Akash Srivastava
- Artificial Intelligence top 1%
- Natural Language Processing Techniques 2
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- Generative Adversarial Networks and Image Synthesis 2
- Aging top 10%
- Genetics, Aging, and Longevity in Model Organisms 3
- Information Systems top 5%
- Signal Processing top 5%
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- Machine Learning in Bioinformatics 2
- Mitochondrial Function and Pathology 2
- Genomics and Phylogenetic Studies 2
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- Model Reduction and Neural Networks 2
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- Parallel Computing and Optimization Techniques 2
- Co-authors
- Charles SuttonLazar ValkovMichael U. GutmannChris RussellOtto W. WitteChristiane FrahmManja MarzEmanuel Barth
- Journals
- SHILAP Revista de lepidopterología (5 papers)Frontiers in Microbiology (1 paper)Neurobiology of Aging (1 paper)
- 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
- Aging 23
- Information Systems 258
- Signal Processing 103
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
The 25 scholars most cited alongside Akash Srivastava, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 1 | |
| 2 | 2023 | 19 | |
| 3 | 2023 | 1 | |
| 4 | 2022 | 3 | |
| 5 | 2021 | 2 | |
| 6 | 2021 | 0 | |
| 7 | 2021 | 6 | |
| 8 | 2021 | 3 | |
| 9 | A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics | 2021 | 4 |
| 10 | 2020 | 11 | |
| 11 | 2020 | 0 | |
| 12 | Variational Russian Roulette for Deep Bayesian Nonparametrics | 2019 | 5 |
| 13 | 2019 | 2 | |
| 14 | 2018 | 2 | |
| 15 | VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning | 2017 | 96 |
| 16 | Study of correlation of cup disc ratio with visual field loss in primary open angle glaucoma | 2017 | 0 |
| 17 | Proceedings for the 5th International Conference on Learning Representationsbreakdown → | 2017 | 1335 |
| 18 | 2016 | 13 | |
| 19 | 2010 | 1 | |
| 20 | 1987 | 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), Generative Adversarial Networks and Image Synthesis (2 papers), Mitochondrial Function and Pathology (2 papers), Genomics and Phylogenetic Studies (2 papers), Natural Language Processing Techniques (2 papers), Model Reduction and Neural Networks (2 papers) and Parallel Computing and Optimization Techniques (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.