Liping Liu - RankMe

23 papers A* 8A 2C 2Journal 10Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	C	`conf`	Act-to-Ground: A Framework for Symbol Grounding in Planning Domains. NeSy Panagiotis Lymperopoulos, Liping Liu
2025	A*	`conf`	Graph Generative Pre-trained Transformer. ICML Xiaohui Chen, Yinkai Wang, Jiaxing He, Yuanqi Du, Soha Hassoun, Xiaolin Xu, Liping Liu
2025	J	`jnl`	Large Language Model is Secretly a Protein Sequence Optimizer. CoRR Yinkai Wang, Jiaxing He, Yuanqi Du, Xiaohui Chen, Jianan Canal Li, Liping Liu, Xiaolin Xu, Soha Hassoun
2024	J	`jnl`	An Ensemble Spectral Prediction (ESP) model for metabolite annotation. Bioinform. Xinmeng Li, Yan Zhou Chen, Apurva Kalia, Hao Zhu, Liping Liu, Soha Hassoun
2024	A	`conf`	Graph Pruning for Enumeration of Minimal Unsatisfiable Subsets. AISTATS Panagiotis Lymperopoulos, Liping Liu
2024	J	`jnl`	Graph Pruning for Enumeration of Minimal Unsatisfiable Subsets. CoRR Panagiotis Lymperopoulos, Liping Liu
2024	A*	`conf`	MassSpecGym: A benchmark for the discovery and identification of molecules. NeurIPS 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, Liping 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`	Development of a Deep Learning System for Intra-Operative Identification of Cancer Metastases. CoRR Thomas Schnelldorfer, Janil Castro, Atoussa Goldar-Najafi, Liping Liu
2023	J	`jnl`	EDGE++: Improved Training and Sampling of EDGE. CoRR Mingyang Wu, Xiaohui Chen, Liping Liu
2023	A*	`conf`	Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling. ICML Xiaohui Chen, Jiaxing He, Xu Han, Liping Liu
2023	J	`jnl`	NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds. Trans. Mach. Learn. Res. Patrick Feeney, Sarah Schneider, Panagiotis Lymperopoulos, Liping Liu, Matthias Scheutz, Michael C. Hughes
2023	A*	`conf`	On Separate Normalization in Self-supervised Transformers. NeurIPS Xiaohui Chen, Yinkai Wang, Yuanqi Du, Soha Hassoun, Liping Liu
2023	A*	`conf`	Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model. NeurIPS Luning Sun, Xu Han, Han Gao, Jian-Xun Wang, Liping Liu
2022	J	`jnl`	Interpretable Node Representation with Attribute Decoding. CoRR Xiaohui Chen, Xi Chen, Liping Liu
2022	J	`jnl`	NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds. CoRR Patrick Feeney, Sarah Schneider, Panagiotis Lymperopoulos, Liping Liu, Matthias Scheutz, Michael C. Hughes
2022	—	`conf`	PatchGT: Transformer Over Non-Trainable Clusters for Learning Graph Representations. LoG Han Gao, Xu Han, Jiaoyang Huang, Jian-Xun Wang, Liping Liu
2022	A*	`conf`	Predicting Physics in Mesh-reduced Space with Temporal Attention. ICLR Xu Han, Han Gao, Tobias Pfaff, Jian-Xun Wang, Liping Liu
2021	J	`jnl`	Modeling Graph Node Correlations with Neighbor Mixture Models. CoRR Linfeng Liu, Michael C. Hughes, Liping Liu
2021	A*	`conf`	Stochastic Iterative Graph Matching. ICML Linfeng Liu, Michael C. Hughes, Soha Hassoun, Liping Liu
2020	A*	`conf`	Kriging Convolutional Networks. AAAI Gabriel Appleby, Linfeng Liu, Liping Liu
2020	C	`conf`	Localizing and Amortizing: Efficient Inference for Gaussian Processes. ACML Linfeng Liu, Liping Liu
2019	A	`conf`	Amortized Variational Inference with Graph Convolutional Networks for Gaussian Processes. AISTATS Linfeng Liu, Liping Liu
2018	J	`jnl`	Non-Parametric Variational Inference with Graph Convolutional Networks for Gaussian Processes. CoRR Linfeng Liu, Liping Liu