More Like this

1. Knowledge As Priors: Cross-Modal Knowledge Generalization for Datasets Without Superior Knowledge

   https://doi.org/10.1109/CVPR42600.2020.00656  

   Zhao, Long ; Peng, Xi ; Chen, Yuxiao ; Kapadia, Mubbasir ; Metaxas, Dimitris N. ( June 2020 , Computer Vision and Pattern Recognition)

   null (Ed.)
2. Knowledge as Priors: Cross-Modal Knowledge Generalization for Datasets without Superior Knowledge

   Zhao, Long ; Peng, Xi ; Chen, Yuxiao ; Kapadia, Mubbasir ; Metaxas, Dimitris N. ( April 2020 , Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR))
3. Knowledge graph fact prediction via knowledge-enriched tensor factorization

   https://doi.org/10.1016/j.websem.2019.01.004  

   Padia, Ankur ; Kalpakis, Konstantinos ; Ferraro, Francis ; Finin, Tim ( February 2019 , Journal of Web Semantics)
4. Dual-Level Knowledge Distillation via Knowledge Alignment and Correlation

   https://doi.org/10.1109/TNNLS.2022.3190166  

   Ding, Fei ; Yang, Yin ; Hu, Hongxin ; Krovi, Venkat ; Luo, Feng ( July 2022 , IEEE Transactions on Neural Networks and Learning Systems)
5. Quasi anomalous knowledge: searching for new physics with embedded knowledge

   https://doi.org/10.1007/JHEP06(2021)030  

   Park, Sang Eon ; Rankin, Dylan ; Udrescu, Silviu-Marian ; Yunus, Mikaeel ; Harris, Philip ( June 2021 , Journal of High Energy Physics)

   null (Ed.)

   A bstract Discoveries of new phenomena often involve a dedicated search for a hypothetical physics signature. Recently, novel deep learning techniques have emerged for anomaly detection in the absence of a signal prior. However, by ignoring signal priors, the sensitivity of these approaches is significantly reduced. We present a new strategy dubbed Quasi Anomalous Knowledge (QUAK), whereby we introduce alternative signal priors that capture some of the salient features of new physics signatures, allowing for the recovery of sensitivity even when the alternative signal is incorrect. This approach can be applied to a broad range of physics models and neural network architectures. In this paper, we apply QUAK to anomaly detection of new physics events at the CERN Large Hadron Collider utilizing variational autoencoders with normalizing flow. 

   more » « less