Practical Variational Inference for Neural Networks
Impact in
Classified as
- Authors
- Alex Graves
- Journal
- Neural Information Processing Systems
In The Last Decade
doi.org/w8312100 →Countries where authors are citing Practical Variational Inference for Neural Networks
This map shows the geographic impact of Practical Variational Inference for Neural Networks. 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 Practical Variational Inference for Neural Networks with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Practical Variational Inference for Neural Networks more than expected).
Fields of papers citing Practical Variational Inference for Neural Networks
This network shows the impact of Practical Variational Inference for Neural Networks. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Practical Variational Inference for Neural Networks.
About Practical Variational Inference for Neural Networks
This paper, published in 2011, received 475 indexed citations . Written by Alex Graves covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (317 citations), Computer Vision and Pattern Recognition (124 citations), Signal Processing (45 citations), Electrical and Electronic Engineering (34 citations) and Control and Systems Engineering (31 citations). Published in Neural Information Processing Systems.
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/w8312100.