Data-free parameter pruning for deep neural networks S Srinivas, RV Babu British Machine Vision Conference (BMVC), 2015 | 670 | 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 | 334* | 2016 |
Full-gradient representation for neural network visualization S Srinivas, F Fleuret Neural Information Processing Systems (NeurIPS), 2019 | 267 | 2019 |
Training sparse neural networks S Srinivas, A Subramanya, R Venkatesh Babu CVPR Embedded Vision Workshop, 138-145, 2017 | 228 | 2017 |
Knowledge Transfer with Jacobian Matching S Srinivas, F Fleuret International Conference on Machine Learning (ICML), 2018 | 186 | 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 | 70 | 2022 |
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 | 65* | 2018 |
Certifying llm safety against adversarial prompting A Kumar, C Agarwal, S Srinivas, S Feizi, H Lakkaraju arXiv preprint arXiv:2309.02705, 2023 | 58 | 2023 |
Learning the architecture of deep neural networks S Srinivas, RV Babu British Machine Vision Conference (BMVC), 2016 | 48* | 2016 |
Generalized dropout S Srinivas, RV Babu Tech Report, 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 | 41 | 2021 |
Efficient Training of Low-Curvature Neural Networks S Srinivas, K Matoba, H Lakkaraju, F Fleuret Neural Information Processing Systems (NeurIPS), 2022 | 16 | 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 | 13 | 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 | 7 | 2024 |
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 |
Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability U Bhalla, S Srinivas, H Lakkaraju Advances in Neural Information Processing Systems 36, 2024 | 5* | 2024 |
Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE) U Bhalla, A Oesterling, S Srinivas, FP Calmon, H Lakkaraju arXiv preprint arXiv:2402.10376, 2024 | 4 | 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 |
Consistent explanations in the face of model indeterminacy via ensembling D Ley, L Tang, M Nazari, H Lin, S Srinivas, H Lakkaraju arXiv preprint arXiv:2306.06193, 2023 | 2 | 2023 |