Gauri Jagatap

Gauri Jagatap

PhD, New York University | Deep learning, computer vision, signal processing

Publications

Conference publications:

  1. M. Chasmai, G. Jagatap, G. KV, G. Van Horn, S. Maji, A. Fanelli, “Moment Sampling in Video LLMs for Long-Form VQA”, CVPR Workshop on Video LLMs, 2025. [ Preprint ]

  2. A. Joshi, S. Akula, G. Jagatap, C. Hegde, “A Few Adversarial Tokens Can Break Vision Transformers”, CVPR Workshop on Adversarial Machine Learning on Computer Vision, 2023.

  3. T. Nguyen, G. Jagatap and C. Hegde, “Inverse imaging with generative priors via Langevin dynamics”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2022.

  4. G. Jagatap, A. Joshi, A. Chowdhury, S. Garg and C. Hegde, “Adversarially robust learning via entropic regularization”, ICML Workshop on Adversarial Machine Learning, 2021. [ Preprint ]

  5. G. Jagatap, C. Hegde, “High dynamic range imaging using deep image priors”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020. [Preprint / Paper / Talk]

  6. G. Jagatap, C. Hegde, “Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors”, Advances in Neural Information Processing Systems (NeurIPS), 2019. (Acceptance rate: 21.18%) [ Preprint / Code / Slides / Poster]

  7. G. Jagatap, C. Hegde, “Phase Retrieval using Untrained Neural Network Priors”, NeurIPS Workshop on Solving Inverse Problems with Deep Neural Networks, 2019. [ Paper ]

  8. G. Jagatap, C. Hegde, “Linearly Convergent Algorithms for Learning Shallow Residual Networks. “, IEEE International Symposium on Information Theory (ISIT), 2019. [Preprint / Talk]

  9. G. Jagatap, Z. Chen, C. Hegde, N. Vaswani, “Model corrected low rank ptychography”, IEEE International Conference on Image Processing (ICIP), 2018. [Preprint / Paper / Poster]

  10. G. Jagatap, C. Hegde, “Towards sample-optimal methods for solving random quadratic equations with structure”, IEEE International Symposium on Information Theory (ISIT), 2018. [Preprint / Paper / Talk / Code ]

  11. G. Jagatap, Z. Chen, C. Hegde, N. Vaswani, “Sub-diffraction imaging using Fourier ptychography and structured sparsity”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (Oral Presentation) 2018. [ Preprint / Paper / Talk / Code ]

  12. Z. Chen, G. Jagatap, S. Nayer, C. Hegde, N. Vaswani, “Low rank Fourier ptychography”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018. [Preprint / Paper / Poster]

  13. G. Jagatap, C. Hegde, “Fast, sample efficient algorithms for structured phase retrieval”, Advances in Neural Information Processing Systems (NIPS), 2017. (Acceptance rate: 20.93%) [ Paper / Poster / Code ]

Journal articles:

  1. T. Nguyen, G. Jagatap and C. Hegde, “Provable compressed sensing with generative priors via langevin dynamics”, IEEE Transactions on Information Theory, 2022.

  2. G. Jagatap, Z. Chen, C. Hegde, N. Vaswani, “Sample efficient Fourier ptychography on structured data”, IEEE Transactions on Computational Imaging, 2019. [ Preprint / Code ]

  3. G. Jagatap, C. Hegde, “Sample efficient algorithms for recovering structured signals from magnitude-only measurements”, IEEE Transactions on Information Theory, 2019. [arXiv Preprint/ Paper / Code]

  4. G. Jagatap, A. Joshi, A. Chowdhury, S. Garg and C. Hegde, “Adversarially robust learning via entropic regularization”, Frontiers in Artificial Intelligence, 2021. [ Preprint ]

User studies:
  1. P. Mehta, G. Jagatap, K. Gallagher, B. Timmerman, P. Deb, S. Garg, R. Greenstadt and B. Dolan-Gavitt, “Can Deepfakes be created on a whim?”, International Workshop on Cyber Social Threats (CySoc), ACM Web Conference 2023.
Under review:
  1. A. Agrawal, G. KV, G. Jagatap, R. Aralikatti, J. Yuan, V. Kamarshi, A. Fanelli, F. Huang, “Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings”.

You can also find my profile on Google Scholar and Publons.