Li-Ping Liu - RankMe

41 papers A* 9A 1Journal 28Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Graph Generative Pre-trained Transformer. CoRR Xiaohui Chen, Yinkai Wang, Jiaxing He, Yuanqi Du, Soha Hassoun, Xiaolin Xu, Li-Ping Liu
2024	J	`jnl`	A neurosymbolic cognitive architecture framework for handling novelties in open worlds. Artif. Intell. Shivam Goel, Panagiotis Lymperopoulos, Ravenna Thielstrom, Evan A. Krause, Patrick Feeney, Pierrick Lorang, Sarah Schneider, Yichen Wei, Eric J. Kildebeck, Stephen A. Goss, Michael C. Hughes, Li-Ping Liu, Jivko Sinapov, Matthias Scheutz
2024	J	`jnl`	MassSpecGym: A benchmark for the discovery and identification of molecules. CoRR Roman Bushuiev, Anton Bushuiev, Niek F. de Jonge, Adamo Young, Fleming Kretschmer, Raman Samusevich, Janne Heirman, Fei Wang, Luke Zhang, Kai Dührkop, Marcus Ludwig, Nils A. Haupt, Apurva Kalia, Corinna Brungs, Robin Schmid, Russell Greiner, Bo Wang, David S. Wishart, Li-Ping Liu, Juho Rousu, Wout Bittremieux, Hannes Rost, Tytus D. Mak, Soha Hassoun, Florian Huber, Justin J. J. van der Hooft, Michael A. Stravs, Sebastian Böcker, Josef Sivic, Tomás Pluskal
2023	J	`jnl`	Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation. CoRR Han Gao, Xu Han, Xiantao Fan, Luning Sun, Li-Ping Liu, Lian Duan, Jian-Xun Wang
2023	J	`jnl`	Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling. CoRR Xiaohui Chen, Jiaxing He, Xu Han, Li-Ping Liu
2023	J	`jnl`	Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation. J. Mach. Learn. Res. Xu Han, Xiaohui Chen, Francisco J. R. Ruiz, Li-Ping Liu
2023	A*	`conf`	Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems. KDD Xiaohui Chen, Jiankai Sun, Taiqing Wang, Ruocheng Guo, Li-Ping Liu, Aonan Zhang
2023	J	`jnl`	Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems. CoRR Xiaohui Chen, Jiankai Sun, Taiqing Wang, Ruocheng Guo, Li-Ping Liu, Aonan Zhang
2023	J	`jnl`	Kriging Convolutional Networks. CoRR Gabriel Appleby, Linfeng Liu, Li-Ping Liu
2023	J	`jnl`	On Separate Normalization in Self-supervised Transformers. CoRR Xiaohui Chen, Yinkai Wang, Yuanqi Du, Soha Hassoun, Li-Ping Liu
2023	J	`jnl`	Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image Synthesis. CoRR Xiaohui Chen, Yongfei Liu, Yingxiang Yang, Jianbo Yuan, Quanzeng You, Li-Ping Liu, Hongxia Yang
2023	J	`jnl`	Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products. Bioinform. Vladimir Porokhin, Li-Ping Liu, Soha Hassoun
2022	J	`jnl`	Boost-RS: boosted embeddings for recommender systems and its application to enzyme-substrate interaction prediction. Bioinform. Xinmeng Li, Li-Ping Liu, Soha Hassoun
2022	J	`jnl`	Ensemble Spectral Prediction (ESP) Model for Metabolite Annotation. CoRR Xinmeng Li, Hao Zhu, Li-Ping Liu, Soha Hassoun
2022	J	`jnl`	Interpretable Node Representation with Attribute Decoding. Trans. Mach. Learn. Res. Xiaohui Chen, Xi Chen, Li-Ping Liu
2022	J	`jnl`	NVDiff: Graph Generation through the Diffusion of Node Vectors. CoRR Xiaohui Chen, Yukun Li, Aonan Zhang, Li-Ping Liu
2022	J	`jnl`	PatchGT: Transformer over Non-trainable Clusters for Learning Graph Representations. CoRR Han Gao, Xu Han, Jiaoyang Huang, Jian-Xun Wang, Li-Ping Liu
2022	J	`jnl`	Predicting Physics in Mesh-reduced Space with Temporal Attention. CoRR Xu Han, Han Gao, Tobias Pfaff, Jian-Xun Wang, Li-Ping Liu
2022	J	`jnl`	Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning. CoRR Linfeng Liu, Xu Han, Dawei Zhou, Li-Ping Liu
2022	J	`jnl`	Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning. Trans. Mach. Learn. Res. Linfeng Liu, Xu Han, Dawei Zhou, Li-Ping Liu
2021	J	`jnl`	Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction Prediction. CoRR Xinmeng Li, Li-Ping Liu, Soha Hassoun
2021	A*	`conf`	GAN Ensemble for Anomaly Detection. AAAI Xu Han, Xiaohui Chen, Li-Ping Liu
2021	J	`jnl`	Learning graph representations of biochemical networks and its application to enzymatic link prediction. Bioinform. Julie Jiang, Li-Ping Liu, Soha Hassoun
2021	A*	`conf`	Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation. ICML Xiaohui Chen, Xu Han, Jiajing Hu, Francisco J. R. Ruiz, Li-Ping Liu
2021	J	`jnl`	Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation. CoRR Xiaohui Chen, Xu Han, Jiajing Hu, Francisco J. R. Ruiz, Li-Ping Liu
2021	J	`jnl`	Stochastic Iterative Graph Matching. CoRR Linfeng Liu, Michael C. Hughes, Soha Hassoun, Li-Ping Liu
2020	J	`jnl`	GAN Ensemble for Anomaly Detection. CoRR Xu Han, Xiaohui Chen, Li-Ping Liu
2020	J	`jnl`	Using Graph Neural Networks for Mass Spectrometry Prediction. CoRR Hao Zhu, Li-Ping Liu, Soha Hassoun
2019	J	`jnl`	Pathway Activity Analysis and Metabolite Annotation for Untargeted Metabolomics using Probabilistic Modeling. CoRR Ramtin Hosseini, Neda Hassanpour, Li-Ping Liu, Soha Hassoun
2017	—	`conf`	Context Selection for Embedding Models. NIPS Li-Ping Liu, Francisco J. R. Ruiz, Susan Athey, David M. Blei
2016	A*	`conf`	Transductive Optimization of Top k Precision. IJCAI Li-Ping Liu, Thomas G. Dietterich, Nan Li, Zhi-Hua Zhou
2015	—	`conf`	Bayesian Active Clustering with Pairwise Constraints. ECML/PKDD (1) Yuanli Pei, Li-Ping Liu, Xiaoli Z. Fern
2015	J	`jnl`	Transductive Optimization of Top k Precision. CoRR Li-Ping Liu, Thomas G. Dietterich, Nan Li, Zhi-Hua Zhou
2014	A*	`conf`	Gaussian Approximation of Collective Graphical Models. ICML Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich
2014	J	`jnl`	Gaussian Approximation of Collective Graphical Models. CoRR Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich
2014	A*	`conf`	Learnability of the Superset Label Learning Problem. ICML Li-Ping Liu, Thomas G. Dietterich
2012	—	`conf`	A Conditional Multinomial Mixture Model for Superset Label Learning. NIPS Li-Ping Liu, Thomas G. Dietterich
2012	A	`conf`	Constructing Training Sets for Outlier Detection. SDM Li-Ping Liu, Xiaoli Z. Fern
2011	A*	`conf`	Incorporating Boosted Regression Trees into Ecological Latent Variable Models. AAAI Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Dietterich
2009	A*	`conf`	Least Square Incremental Linear Discriminant Analysis. ICDM Li-Ping Liu, Yuan Jiang, Zhi-Hua Zhou
2008	A*	`conf`	TEFE: A Time-Efficient Approach to Feature Extraction. ICDM Li-Ping Liu, Yang Yu, Yuan Jiang, Zhi-Hua Zhou