Παρακολούθηση
Li-Ping Liu
Li-Ping Liu
Tufts University
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα tufts.edu
Τίτλος
Παρατίθεται από
Παρατίθεται από
Έτος
A conditional multinomial mixture model for superset label learning
L Liu, T Dietterich
Advances in neural information processing systems 25, 2012
2662012
Learnability of the superset label learning problem
L Liu, T Dietterich
International conference on machine learning, 1629-1637, 2014
1202014
Predicting physics in mesh-reduced space with temporal attention
X Han, H Gao, T Pfaff, JX Wang, LP Liu
arXiv preprint arXiv:2201.09113, 2022
972022
Gan ensemble for anomaly detection
X Han, X Chen, LP Liu
Proceedings of the AAAI Conference on Artificial Intelligence 35 (5), 4090-4097, 2021
872021
Incorporating boosted regression trees into ecological latent variable models
R Hutchinson, LP Liu, T Dietterich
Proceedings of the AAAI Conference on Artificial Intelligence 25 (1), 1343-1348, 2011
772011
Kriging convolutional networks
G Appleby, L Liu, LP Liu
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3187-3194, 2020
682020
Least square incremental linear discriminant analysis
LP Liu, Y Jiang, ZH Zhou
2009 Ninth IEEE International Conference on Data Mining, 298-306, 2009
552009
Efficient and degree-guided graph generation via discrete diffusion modeling
X Chen, J He, X Han, LP Liu
arXiv preprint arXiv:2305.04111, 2023
452023
Order matters: Probabilistic modeling of node sequence for graph generation
X Chen, X Han, J Hu, FJR Ruiz, L Liu
arXiv preprint arXiv:2106.06189, 2021
322021
TEFE: A time-efficient approach to feature extraction
LP Liu, Y Yu, Y Jiang, ZH Zhou
2008 Eighth IEEE International Conference on Data Mining, 423-432, 2008
262008
Nvdiff: Graph generation through the diffusion of node vectors
X Chen, Y Li, A Zhang, L Liu
arXiv preprint arXiv:2211.10794, 2022
212022
Transductive optimization of top k precision
LP Liu, TG Dietterich, N Li, ZH Zhou
arXiv preprint arXiv:1510.05976, 2015
212015
Stochastic iterative graph matching
L Liu, MC Hughes, S Hassoun, L Liu
International Conference on Machine Learning, 6815-6825, 2021
192021
Context selection for embedding models
L Liu, F Ruiz, S Athey, D Blei
Advances in Neural Information Processing Systems 30, 2017
192017
Learning graph representations of biochemical networks and its application to enzymatic link prediction
J Jiang, LP Liu, S Hassoun
Bioinformatics 37 (6), 793-799, 2021
182021
Gaussian approximation of collective graphical models
L Liu, D Sheldon, T Dietterich
International Conference on Machine Learning, 1602-1610, 2014
172014
Using graph neural networks for mass spectrometry prediction
H Zhu, L Liu, S Hassoun
arXiv preprint arXiv:2010.04661, 2020
162020
Bayesian conditional diffusion models for versatile spatiotemporal turbulence generation
H Gao, X Han, X Fan, L Sun, LP Liu, L Duan, JX Wang
Computer Methods in Applied Mechanics and Engineering 427, 117023, 2024
112024
Pathway-activity likelihood analysis and metabolite annotation for untargeted metabolomics using probabilistic modeling
R Hosseini, N Hassanpour, LP Liu, S Hassoun
Metabolites 10 (5), 183, 2020
112020
Amortized variational inference with graph convolutional networks for gaussian processes
L Liu, L Liu
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
112019
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