Suraj Srinivas 个人学术档案

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R. Venkatesh BabuCDS, Indian Institute of Science, Bangalore, India在 iisc.ac.in 的电子邮件经过验证
Himabindu LakkarajuAssistant Professor, Harvard University在 seas.harvard.edu 的电子邮件经过验证
François FleuretUniversity of Geneva在 fleuret.org 的电子邮件经过验证
Akshayvarun SubramanyaPh.D student at University of Maryland, Baltimore County在 umbc.edu 的电子邮件经过验证
Ravi Kiran SarvadevabhatlaAssistant Professor, Centre for Visual Information Technology, IIIT-Hyderabad, India在 iiit.ac.in 的电子邮件经过验证
Konda Reddy MopuriIndian Institute of Technology Hyderabad在 ai.iith.ac.in 的电子邮件经过验证
Nikita PrabhuGraduate Student, Computational and Data Sciences, Indian Institute of Science在 ssl.serc.iisc.in 的电子邮件经过验证
Tessa HanHarvard University在 g.harvard.edu 的电子邮件经过验证
Usha BhallaPh.D. Student, Harvard University在 g.harvard.edu 的电子邮件经过验证
Aounon KumarPostdoc, Harvard University在 hbs.edu 的电子邮件经过验证
Chirag AgarwalPostdoctoral Research Fellow, Harvard在 hbs.edu 的电子邮件经过验证
Tijmen BlankevoortMeta Reality Labs在 qti.qualcomm.com 的电子邮件经过验证
Markus NagelQualcomm AI Research在 qualcomm.com 的电子邮件经过验证
Andrii SkliarResearch Engineer, Qualcomm AI Research Netherlands在 qti.qualcomm.com 的电子邮件经过验证
Dan LeyPhD Student, Harvard University在 cam.ac.uk 的电子邮件经过验证
Mart van BaalenResearch Scientist (Senior Staff), Qualcomm AI Research在 qti.qualcomm.com 的电子邮件经过验证
Andrey KuzminQualcomm AI Research在 qti.qualcomm.com 的电子邮件经过验证
Marwa El HalabiResearch Scientist, Samsung, SAIL在 samsung.com 的电子邮件经过验证
Simon Lacoste-JulienAssociate Professor - Canada CIFAR AI Chair, University of Montreal / Mila在 iro.umontreal.ca 的电子邮件经过验证
Alex OesterlingPhD Student, Harvard University在 g.harvard.edu 的电子邮件经过验证

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Suraj Srinivas

Postdoctoral Research Fellow at Harvard University

在 seas.harvard.edu 的电子邮件经过验证 - 首页

Deep Learning Machine Learning


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
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
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
Characterizing Data Point Vulnerability as Average-Case Robustness T Han, S Srinivas, H Lakkaraju The 40th Conference on Uncertainty in Artificial Intelligence, 2024		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	6*	2023
Certifying llm safety against adversarial prompting A Kumar, C Agarwal, S Srinivas, S Feizi, H Lakkaraju arXiv preprint arXiv:2309.02705, 2023	65	2023
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
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	1	2023
Data-efficient structured pruning via submodular optimization M El Halabi, S Srinivas, S Lacoste-Julien Neural Information Processing Systems (NeurIPS), 2022	12	2022
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	74	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	13	2022
Efficient Training of Low-Curvature Neural Networks S Srinivas, K Matoba, H Lakkaraju, F Fleuret Neural Information Processing Systems (NeurIPS), 2022	17	2022
Gradient-based Methods for Deep Model Interpretability S Srinivas EPFL, 2021	1	2021
Rethinking the Role of Gradient Based Attribution Methods for Model Interpretability S Srinivas, F Fleuret International Conference on Learning Representations (ICLR), 2021	42	2021
Full-gradient representation for neural network visualization S Srinivas, F Fleuret Neural Information Processing Systems (NeurIPS), 2019	272	2019
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
Knowledge Transfer with Jacobian Matching S Srinivas, F Fleuret International Conference on Machine Learning (ICML), 2018	191	2018
Learning Compact Architectures for Deep Neural Networks S Srinivas Indian Institute of Science Bangalore, 2017		2017
Training sparse neural networks S Srinivas, A Subramanya, R Venkatesh Babu CVPR Embedded Vision Workshop, 138-145, 2017	231	2017
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

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