Two-Sample Tests
F Liu, W Xu, J Lu, G Zhang, A Gretton, DJ Sutherland
ICML 2020, 2020
2222020
Open Set Domain Adaptation: Theoretical Bound and Algorithm
Z Fang, J Lu*, F Liu, J Xuan, G Zhang
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021
2092021
Heterogeneous domain adaptation: An unsupervised approach
F Liu, G Zhang, J Lu*
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2020
1722020
Is Out-of-Distribution Detection Learnable?
Z Fang, Y Li, J Lu, J Dong, B Han, F Liu
NeurIPS 2022 (oral, outstanding paper award), 2022
1512022
A cross-domain recommender system with consistent information transfer
Q Zhang, D Wu, J Lu*, F Liu, G Zhang
Decision Support Systems (DSS) 104, 49-63, 2017
1392017
Accumulating regional density dissimilarity for concept drift detection in data streams
A Liu, J Lu*, F Liu, G Zhang
Pattern Recognition (PR) 76, 256-272, 2018
1312018
A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting
P Jiang, F Liu*, Y Song
Energy 119, 694-709, 2017
1292017
Does deep learning help topic extraction? A kernel k-means clustering method with word embedding
Y Zhang, J Lu, F Liu, Q Liu, A Porter, H Chen*, G Zhang
Journal of Informetrics (JOI) 12 (4), 1099-1117, 2018
1272018
Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models
S Qin, F Liu*, J Wang, B Sun
Atmospheric environment 98, 665-675, 2014
1162014
Multisource Heterogeneous Unsupervised Domain Adaptation via Fuzzy Relation Neural Networks
F Liu, G Zhang, J Lu*
IEEE Transactions on Fuzzy Systems (TFS), 2021
1132021
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation
L Zhong, Z Fang, F Liu, B Yuan, G Zhang, J Lu*
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021
1042021
A dynamic-choice neural network for electricity price forecasting
J Wang, F Liu*, Y Song, J Zhao
Applied Soft Computing 48, 281-297, 2016
102*2016
Development of a hybrid model to predict construction and demolition waste: China as a case study
Y Song, Y Wang*, F Liu, Y Zhang
Waste management 59, 350-361, 2017
1002017
Learning from a Complementary-label Source Domain: Theory and Algorithms
Y Zhang, F Liu, Z Fang, B Yuan, G Zhang, J Lu*
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021
962021
The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region
Y Song, S Qin*, J Qu, F Liu
Atmospheric Environment 118, 58-69, 2015
932015
Maximum mean discrepancy test is aware of adversarial attacks
R Gao, F Liu, J Zhang, B Han, T Liu, G Niu, M Sugiyama
ICML 2021, 2021
81*2021
Semi-supervised Heterogeneous Domain Adaptation: Theory and Algorithms
Z Fang, J Lu, F Liu, G Zhang
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022
762022
Interval forecasts of a novel hybrid model for wind speeds
S Qin, F Liu*, J Wang, Y Song
Energy Reports 1, 8-16, 2015
75*2015
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Artigos 1–20