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
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	333*	2016
All Roads Lead to Rome? Exploring Representational Similarities Between Latent Spaces of Generative Image Models C Badrinath, U Bhalla, A Oesterling, S Srinivas, H Lakkaraju ICML 2024 Workshop on Structured Probabilistic Inference {\&} Generative …, 0
Certifying llm safety against adversarial prompting A Kumar, C Agarwal, S Srinivas, S Feizi, H Lakkaraju arXiv preprint arXiv:2309.02705, 2023	65	2023
Characterizing Data Point Vulnerability as Average-Case Robustness T Han, S Srinivas, H Lakkaraju The 40th Conference on Uncertainty in Artificial Intelligence, 2024		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
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
Controlled blurring for improving image reconstruction quality in flutter-shutter acquisition S Srinivas, A Adiga, CS Seelamantula International Conference on Image Processing (ICIP), 2014	1	2014
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
Data-efficient structured pruning via submodular optimization M El Halabi, S Srinivas, S Lacoste-Julien Neural Information Processing Systems (NeurIPS), 2022	12	2022
Data-free parameter pruning for deep neural networks S Srinivas, RV Babu British Machine Vision Conference (BMVC), 2015	672	2015
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
Efficient Training of Low-Curvature Neural Networks S Srinivas, K Matoba, H Lakkaraju, F Fleuret Neural Information Processing Systems (NeurIPS), 2022	17	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	66*	2018
Full-gradient representation for neural network visualization S Srinivas, F Fleuret Neural Information Processing Systems (NeurIPS), 2019	272	2019
Generalized dropout S Srinivas, RV Babu Tech Report, 2016	48	2016
Gradient-based Methods for Deep Model Interpretability S Srinivas EPFL, 2021	1	2021
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
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
Learning the architecture of deep neural networks S Srinivas, RV Babu British Machine Vision Conference (BMVC), 2016	47*	2016

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