Suraj Srinivas

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R. Venkatesh BabuCDS, Indian Institute of Science, Bangalore, IndiaVerified email at iisc.ac.in
François FleuretUniversity of GenevaVerified email at fleuret.org
Akshayvarun SubramanyaPh.D student at University of Maryland, Baltimore CountyVerified email at umbc.edu
Ravi Kiran SarvadevabhatlaAssistant Professor, Centre for Visual Information Technology, IIIT-Hyderabad, IndiaVerified email at iiit.ac.in
Konda Reddy MopuriIndian Institute of Technology HyderabadVerified email at ai.iith.ac.in
Nikita PrabhuGraduate Student, Computational and Data Sciences, Indian Institute of ScienceVerified email at ssl.serc.iisc.in
Himabindu LakkarajuAssistant Professor, Harvard UniversityVerified email at seas.harvard.edu
Tessa HanHarvard UniversityVerified email at g.harvard.edu
Tijmen BlankevoortQualcomm AI ResearchVerified email at qti.qualcomm.com
Markus NagelQualcomm AI ResearchVerified email at qualcomm.com
Andrii SkliarResearch Engineer, Qualcomm AI Research NetherlandsVerified email at qti.qualcomm.com
Lokesh BoominathanRice UniversityVerified email at rice.edu
Mart van BaalenMachine Learning Researcher, Qualcomm AI ResearchVerified email at qti.qualcomm.com
Andrey KuzminQualcomm AI ResearchVerified email at qti.qualcomm.com
Marwa El HalabiResearch Scientist, Samsung, SAILVerified email at samsung.com
Simon Lacoste-JulienAssociate Professor - Canada CIFAR AI Chair, University of Montreal / MilaVerified email at iro.umontreal.ca
Aniruddha AdigaResearch Scientist, Biocomplexity Institute and Initiative, UVAVerified email at virginia.edu
Chandra Sekhar SeelamantulaIndian Institute of Science, BangaloreVerified email at ee.iisc.ernet.in
Sebastian BordtUniversity of TübingenVerified email at uni-tuebingen.de

Suraj Srinivas

Postdoctoral Research Fellow at Harvard University

Verified email at seas.harvard.edu - Homepage

Deep Learning Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Data-free parameter pruning for deep neural networks S Srinivas, RV Babu British Machine Vision Conference (BMVC), 2015	566	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	292*	2016
Training sparse neural networks S Srinivas, A Subramanya, R Venkatesh Babu CVPR Embedded Vision Workshop, 138-145, 2017	185	2017
Full-gradient representation for neural network visualization S Srinivas, F Fleuret Neural Information Processing Systems (NeurIPS), 2019	178	2019
Knowledge Transfer with Jacobian Matching S Srinivas, F Fleuret International Conference on Machine Learning (ICML), 2018	146	2018
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	57*	2018
Learning the architecture of deep neural networks S Srinivas, RV Babu British Machine Vision Conference (BMVC), 2016	46*	2016
Generalized dropout S Srinivas, RV Babu Tech Report, 2016	43	2016
Rethinking the Role of Gradient Based Attribution Methods for Model Interpretability S Srinivas, F Fleuret International Conference on Learning Representations (ICLR), 2021	32*	2021
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	14	2022
Efficient Training of Low-Curvature Neural Networks S Srinivas, K Matoba, H Lakkaraju, F Fleuret Neural Information Processing Systems (NeurIPS), 2022	5	2022
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	4	2016
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	3	2022
Data-efficient structured pruning via submodular optimization M El Halabi, S Srinivas, S Lacoste-Julien Neural Information Processing Systems (NeurIPS), 2022	2	2022
Gradient-based Methods for Deep Model Interpretability S Srinivas EPFL, 2021	1	2021
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
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness S Srinivas, S Bordt, H Lakkaraju arXiv preprint arXiv:2305.19101, 2023		2023

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