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Suraj Srinivas
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2020 – today
- 2024
- [i24]Usha Bhalla, Alex Oesterling, Suraj Srinivas, Flávio P. Calmon, Himabindu Lakkaraju:
Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE). CoRR abs/2402.10376 (2024) - [i23]Charumathi Badrinath, Usha Bhalla, Alex Oesterling, Suraj Srinivas, Himabindu Lakkaraju:
All Roads Lead to Rome? Exploring Representational Similarities Between Latent Spaces of Generative Image Models. CoRR abs/2407.13449 (2024) - [i22]Sebastian Bordt, Suraj Srinivas, Valentyn Boreiko, Ulrike von Luxburg:
How much can we forget about Data Contamination? CoRR abs/2410.03249 (2024) - 2023
- [c16]Usha Bhalla, Suraj Srinivas, Himabindu Lakkaraju:
Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability. NeurIPS 2023 - [c15]Suraj Srinivas, Sebastian Bordt, Himabindu Lakkaraju:
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness. NeurIPS 2023 - [c14]Anna P. Meyer, Dan Ley, Suraj Srinivas, Himabindu Lakkaraju:
On Minimizing the Impact of Dataset Shifts on Actionable Explanations. UAI 2023: 1434-1444 - [i21]Suraj Srinivas, Sebastian Bordt, Hima Lakkaraju:
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness. CoRR abs/2305.19101 (2023) - [i20]Alexander Lin, Lucas Monteiro Paes, Sree Harsha Tanneru, Suraj Srinivas, Himabindu Lakkaraju:
Word-Level Explanations for Analyzing Bias in Text-to-Image Models. CoRR abs/2306.05500 (2023) - [i19]Dan Ley, Leonard Tang, Matthew Nazari, Hongjin Lin, Suraj Srinivas, Himabindu Lakkaraju:
Consistent Explanations in the Face of Model Indeterminacy via Ensembling. CoRR abs/2306.06193 (2023) - [i18]Anna P. Meyer, Dan Ley, Suraj Srinivas, Himabindu Lakkaraju:
On Minimizing the Impact of Dataset Shifts on Actionable Explanations. CoRR abs/2306.06716 (2023) - [i17]Tessa Han, Suraj Srinivas, Himabindu Lakkaraju:
Efficient Estimation of the Local Robustness of Machine Learning Models. CoRR abs/2307.13885 (2023) - [i16]Usha Bhalla, Suraj Srinivas, Himabindu Lakkaraju:
Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability. CoRR abs/2307.15007 (2023) - [i15]Aounon Kumar, Chirag Agarwal, Suraj Srinivas, Soheil Feizi, Hima Lakkaraju:
Certifying LLM Safety against Adversarial Prompting. CoRR abs/2309.02705 (2023) - 2022
- [c13]Suraj Srinivas, Andrey Kuzmin, Markus Nagel, Mart van Baalen, Andrii Skliar, Tijmen Blankevoort:
Cyclical Pruning for Sparse Neural Networks. CVPR Workshops 2022: 2761-2770 - [c12]Marwa El Halabi, Suraj Srinivas, Simon Lacoste-Julien:
Data-Efficient Structured Pruning via Submodular Optimization. NeurIPS 2022 - [c11]Tessa Han, Suraj Srinivas, Himabindu Lakkaraju:
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations. NeurIPS 2022 - [c10]Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret:
Efficient Training of Low-Curvature Neural Networks. NeurIPS 2022 - [i14]Suraj Srinivas, Andrey Kuzmin, Markus Nagel, Mart van Baalen, Andrii Skliar, Tijmen Blankevoort:
Cyclical Pruning for Sparse Neural Networks. CoRR abs/2202.01290 (2022) - [i13]Marwa El Halabi, Suraj Srinivas, Simon Lacoste-Julien:
Data-Efficient Structured Pruning via Submodular Optimization. CoRR abs/2203.04940 (2022) - [i12]Tessa Han, Suraj Srinivas, Himabindu Lakkaraju:
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations. CoRR abs/2206.01254 (2022) - [i11]Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret:
Flatten the Curve: Efficiently Training Low-Curvature Neural Networks. CoRR abs/2206.07144 (2022) - 2021
- [c9]Suraj Srinivas, François Fleuret:
Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability. ICLR 2021 - 2020
- [i10]Suraj Srinivas, François Fleuret:
Gradient Alignment in Deep Neural Networks. CoRR abs/2006.09128 (2020)
2010 – 2019
- 2019
- [c8]Suraj Srinivas, François Fleuret:
Full-Gradient Representation for Neural Network Visualization. NeurIPS 2019: 4126-4135 - [i9]Suraj Srinivas, François Fleuret:
Full-Jacobian Representation of Neural Networks. CoRR abs/1905.00780 (2019) - 2018
- [c7]Suraj Srinivas, François Fleuret:
Knowledge Transfer with Jacobian Matching. ICML 2018: 4730-4738 - [c6]Akshayvarun Subramanya, Suraj Srinivas, R. Venkatesh Babu
:
Estimating Confidence for Deep Neural Networks through Density modeling. SPCOM 2018: 397-401 - [i8]Suraj Srinivas, François Fleuret:
Knowledge Transfer with Jacobian Matching. CoRR abs/1803.00443 (2018) - 2017
- [c5]Suraj Srinivas, Akshayvarun Subramanya, R. Venkatesh Babu
Training Sparse Neural Networks. CVPR Workshops 2017: 455-462 - [p1]Suraj Srinivas, Ravi Kiran Sarvadevabhatla
An Introduction to Deep Convolutional Neural Nets for Computer Vision. Deep Learning for Medical Image Analysis 2017: 25-52 - [i7]Akshayvarun Subramanya, Suraj Srinivas, R. Venkatesh Babu:
Confidence estimation in Deep Neural networks via density modelling. CoRR abs/1707.07013 (2017) - 2016
- [c4]Suraj Srinivas, Radhakrishnan Venkatesh Babu:
Learning Neural Network Architectures using Backpropagation. BMVC 2016 - [c3]Lokesh Boominathan, Suraj Srinivas, R. Venkatesh Babu
Compensating for large in-plane rotations in natural images. ICVGIP 2016: 69:1-69:8 - [i6]Suraj Srinivas, Ravi Kiran Sarvadevabhatla, Konda Reddy Mopuri, Nikita Prabhu, Srinivas S. S. Kruthiventi, R. Venkatesh Babu:
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision. CoRR abs/1601.06615 (2016) - [i5]Lokesh Boominathan, Suraj Srinivas, R. Venkatesh Babu:
Compensating for Large In-Plane Rotations in Natural Images. CoRR abs/1611.05744 (2016) - [i4]Suraj Srinivas, Akshayvarun Subramanya, R. Venkatesh Babu:
Training Sparse Neural Networks. CoRR abs/1611.06694 (2016) - [i3]Suraj Srinivas, R. Venkatesh Babu:
Generalized Dropout. CoRR abs/1611.06791 (2016) - 2015
- [j2]Suraj Srinivas, Ravi Kiran Sarvadevabhatla
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision. Frontiers Robotics AI 2: 36 (2015) - [c2]Suraj Srinivas, R. Venkatesh Babu:
Data-free Parameter Pruning for Deep Neural Networks. BMVC 2015: 31.1-31.12 - [i2]Suraj Srinivas, R. Venkatesh Babu:
Data-free parameter pruning for Deep Neural Networks. CoRR abs/1507.06149 (2015) - [i1]Suraj Srinivas, R. Venkatesh Babu:
Learning the Architecture of Deep Neural Networks. CoRR abs/1511.05497 (2015) - 2014
- [j1]Suraj Srinivas, Naman Nandan, Preetham Kulai, Akash R. Vasishta:
Universal Gestural Remote: A Conceptual Framework for a Human-Device Interface. Aust. J. Intell. Inf. Process. Syst. 13(4) (2014) - [c1]Suraj Srinivas, Aniruddha Adiga, Chandra Sekhar Seelamantula:
Controlled blurring for improving image reconstruction quality in flutter-shutter acquisition. ICIP 2014: 5826-5830
Coauthor Index
aka: Hima Lakkaraju
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