Srinadh Bhojanapalli
- Computational Mathematics top 10%
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- Advanced Neural Network Applications 3
- Artificial Intelligence top 5%
- Neural Networks and Applications 3
- Topic Modeling 3
- Natural Language Processing Techniques 3
- Adversarial Robustness in Machine Learning 2
- Computational Mechanics top 10%
- Sparse and Compressive Sensing Techniques 6
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- Blind Source Separation Techniques 3
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- Advanced Optimization Algorithms Research 2
- Co-authors
- Nathan SrebroBehnam NeyshaburAndreas VeitThomas UnterthinerDaliang LiAyan ChakrabartiDaniel GläsnerDavid McAllester
- Journals
- npj Quantum Information (1 paper)Conference on Learning Theory (1 paper)arXiv (Cornell University) (6 papers)
- Partner nations
- United StatesJapanUnited Kingdom
In The Last Decade
Srinadh Bhojanapalli
14 papers receiving 617 citations
Hit Papers
Peers
Comparison fields: 5 of 92
- Computational Mathematics 15
- Computer Vision and Pattern Recognition 255
- Artificial Intelligence 382
- Acoustics and Ultrasonics 6
- Computational Mechanics 130
Countries citing papers authored by Srinadh Bhojanapalli
This map shows the geographic impact of Srinadh Bhojanapalli'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 Srinadh Bhojanapalli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Srinadh Bhojanapalli more than expected).
Fields of papers citing papers by Srinadh Bhojanapalli
This network shows the impact of papers produced by Srinadh Bhojanapalli. 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 Srinadh Bhojanapalli. The network helps show where Srinadh Bhojanapalli may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Srinadh Bhojanapalli, 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 | Demystifying the Better Performance of Position Encoding Variants for Transformer | 2021 | 4 |
| 2 | 2021 | 33 | |
| 3 | 2021 | 1 | |
| 4 | Understanding Robustness of Transformers for Image Classificationbreakdown → | 2021 | 227 |
| 5 | An efficient nonconvex reformulation of stagewise convex optimization problems | 2020 | 1 |
| 6 | 2019 | 10 | |
| 7 | The role of over-parametrization in generalization of neural networks | 2019 | 49 |
| 8 | Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form | 2018 | 2 |
| 9 | 2018 | 33 | |
| 10 | A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks | 2017 | 40 |
| 11 | Exploring Generalization in Deep Learning | 2017 | 139 |
| 12 | Global optimality of local search for low rank matrix recovery | 2016 | 57 |
| 13 | 2016 | 4 | |
| 14 | Coherent Matrix Completion | 2014 | 41 |
| 15 | Coherent Matrix Completion | 2013 | 6 |
About Srinadh Bhojanapalli
Srinadh Bhojanapalli is a scholar working on Numerical Analysis, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 15 papers that have together received 647 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (6 papers), Neural Networks and Applications (3 papers), Topic Modeling (3 papers), Natural Language Processing Techniques (3 papers), Advanced Neural Network Applications (3 papers), Blind Source Separation Techniques (3 papers), Advanced Optimization Algorithms Research (2 papers) and Adversarial Robustness in Machine Learning (2 papers). The work is most often cited by research in Computational Mathematics (15 citations), Computer Vision and Pattern Recognition (255 citations) and Artificial Intelligence (382 citations). Srinadh Bhojanapalli has collaborated with scholars based in United States, Japan and United Kingdom. Frequent co-authors include Nathan Srebro, Behnam Neyshabur, Andreas Veit, Thomas Unterthiner, Daliang Li, Ayan Chakrabarti, Daniel Gläsner, David McAllester, Sujay Sanghavi and Yudong Chen. Their work appears in journals such as npj Quantum Information, Conference on Learning Theory, arXiv (Cornell University), International Conference on Learning Representations and 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.