Suraj Srinivas

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 35

Top Topics

Avoid Overfitting

Machine Learning Models

Top Venues

Publications

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 (2024)
Usha Bhalla, Alex Oesterling, Suraj Srinivas, Flávio P. Calmon, Himabindu Lakkaraju
Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE). CoRR (2024)
Tessa Han, Suraj Srinivas, Himabindu Lakkaraju
Efficient Estimation of the Local Robustness of Machine Learning Models. CoRR (2023)
Alexander Lin, Lucas Monteiro Paes, Sree Harsha Tanneru, Suraj Srinivas, Himabindu Lakkaraju
Word-Level Explanations for Analyzing Bias in Text-to-Image Models. CoRR (2023)
Suraj Srinivas, Sebastian Bordt, Hima Lakkaraju
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness. CoRR (2023)
Anna P. Meyer, Dan Ley, Suraj Srinivas, Himabindu Lakkaraju
On Minimizing the Impact of Dataset Shifts on Actionable Explanations. UAI (2023)
Dan Ley, Leonard Tang, Matthew Nazari, Hongjin Lin, Suraj Srinivas, Himabindu Lakkaraju
Consistent Explanations in the Face of Model Indeterminacy via Ensembling. CoRR (2023)
Usha Bhalla, Suraj Srinivas, Himabindu Lakkaraju
Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability. NeurIPS (2023)
Aounon Kumar, Chirag Agarwal, Suraj Srinivas, Soheil Feizi, Hima Lakkaraju
Certifying LLM Safety against Adversarial Prompting. CoRR (2023)
Suraj Srinivas, Sebastian Bordt, Himabindu Lakkaraju
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness. NeurIPS (2023)
Anna P. Meyer, Dan Ley, Suraj Srinivas, Himabindu Lakkaraju
On Minimizing the Impact of Dataset Shifts on Actionable Explanations. CoRR (2023)
Usha Bhalla, Suraj Srinivas, Himabindu Lakkaraju
Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability. CoRR (2023)
Tessa Han, Suraj Srinivas, Himabindu Lakkaraju
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations. CoRR (2022)
Tessa Han, Suraj Srinivas, Himabindu Lakkaraju
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations. NeurIPS (2022)
Suraj Srinivas, Andrey Kuzmin, Markus Nagel, Mart van Baalen, Andrii Skliar, Tijmen Blankevoort
Cyclical Pruning for Sparse Neural Networks. CoRR (2022)
Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret
Efficient Training of Low-Curvature Neural Networks. NeurIPS (2022)
Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret
Flatten the Curve: Efficiently Training Low-Curvature Neural Networks. CoRR (2022)
Marwa El Halabi, Suraj Srinivas, Simon Lacoste-Julien
Data-Efficient Structured Pruning via Submodular Optimization. NeurIPS (2022)
Suraj Srinivas, Andrey Kuzmin, Markus Nagel, Mart van Baalen, Andrii Skliar, Tijmen Blankevoort
Cyclical Pruning for Sparse Neural Networks. CVPR Workshops (2022)
Marwa El Halabi, Suraj Srinivas, Simon Lacoste-Julien
Data-Efficient Structured Pruning via Submodular Optimization. CoRR (2022)
Suraj Srinivas, François Fleuret
Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability. ICLR (2021)
Suraj Srinivas, François Fleuret
Gradient Alignment in Deep Neural Networks. CoRR (2020)
Suraj Srinivas, François Fleuret
Full-Jacobian Representation of Neural Networks. CoRR (2019)
Suraj Srinivas, François Fleuret
Full-Gradient Representation for Neural Network Visualization. NeurIPS (2019)
Akshayvarun Subramanya, Suraj Srinivas, R. Venkatesh Babu
Estimating Confidence for Deep Neural Networks through Density modeling. SPCOM (2018)
Suraj Srinivas, François Fleuret
Knowledge Transfer with Jacobian Matching. ICML (2018)
Suraj Srinivas, François Fleuret
Knowledge Transfer with Jacobian Matching. CoRR (2018)
Akshayvarun Subramanya, Suraj Srinivas, R. Venkatesh Babu
Confidence estimation in Deep Neural networks via density modelling. CoRR (2017)
Suraj Srinivas, Akshayvarun Subramanya, R. Venkatesh Babu
Training Sparse Neural Networks. CVPR Workshops (2017)
Suraj Srinivas, Akshayvarun Subramanya, R. Venkatesh Babu
Training Sparse Neural Networks. CoRR (2016)
Suraj Srinivas, Radhakrishnan Venkatesh Babu
Learning Neural Network Architectures using Backpropagation. BMVC (2016)
Lokesh Boominathan, Suraj Srinivas, R. Venkatesh Babu
Compensating for large in-plane rotations in natural images. ICVGIP (2016)
Lokesh Boominathan, Suraj Srinivas, R. Venkatesh Babu
Compensating for Large In-Plane Rotations in Natural Images. CoRR (2016)
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 (2016)
Suraj Srinivas, R. Venkatesh Babu
Generalized Dropout. CoRR (2016)
Suraj Srinivas, R. Venkatesh Babu
Learning the Architecture of Deep Neural Networks. CoRR (2015)
Suraj Srinivas, R. Venkatesh Babu
Data-free parameter pruning for Deep Neural Networks. CoRR (2015)
Suraj Srinivas, R. Venkatesh Babu
Data-free Parameter Pruning for Deep Neural Networks. BMVC (2015)
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. Frontiers Robotics AI 2 (2015)
Suraj Srinivas, Aniruddha Adiga, Chandra Sekhar Seelamantula
Controlled blurring for improving image reconstruction quality in flutter-shutter acquisition. ICIP (2014)
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)