| Data-free parameter pruning for deep neural networks S Srinivas, RV Babu British Machine Vision Conference (BMVC), 2015 | 674 | 2015 |
| A taxonomy of deep convolutional neural nets for computer vision S Srinivas, RK Sarvadevabhatla, KR Mopuri, N Prabhu, SSS Kruthiventi, ... Frontiers in Robotics and AI 2, 36, 2016 | 335* | 2016 |
| Full-gradient representation for neural network visualization S Srinivas, F Fleuret Neural Information Processing Systems (NeurIPS), 2019 | 278 | 2019 |
| Training sparse neural networks S Srinivas, A Subramanya, R Venkatesh Babu CVPR Embedded Vision Workshop, 138-145, 2017 | 231 | 2017 |
| Knowledge Transfer with Jacobian Matching S Srinivas, F Fleuret International Conference on Machine Learning (ICML), 2018 | 192 | 2018 |
| Which explanation should i choose? a function approximation perspective to characterizing post hoc explanations T Han, S Srinivas, H Lakkaraju Neural Information Processing Systems (NeurIPS), 2022 | 76 | 2022 |
| Certifying llm safety against adversarial prompting A Kumar, C Agarwal, S Srinivas, S Feizi, H Lakkaraju arXiv preprint arXiv:2309.02705, 2023 | 69 | 2023 |
| Estimating Confidence for Deep Neural Networks through Density modeling A Subramanya, S Srinivas, RV Babu 2018 International Conference on Signal Processing and Communications (SPCOM …, 2018 | 66* | 2018 |
| Generalized dropout S Srinivas, RV Babu Tech Report, 2016 | 48 | 2016 |
| Learning the architecture of deep neural networks S Srinivas, RV Babu British Machine Vision Conference (BMVC), 2016 | 47* | 2016 |
| Rethinking the Role of Gradient Based Attribution Methods for Model Interpretability S Srinivas, F Fleuret International Conference on Learning Representations (ICLR), 2021 | 43 | 2021 |
| Efficient Training of Low-Curvature Neural Networks S Srinivas, K Matoba, H Lakkaraju, F Fleuret Neural Information Processing Systems (NeurIPS), 2022 | 18 | 2022 |
| Cyclical Pruning for Sparse Neural Networks S Srinivas, A Kuzmin, M Nagel, M van Baalen, A Skliar, T Blankevoort CVPR Workshop on Efficient Deep Learning for Computer Vision, 2022 | 14 | 2022 |
| Data-efficient structured pruning via submodular optimization M El Halabi, S Srinivas, S Lacoste-Julien Neural Information Processing Systems (NeurIPS), 2022 | 12 | 2022 |
| Which models have perceptually-aligned gradients? an explanation via off-manifold robustness S Srinivas, S Bordt, H Lakkaraju Advances in neural information processing systems 36, 2024 | 8 | 2024 |
| Discriminative Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability U Bhalla, S Srinivas, H Lakkaraju Advances in neural information processing systems, 2023 | 8* | 2023 |
| Compensating for large in-plane rotations in natural images L Boominathan, S Srinivas, RV Babu Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), 2016 | 7 | 2016 |
| Interpreting clip with sparse linear concept embeddings (splice) U Bhalla, A Oesterling, S Srinivas, FP Calmon, H Lakkaraju arXiv preprint arXiv:2402.10376, 2024 | 6 | 2024 |
| On minimizing the impact of dataset shifts on actionable explanations AP Meyer, D Ley, S Srinivas, H Lakkaraju Uncertainty in Artificial Intelligence, 1434-1444, 2023 | 4 | 2023 |
| Word-Level Explanations for Analyzing Bias in Text-to-Image Models A Lin, LM Paes, SH Tanneru, S Srinivas, H Lakkaraju arXiv preprint arXiv:2306.05500, 2023 | 3 | 2023 |
R. Venkatesh BabuCDS, Indian Institute of Science, Bangalore, IndiaVerified email at iisc.ac.in
