Suraj Srinivas

According to our database, Suraj Srinivas authored at least 31 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE).

Usha Bhalla, Alex Oesterling, Suraj Srinivas, Flávio P. Calmon, Himabindu Lakkaraju

CoRR, 2024

2023

Certifying LLM Safety against Adversarial Prompting.

Aounon Kumar, Chirag Agarwal, Suraj Srinivas, Soheil Feizi, Hima Lakkaraju

CoRR, 2023

Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability.

Usha Bhalla, Suraj Srinivas, Himabindu Lakkaraju

CoRR, 2023

Efficient Estimation of the Local Robustness of Machine Learning Models.

Tessa Han, Suraj Srinivas, Himabindu Lakkaraju

CoRR, 2023

Consistent Explanations in the Face of Model Indeterminacy via Ensembling.

Dan Ley, Leonard Tang, Matthew Nazari, Hongjin Lin, Suraj Srinivas, Himabindu Lakkaraju

CoRR, 2023

Word-Level Explanations for Analyzing Bias in Text-to-Image Models.

Alexander Lin, Lucas Monteiro Paes, Sree Harsha Tanneru, Suraj Srinivas, Himabindu Lakkaraju

CoRR, 2023

On Minimizing the Impact of Dataset Shifts on Actionable Explanations.

Anna P. Meyer, Dan Ley, Suraj Srinivas, Himabindu Lakkaraju

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness.

Suraj Srinivas, Sebastian Bordt, Himabindu Lakkaraju

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability.

Usha Bhalla, Suraj Srinivas, Himabindu Lakkaraju

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Flatten the Curve: Efficiently Training Low-Curvature Neural Networks.

Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret

CoRR, 2022

Efficient Training of Low-Curvature Neural Networks.

Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations.

Tessa Han, Suraj Srinivas, Himabindu Lakkaraju

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Data-Efficient Structured Pruning via Submodular Optimization.

Marwa El Halabi, Suraj Srinivas, Simon Lacoste-Julien

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Cyclical Pruning for Sparse Neural Networks.

Suraj Srinivas, Andrey Kuzmin, Markus Nagel, Mart van Baalen, Andrii Skliar, Tijmen Blankevoort

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

2021

Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability.

Suraj Srinivas, François Fleuret

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Gradient Alignment in Deep Neural Networks.

Suraj Srinivas, François Fleuret

CoRR, 2020

2019

Full-Jacobian Representation of Neural Networks.

Suraj Srinivas, François Fleuret

CoRR, 2019

Full-Gradient Representation for Neural Network Visualization.

Suraj Srinivas, François Fleuret

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Estimating Confidence for Deep Neural Networks through Density modeling.

Akshayvarun Subramanya, Suraj Srinivas, R. Venkatesh Babu

Proceedings of the 2018 International Conference on Signal Processing and Communications (SPCOM), 2018

Knowledge Transfer with Jacobian Matching.

Suraj Srinivas, François Fleuret

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Confidence estimation in Deep Neural networks via density modelling.

Akshayvarun Subramanya, Suraj Srinivas, R. Venkatesh Babu

CoRR, 2017

Training Sparse Neural Networks.

Suraj Srinivas, Akshayvarun Subramanya, R. Venkatesh Babu

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017

An Introduction to Deep Convolutional Neural Nets for Computer Vision.

Suraj Srinivas, Ravi Kiran Sarvadevabhatla, Konda Reddy Mopuri, Nikita Prabhu, Srinivas S. S. Kruthiventi, R. Venkatesh Babu

Proceedings of the Deep Learning for Medical Image Analysis, 1st Edition, 2017

2016

Generalized Dropout.

Suraj Srinivas, R. Venkatesh Babu

CoRR, 2016

Compensating for large in-plane rotations in natural images.

Lokesh Boominathan, Suraj Srinivas, R. Venkatesh Babu

Proceedings of the Tenth Indian Conference on Computer Vision, 2016

Learning Neural Network Architectures using Backpropagation.

Suraj Srinivas, Radhakrishnan Venkatesh Babu

Proceedings of the British Machine Vision Conference 2016, 2016

2015

A Taxonomy of Deep Convolutional Neural Nets for Computer Vision.

Suraj Srinivas, Ravi Kiran Sarvadevabhatla, Konda Reddy Mopuri, Nikita Prabhu, Srinivas S. S. Kruthiventi, R. Venkatesh Babu

Frontiers Robotics AI, 2015

Learning the Architecture of Deep Neural Networks.

Suraj Srinivas, R. Venkatesh Babu

CoRR, 2015

Data-free Parameter Pruning for Deep Neural Networks.

Suraj Srinivas, R. Venkatesh Babu

Proceedings of the British Machine Vision Conference 2015, 2015

2014

Universal Gestural Remote: A Conceptual Framework for a Human-Device Interface.

Suraj Srinivas, Naman Nandan, Preetham Kulai, Akash R. Vasishta

Aust. J. Intell. Inf. Process. Syst., 2014

Controlled blurring for improving image reconstruction quality in flutter-shutter acquisition.

Suraj Srinivas, Aniruddha Adiga, Chandra Sekhar Seelamantula

Proceedings of the 2014 IEEE International Conference on Image Processing, 2014