Obserwuj
Li-Ping Liu
Li-Ping Liu
Tufts University
Zweryfikowany adres z tufts.edu
Tytuł
Cytowane przez
Cytowane przez
Rok
A conditional multinomial mixture model for superset label learning
L Liu, T Dietterich
Advances in neural information processing systems 25, 2012
2722012
Learnability of the superset label learning problem
L Liu, T Dietterich
International conference on machine learning, 1629-1637, 2014
1212014
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
1042022
Gan ensemble for anomaly detection
X Han, X Chen, LP Liu
Proceedings of the AAAI Conference on Artificial Intelligence 35 (5), 4090-4097, 2021
932021
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
812011
Kriging convolutional networks
G Appleby, L Liu, LP Liu
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3187-3194, 2020
762020
Efficient and degree-guided graph generation via discrete diffusion modeling
X Chen, J He, X Han, LP Liu
arXiv preprint arXiv:2305.04111, 2023
572023
Least square incremental linear discriminant analysis
LP Liu, Y Jiang, ZH Zhou
2009 Ninth IEEE International Conference on Data Mining, 298-306, 2009
552009
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
332021
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
202021
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
192021
Context selection for embedding models
L Liu, F Ruiz, S Athey, D Blei
Advances in Neural Information Processing Systems 30, 2017
192017
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
152024
Zero-inflated exponential family embeddings
LP Liu, DM Blei
International Conference on Machine Learning, 2140-2148, 2017
122017
An Ensemble Spectral Prediction (ESP) model for metabolite annotation
X Li, Y Zhou Chen, A Kalia, H Zhu, L Liu, S Hassoun
Bioinformatics 40 (8), btae490, 2024
112024
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