Liping Liu

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products.

[BibT_eX]

[DOI]

Vladimir Porokhin

Bioinform., March, 2023

NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds.

[BibT_eX]

[DOI]

Patrick Feeney

Sarah Schneider

Panagiotis Lymperopoulos

Matthias Scheutz

Michael C. Hughes

Trans. Mach. Learn. Res., 2023

Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2023

Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image Synthesis.

[BibT_eX]

[DOI]

CoRR, 2023

Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation.

[BibT_eX]

[DOI]

CoRR, 2023

EDGE++: Improved Training and Sampling of EDGE.

[BibT_eX]

[DOI]

Mingyang Wu

Xiaohui Chen

CoRR, 2023

Development of a Deep Learning System for Intra-Operative Identification of Cancer Metastases.

[BibT_eX]

[DOI]

Thomas Schnelldorfer

Janil Castro

Atoussa Goldar-Najafi

CoRR, 2023

Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On Separate Normalization in Self-supervised Transformers.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems.

[BibT_eX]

[DOI]

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

2022

Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2022

Interpretable Node Representation with Attribute Decoding.

[BibT_eX]

[DOI]

Xiaohui Chen

Xi Chen

Trans. Mach. Learn. Res., 2022

NVDiff: Graph Generation through the Diffusion of Node Vectors.

[BibT_eX]

[DOI]

CoRR, 2022

Ensemble Spectral Prediction (ESP) Model for Metabolite Annotation.

[BibT_eX]

[DOI]

CoRR, 2022

Boost-RS: boosted embeddings for recommender systems and its application to enzyme-substrate interaction prediction.

[BibT_eX]

[DOI]

Xinmeng Li

Bioinform., 2022

PatchGT: Transformer Over Non-Trainable Clusters for Learning Graph Representations.

[BibT_eX]

[DOI]

Proceedings of the Learning on Graphs Conference, 2022

Predicting Physics in Mesh-reduced Space with Temporal Attention.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Modeling Graph Node Correlations with Neighbor Mixture Models.

[BibT_eX]

[DOI]

Michael C. Hughes

CoRR, 2021

Learning graph representations of biochemical networks and its application to enzymatic link prediction.

[BibT_eX]

[DOI]

Julie Jiang

Bioinform., 2021

Stochastic Iterative Graph Matching.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

GAN Ensemble for Anomaly Detection.

[BibT_eX]

[DOI]

Xu Han

Xiaohui Chen

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Using Graph Neural Networks for Mass Spectrometry Prediction.

[BibT_eX]

[DOI]

Hao Zhu

CoRR, 2020

Localizing and Amortizing: Efficient Inference for Gaussian Processes.

[BibT_eX]

[DOI]

Proceedings of The 12th Asian Conference on Machine Learning, 2020

Kriging Convolutional Networks.

[BibT_eX]

[DOI]

Gabriel Appleby

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Pathway Activity Analysis and Metabolite Annotation for Untargeted Metabolomics using Probabilistic Modeling.

[BibT_eX]

[DOI]

CoRR, 2019

Amortized Variational Inference with Graph Convolutional Networks for Gaussian Processes.

[BibT_eX]

[DOI]

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Non-Parametric Variational Inference with Graph Convolutional Networks for Gaussian Processes.

[BibT_eX]

[DOI]

CoRR, 2018

2017

Context Selection for Embedding Models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Transductive Optimization of Top k Precision.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

2015

Bayesian Active Clustering with Pairwise Constraints.

[BibT_eX]

[DOI]

Yuanli Pei

Xiaoli Z. Fern

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

2014

Gaussian Approximation of Collective Graphical Models.

[BibT_eX]

[DOI]

Daniel Sheldon

Proceedings of the 31th International Conference on Machine Learning, 2014

Learnability of the Superset Label Learning Problem.

[BibT_eX]

[DOI]

Proceedings of the 31th International Conference on Machine Learning, 2014

2012

Constructing Training Sets for Outlier Detection.

[BibT_eX]

[DOI]

Xiaoli Z. Fern

Proceedings of the Twelfth SIAM International Conference on Data Mining, 2012

A Conditional Multinomial Mixture Model for Superset Label Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

Incorporating Boosted Regression Trees into Ecological Latent Variable Models.

[BibT_eX]

[DOI]

Rebecca A. Hutchinson