Weight Uncertainty in Neural Network
- Journal
- International Conference on Machine Learning
In The Last Decade
doi.org/w7993249 →Countries where authors are citing Weight Uncertainty in Neural Network
This map shows the geographic impact of Weight Uncertainty in Neural Network. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Weight Uncertainty in Neural Network with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Weight Uncertainty in Neural Network more than expected).
Fields of papers citing Weight Uncertainty in Neural Network
This network shows the impact of Weight Uncertainty in Neural Network. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Weight Uncertainty in Neural Network.
About Weight Uncertainty in Neural Network
This paper, published in 2015, received 363 indexed citations . Written by Charles Blundell, Julien Cornebise, Koray Kavukcuoglu and Daan Wierstra covering the research area of Artificial Intelligence and Management Science and Operations Research. It is primarily cited by scholars working on Artificial Intelligence (246 citations), Computer Vision and Pattern Recognition (120 citations) and Electrical and Electronic Engineering (38 citations). Published in International Conference on Machine Learning.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w7993249.