Akash Srivastava
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 2%
- Information Systems top 5%
- Molecular Biology
- Statistical and Nonlinear Physics top 5%
- Co-authors
- Charles SuttonLazar ValkovMichael U. GutmannChris RussellOtto W. WitteChristiane FrahmManja MarzEmanuel Barth
- Topics
- Genetics, Aging, and Longevity in Model Organisms (3 papers)Machine Learning in Bioinformatics (2 papers)Generative Adversarial Networks and Image Synthesis (2 papers)
- Journals
- SHILAP Revista de lepidopterologíaFrontiers in MicrobiologyNeurobiology of Aging
- Partner nations
- IndiaUnited StatesGermany
In The Last Decade
Akash Srivastava
36 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 164
- Artificial Intelligence 1.0k
- Computer Vision and Pattern Recognition 466
- Information Systems 258
- Molecular Biology 147
- Statistical and Nonlinear Physics 118
Countries citing papers authored by Akash Srivastava
This map shows the geographic impact of Akash Srivastava's research. 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 Akash Srivastava with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Akash Srivastava more than expected).
Fields of papers citing papers by Akash Srivastava
This network shows the impact of papers produced by Akash Srivastava. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Akash Srivastava. The network helps show where Akash Srivastava may publish in the future.
Co-authorship network of co-authors of Akash Srivastava
This figure shows the co-authorship network connecting the top 25 collaborators of Akash Srivastava. A scholar is included among the top collaborators of Akash Srivastava based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Akash Srivastava. Akash Srivastava is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 19 | |
| 3 | 1 | |
| 4 | 3 | |
| 5 | 2 | |
| 6 | 0 | |
| 7 | 6 | |
| 8 | 3 | |
| 9 | A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics | 4 |
| 10 | 11 | |
| 11 | 0 | |
| 12 | Variational Russian Roulette for Deep Bayesian Nonparametrics | 5 |
| 13 | 2 | |
| 14 | 2 | |
| 15 | VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning | 96 |
| 16 | Study of correlation of cup disc ratio with visual field loss in primary open angle glaucoma | 0 |
| 17 | Proceedings for the 5th International Conference on Learning Representationsbreakdown → | 1335 |
| 18 | 13 | |
| 19 | 1 | |
| 20 | 1 |
About Akash Srivastava
Akash Srivastava is a scholar working on Aging, Health Informatics and Molecular Medicine, having authored 44 papers that have together received 1.7k indexed citations. Recurring topics across this work include Genetics, Aging, and Longevity in Model Organisms (3 papers), Machine Learning in Bioinformatics (2 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). The work is most often cited by research in Artificial Intelligence (1.0k citations), Computer Vision and Pattern Recognition (466 citations) and Aging (23 citations). Akash Srivastava has collaborated with scholars based in India, United States and Germany. Frequent co-authors include Charles Sutton, Lazar Valkov, Michael U. Gutmann, Chris Russell, Otto W. Witte, Christiane Frahm, Manja Marz, Emanuel Barth, Dan Gutfreund and Faez Ahmed. Their work appears in journals such as SHILAP Revista de lepidopterología, Frontiers in Microbiology and Neurobiology of Aging.
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.