WebJul 30, 2024 · This fork implements message passing neural networks with Deep Evidential Regression. The changes made to implement evidential uncertainty can be found in: Model modifications: Because the evidential model requires outputting 4 auxilary parameters of the evidential distribution for every single desired target, we have … Web2024 Yanglin Feng, Hongyuan Zhu, Dezhong Peng, Xi Peng, Peng Hu#, RONO: Robust Discriminative Learning with Noisy Labels for 2D-3D Cross-Modal Retrieval, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada. Jun. 18-22, 2024. Pengxin Zeng, Yunfan Li, Peng Hu, Dezhong Peng, Jiancheng Lv, Xi …
[2104.06135] Multivariate Deep Evidential Regression - arXiv.org
WebDeep Evidential Regression - MIT To use this package, you must install the following dependencies first: 1. python (>=3.7) 2. tensorflow (>=2.0) 3. pytorch (support coming soon) Now you can install to start adding evidential layers and losses to your models! Now you're ready to start using this package directly as part of your existing tf.keras … See more All of the results published as part of our NeurIPS paper can be reproduced as part of this repository. Please refer to the reproducibility sectionfor details and instructions to obtain each result. See more If you use this code for evidential learning as part of your project or paper, please cite the following work: See more phil archer contact
deep-symbolic-regression/controller.py at master - Github
WebReview 2. Summary and Contributions: - Paper proposes a novel method for training non-Bayesian NNs to estimate a continuous target as well as its associated evidence in order to learn both aleatoric and epistemic uncertainty - No need for multiple passes and/or OOD datasets during training - The paper formulates an evidential regularizer for continuous … WebApr 13, 2024 · Multivariate Deep Evidential Regression. Nis Meinert, Alexander Lavin. There is significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains. A new approach with uncertainty-aware neural networks (NNs), based on learning evidential distributions for aleatoric and ... phil archer kprc-tv