Suraj Srinivas

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R. Venkatesh BabuCDS, Indian Institute of Science, Bangalore, IndiaEmail yang diverifikasi di iisc.ac.in
Himabindu LakkarajuAssistant Professor, Harvard UniversityEmail yang diverifikasi di seas.harvard.edu
François FleuretUniversity of GenevaEmail yang diverifikasi di fleuret.org
Akshayvarun SubramanyaPh.D student at University of Maryland, Baltimore CountyEmail yang diverifikasi di umbc.edu
Tessa HanHarvard UniversityEmail yang diverifikasi di g.harvard.edu
Usha BhallaPh.D. Student, Harvard UniversityEmail yang diverifikasi di g.harvard.edu
Aounon KumarResearch Associate, Harvard UniversityEmail yang diverifikasi di hbs.edu
Alex OesterlingPhD Candidate, Harvard UniversityEmail yang diverifikasi di g.harvard.edu
Tijmen BlankevoortMeta - GenAI Llama Foundation ModelsEmail yang diverifikasi di qti.qualcomm.com
Dan LeyPhD Student, Harvard UniversityEmail yang diverifikasi di cam.ac.uk
Sebastian BordtUniversity of TübingenEmail yang diverifikasi di uni-tuebingen.de
Flavio du Pin CalmonHarvard UniversityEmail yang diverifikasi di seas.harvard.edu
Marwa El HalabiResearch Scientist, Samsung, SAILEmail yang diverifikasi di samsung.com
Andrey KuzminQualcomm AI ResearchEmail yang diverifikasi di qti.qualcomm.com
Lokesh BoominathanCarnegie Mellon UniversityEmail yang diverifikasi di andrew.cmu.edu
Anna P. MeyerUniversity of Wisconsin - MadisonEmail yang diverifikasi di wisc.edu
Charumathi BadrinathHarvard UniversityEmail yang diverifikasi di college.harvard.edu

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Suraj Srinivas

Research Scientist at Bosch

Email yang diverifikasi di us.bosch.com - Beranda

Deep Learning Machine Learning


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Data-free parameter pruning for deep neural networks S Srinivas, RV Babu British Machine Vision Conference (BMVC), 2015	711	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, 172022, 2016	354*	2016
Full-gradient representation for neural network visualization S Srinivas, F Fleuret Neural Information Processing Systems (NeurIPS), 2019	336	2019
Training sparse neural networks S Srinivas, A Subramanya, R Venkatesh Babu CVPR Embedded Vision Workshop, 138-145, 2017	250	2017
Knowledge Transfer with Jacobian Matching S Srinivas, F Fleuret International Conference on Machine Learning (ICML), 2018	202	2018
Certifying llm safety against adversarial prompting A Kumar, C Agarwal, S Srinivas, AJ Li, S Feizi, H Lakkaraju arXiv preprint arXiv:2309.02705, 2023	149	2023
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	97	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	68*	2018
Rethinking the Role of Gradient Based Attribution Methods for Model Interpretability S Srinivas, F Fleuret International Conference on Learning Representations (ICLR), 2021	53	2021
Generalized dropout S Srinivas, RV Babu Tech Report, 2016	51	2016
Learning the architecture of deep neural networks S Srinivas, RV Babu British Machine Vision Conference (BMVC), 2016	48*	2016
Efficient Training of Low-Curvature Neural Networks S Srinivas, K Matoba, H Lakkaraju, F Fleuret Neural Information Processing Systems (NeurIPS), 2022	23	2022
Interpreting clip with sparse linear concept embeddings (splice) U Bhalla, A Oesterling, S Srinivas, FP Calmon, H Lakkaraju arXiv preprint arXiv:2402.10376, 2024	22	2024
Data-efficient structured pruning via submodular optimization M El Halabi, S Srinivas, S Lacoste-Julien Neural Information Processing Systems (NeurIPS), 2022	19	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	19	2022
Discriminative feature attributions: bridging post hoc explainability and inherent interpretability U Bhalla, S Srinivas, H Lakkaraju Advances in neural information processing systems 36, 44105-44122, 2023	14*	2023
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, 21172-21195, 2023	13	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
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	5	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	5	2023

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