Controlled blurring for improving image reconstruction quality in flutter-shutter acquisition. - Suraj Srinivas; Aniruddha Adiga; Chandra Sekhar Seelamantula (ICIP, 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)

Data-free Parameter Pruning for Deep Neural Networks. - Suraj Srinivas; R. Venkatesh Babu (BMVC, 2015)

Learning the Architecture of Deep Neural Networks. - Suraj Srinivas; R. Venkatesh Babu (CoRR, 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 Neural Network Architectures using Backpropagation. - Suraj Srinivas; Radhakrishnan Venkatesh Babu (BMVC, 2016)

Compensating for large in-plane rotations in natural images. - Lokesh Boominathan; Suraj Srinivas; R. Venkatesh Babu (ICVGIP, 2016)

Generalized Dropout. - Suraj Srinivas; R. Venkatesh Babu (CoRR, 2016)

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 (Deep Learning for Medical Image Analysis, 2017)

Training Sparse Neural Networks. - Suraj Srinivas; Akshayvarun Subramanya; R. Venkatesh Babu (CVPR Workshops, 2017)

Confidence estimation in Deep Neural networks via density modelling. - Akshayvarun Subramanya; Suraj Srinivas; R. Venkatesh Babu (CoRR, 2017)

Knowledge Transfer with Jacobian Matching. - Suraj Srinivas; François Fleuret (ICML, 2018)

Estimating Confidence for Deep Neural Networks through Density modeling. - Akshayvarun Subramanya; Suraj Srinivas; R. Venkatesh Babu (SPCOM, 2018)

Full-Gradient Representation for Neural Network Visualization. - Suraj Srinivas; François Fleuret (NeurIPS, 2019)

Full-Jacobian Representation of Neural Networks. - Suraj Srinivas; François Fleuret (CoRR, 2019)

Gradient Alignment in Deep Neural Networks. - Suraj Srinivas; François Fleuret (CoRR, 2020)

Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability. - Suraj Srinivas; François Fleuret (ICLR, 2021)

Cyclical Pruning for Sparse Neural Networks. - Suraj Srinivas; Andrey Kuzmin; Markus Nagel; Mart van Baalen; Andrii Skliar; Tijmen Blankevoort (CVPR Workshops, 2022)

Data-Efficient Structured Pruning via Submodular Optimization. - Marwa El Halabi; Suraj Srinivas; Simon Lacoste-Julien (NeurIPS, 2022)

Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations. - Tessa Han; Suraj Srinivas; Himabindu Lakkaraju (NeurIPS, 2022)

Efficient Training of Low-Curvature Neural Networks. - Suraj Srinivas; Kyle Matoba; Himabindu Lakkaraju; François Fleuret (NeurIPS, 2022)

Flatten the Curve: Efficiently Training Low-Curvature Neural Networks. - Suraj Srinivas; Kyle Matoba; Himabindu Lakkaraju; François Fleuret (CoRR, 2022)

Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability. - Usha Bhalla; Suraj Srinivas; Himabindu Lakkaraju (NeurIPS, 2023)

Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness. - Suraj Srinivas; Sebastian Bordt; Himabindu Lakkaraju (NeurIPS, 2023)

On Minimizing the Impact of Dataset Shifts on Actionable Explanations. - Anna P. Meyer; Dan Ley; Suraj Srinivas; Himabindu Lakkaraju (UAI, 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)

Consistent Explanations in the Face of Model Indeterminacy via Ensembling. - Dan Ley; Leonard Tang; Matthew Nazari; Hongjin Lin; Suraj Srinivas; Himabindu Lakkaraju (CoRR, 2023)

Efficient Estimation of the Local Robustness of Machine Learning Models. - Tessa Han; Suraj Srinivas; Himabindu Lakkaraju (CoRR, 2023)

Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability. - Usha Bhalla; Suraj Srinivas; Himabindu Lakkaraju (CoRR, 2023)

Certifying LLM Safety against Adversarial Prompting. - Aounon Kumar; Chirag Agarwal; Suraj Srinivas; Soheil Feizi; Hima Lakkaraju (CoRR, 2023)

Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE). - Usha Bhalla; Alex Oesterling; Suraj Srinivas; Flávio P. Calmon; Himabindu Lakkaraju (NeurIPS, 2024)

Characterizing Data Point Vulnerability as Average-Case Robustness. - Tessa Han; Suraj Srinivas; Himabindu Lakkaraju (UAI, 2024)

All Roads Lead to Rome? Exploring Representational Similarities Between Latent Spaces of Generative Image Models. - Charumathi Badrinath; Usha Bhalla; Alex Oesterling; Suraj Srinivas; Himabindu Lakkaraju (CoRR, 2024)

How much can we forget about Data Contamination? - Sebastian Bordt; Suraj Srinivas; Valentyn Boreiko; Ulrike von Luxburg (CoRR, 2024)

Generalized Group Data Attribution. - Dan Ley; Suraj Srinivas; Shichang Zhang; Gili Rusak; Himabindu Lakkaraju (CoRR, 2024)

Towards Unifying Interpretability and Control: Evaluation via Intervention. - Usha Bhalla; Suraj Srinivas; Asma Ghandeharioun; Himabindu Lakkaraju (CoRR, 2024)

Towards Interpretable Soft Prompts. - Oam Patel; Jason Wang; Nikhil Shivakumar Nayak; Suraj Srinivas; Himabindu Lakkaraju (CoRR, 2025)

Interpretability Illusions with Sparse Autoencoders: Evaluating Robustness of Concept Representations. - Aaron J. Li; Suraj Srinivas; Usha Bhalla; Himabindu Lakkaraju (CoRR, 2025)