Isotope Identification Using Deep Learning: an Explanation
Oregon State Univ | Energy Systems and Nuclear Science Research Centre | NuScale Power LLC | King Abdulaziz Univ
Abstract
The exceptional performance of machine learning methods has led to their adaptation in many different domains. In the nuclear industry, it has been proposed that machine learning methods have the potential to revolutionize nuclear safety and radiation detection by leveraging that they can be used to augment human and device capabilities. While many applications focus on the accuracy of the learning algorithm's prediction, it has been shown in practice that these algorithms are prone to learn characteristics that are not descriptive or relevant. Hence, this paper focuses on understanding the reasoning behind the classification using saliency methods. Visual representations of the network's learned regions of interest are used to demonstrate whether domain-specific characteristics are being identified, which allows for the end-user to evaluate the performance based on domain knowledge. The results obtained show that focusing on a human-centered approach will ultimately enhance the transparency and trust of the deep learning algorithm's decision.
MoreTranslated text
Key words
Nuclear science,Robust artificial intelligence,Explainable deep learning,Gamma-ray spectroscopy
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
Application of Neural Networks for the Analysis of Gamma-Ray Spectra Measured with a Ge Spectrometer
2002
被引用102 | 浏览
1997
被引用25 | 浏览
2009
被引用25 | 浏览
2012
被引用36 | 浏览
2015
被引用29 | 浏览
2012
被引用15 | 浏览
2014
被引用6 | 浏览
2012
被引用60 | 浏览
2016
被引用22 | 浏览
2019
被引用47 | 浏览
2020
被引用69 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest