NSF PAR Search | NSF Public Access Repository

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

CRANIAL ONTOGENY IN THE EARLY CRETACEOUS CERATOPSIAN DINOSAUR PSITTACOSAURUS LUJIATUNENSIS: INTRASPECIFIC VARIATION BY AGE AND TAXONOMIC IMPLICATIONS

SON, MINYOUNG; MAKOVICKY, PETER J; ERICKSON, GREGORY M; ZHOU, CHANG-FU; YIN, YA-LEI (June 2024, UNIVERSITY OF MICHIGAN PAPERS ON PALEONTOLOGY NO. 39)

Full Text Available
Controllable Gradient Item Retrieval

https://doi.org/10.1145/3442381.3449963

Wang, Haonan; Zhou, Chang; Yang, Carl; Yang, Hongxia; He, Jingrui (April 2021, The Web Conference 2021)
null (Ed.)
Full Text Available
Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities

Yuan, Baichuan; Wang, Xiaowei; Ma, Jianxin; Zhou, Chang; Bertozzi, Andrea L.; Yang, Hongxia (January 2020, Prof. Int. Conf. Learning Representations (ICLR))

Multivariate spatial point process models can describe heterotopic data over space. However, highly multivariate intensities are computationally challenging due to the curse of dimensionality. To bridge this gap, we introduce a declustering based hidden variable model that leads to an efficient inference procedure via a variational autoencoder (VAE). We also prove that this model is a generalization of the VAE-based model for collaborative filtering. This leads to an interesting application of spatial point process models to recommender systems. Experimental results show the method’s utility on both synthetic data and real-world data sets.
more » « less
Full Text Available

Search for: All records