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2020 – today
- 2024
[j26]Biswajit Khara, Ethan Herron, Aditya Balu, Dhruv Gamdha, Chih-Hsuan Yang, Kumar Saurabh, Anushrut Jignasu, Zhanhong Jiang, Soumik Sarkar, Chinmay Hegde, Baskar Ganapathysubramanian, Adarsh Krishnamurthy
:
Neural PDE Solvers for Irregular Domains. Comput. Aided Des. 172: 103709 (2024)- [j25]Biswajit Khara, Aditya Balu, Ameya Joshi, Soumik Sarkar, Chinmay Hegde, Adarsh Krishnamurthy, Baskar Ganapathysubramanian:
NeuFENet: neural finite element solutions with theoretical bounds for parametric PDEs. Eng. Comput. 40(5): 2761-2783 (2024) - [j24]Benjamin Feuer, Yurong Liu, Chinmay Hegde, Juliana Freire:
ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Models. Proc. VLDB Endow. 17(9): 2279-2292 (2024) - [j23]Md. Zahid Hasan, Jiajing Chen, Jiyang Wang, Mohammed Shaiqur Rahman, Ameya Joshi, Senem Velipasalar, Chinmay Hegde, Anuj Sharma, Soumik Sarkar:
Vision-Language Models Can Identify Distracted Driver Behavior From Naturalistic Videos. IEEE Trans. Intell. Transp. Syst. 25(9): 11602-11616 (2024) - [c80]Aarshvi Gajjar, Wai Ming Tai, Xingyu Xu, Chinmay Hegde, Christopher Musco, Yi Li:
Agnostic Active Learning of Single Index Models with Linear Sample Complexity. COLT 2024: 1715-1754 - [c79]Anushrut Jignasu, Aditya Balu, Soumik Sarkar, Chinmay Hegde, Baskar Ganapathysubramanian, Adarsh Krishnamurthy:
SDFConnect: Neural Implicit Surface Reconstruction of a Sparse Point Cloud with Topological Constraints. CVPR Workshops 2024: 5271-5279 - [c78]Nastaran Saadati, Minh Pham, Nasla Saleem, Joshua R. Waite, Aditya Balu, Zhanhong Jiang, Chinmay Hegde, Soumik Sarkar:
DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Models. CVPR 2024: 27507-27517 - [c77]Govind Mittal, Chinmay Hegde, Nasir D. Memon:
Gotcha: Real-Time Video Deepfake Detection via Challenge-Response. EuroS&P 2024: 1-20 - [c76]Sudipta Banerjee, Sai Pranaswi Mullangi, Shruti Wagle, Chinmay Hegde, Nasir D. Memon:
Mitigating the Impact of Attribute Editing on Face Recognition. IJCB 2024: 1-10 - [c75]Minh Pham, Kelly O. Marshall, Niv Cohen, Govind Mittal, Chinmay Hegde:
Circumventing Concept Erasure Methods For Text-To-Image Generative Models. ICLR 2024 - [c74]Ashutosh Nirala, Ameya Joshi, Soumik Sarkar, Chinmay Hegde:
Fast Certification of Vision-Language Models Using Incremental Randomized Smoothing. SaTML 2024: 252-271 - [i82]Benjamin Feuer, Robin Tibor Schirrmeister, Valeriia Cherepanova, Chinmay Hegde, Frank Hutter, Micah Goldblum, Niv Cohen, Colin White:
TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks. CoRR abs/2402.11137 (2024) - [i81]Govind Mittal, Arthur Jakobsson, Kelly O. Marshall, Chinmay Hegde, Nasir D. Memon:
AI-assisted Tagging of Deepfake Audio Calls using Challenge-Response. CoRR abs/2402.18085 (2024) - [i80]Sudipta Banerjee, Sai Pranaswi Mullangi, Shruti Wagle, Chinmay Hegde, Nasir D. Memon:
Mitigating the Impact of Attribute Editing on Face Recognition. CoRR abs/2403.08092 (2024) - [i79]Minh Pham, Kelly O. Marshall, Chinmay Hegde, Niv Cohen:
Robust Concept Erasure Using Task Vectors. CoRR abs/2404.03631 (2024) - [i78]Nastaran Saadati, Minh Pham, Nasla Saleem, Joshua R. Waite, Aditya Balu, Zhanhong Jiang, Chinmay Hegde, Soumik Sarkar:
DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Models. CoRR abs/2404.08079 (2024) - [i77]Aarshvi Gajjar, Wai Ming Tai, Xingyu Xu, Chinmay Hegde, Christopher Musco, Yi Li:
Agnostic Active Learning of Single Index Models with Linear Sample Complexity. CoRR abs/2405.09312 (2024) - [i76]Chih-Hsuan Yang, Benjamin Feuer, Zaki Jubery, Zi K. Deng, Andre Nakkab, Md. Zahid Hasan, Shivani Chiranjeevi, Kelly O. Marshall, Nirmal Baishnab, Asheesh K. Singh, Arti Singh, Soumik Sarkar, Nirav C. Merchant, Chinmay Hegde, Baskar Ganapathysubramanian:
Arboretum: A Large Multimodal Dataset Enabling AI for Biodiversity. CoRR abs/2406.17720 (2024) - [i75]Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Benjamin Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Siddartha Naidu, Chinmay Hegde, Yann LeCun, Tom Goldstein, Willie Neiswanger, Micah Goldblum:
LiveBench: A Challenging, Contamination-Free LLM Benchmark. CoRR abs/2406.19314 (2024) - [i74]Anushrut Jignasu, Kelly O. Marshall, Ankush Kumar Mishra, Lucas Nerone Rillo, Baskar Ganapathysubramanian, Aditya Balu, Chinmay Hegde, Adarsh Krishnamurthy:
Slice-100K: A Multimodal Dataset for Extrusion-based 3D Printing. CoRR abs/2407.04180 (2024) - [i73]Muhammad Arbab Arshad, Talukder Zaki Jubery, Tirtho Roy, Rim Nassiri, Asheesh K. Singh, Arti Singh, Chinmay Hegde, Baskar Ganapathysubramanian, Aditya Balu, Adarsh Krishnamurthy, Soumik Sarkar:
AgEval: A Benchmark for Zero-Shot and Few-Shot Plant Stress Phenotyping with Multimodal LLMs. CoRR abs/2407.19617 (2024) - [i72]Sanjita Prajapati, Tanu Singh, Chinmay Hegde, Pranamesh Chakraborty:
Evaluation and Comparison of Visual Language Models for Transportation Engineering Problems. CoRR abs/2409.02278 (2024) - [i71]Ronak Tali, Ali Rabeh, Cheng-Hau Yang, Mehdi Shadkhah, Samundra Karki, Abhisek Upadhyaya, Suriya Dhakshinamoorthy, Marjan Saadati, Soumik Sarkar, Adarsh Krishnamurthy, Chinmay Hegde, Aditya Balu, Baskar Ganapathysubramanian:
FlowBench: A Large Scale Benchmark for Flow Simulation over Complex Geometries. CoRR abs/2409.18032 (2024) - [i70]Benjamin Feuer, Jiawei Xu, Niv Cohen, Patrick Yubeaton, Govind Mittal, Chinmay Hegde:
SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification. CoRR abs/2410.05057 (2024) - 2023
- [j22]Benjamin Feuer, Ameya Joshi, Minh Pham, Chinmay Hegde:
Distributionally Robust Classification on a Data Budget. Trans. Mach. Learn. Res. 2023 (2023) - [c73]Aarshvi Gajjar, Christopher Musco, Chinmay Hegde:
Active Learning for Single Neuron Models with Lipschitz Non-Linearities. AISTATS 2023: 4101-4113 - [c72]Feyza Duman Keles, Pruthuvi Mahesakya Wijewardena, Chinmay Hegde:
On The Computational Complexity of Self-Attention. ALT 2023: 597-619 - [c71]Sudipta Banerjee, Govind Mittal, Ameya Joshi, Chinmay Hegde, Nasir D. Memon:
Identity-Preserving Aging of Face Images via Latent Diffusion Models. IJCB 2023: 1-10 - [c70]Jiangyuan Li, Thanh Van Nguyen, Chinmay Hegde, Raymond K. W. Wong:
Implicit Regularization for Group Sparsity. ICLR 2023 - [i69]Sam Earle, Ozlem Yildiz, Julian Togelius, Chinmay Hegde:
Pathfinding Neural Cellular Automata. CoRR abs/2301.06820 (2023) - [i68]Jiangyuan Li, Thanh Van Nguyen, Chinmay Hegde, Raymond K. W. Wong:
Implicit Regularization for Group Sparsity. CoRR abs/2301.12540 (2023) - [i67]Andre Nakkab, Benjamin Feuer, Chinmay Hegde:
LiT Tuned Models for Efficient Species Detection. CoRR abs/2302.10281 (2023) - [i66]Kelly O. Marshall, Minh Pham, Ameya Joshi, Anushrut Jignasu, Aditya Balu, Adarsh Krishnamurthy, Chinmay Hegde:
ZeroForge: Feedforward Text-to-Shape Without 3D Supervision. CoRR abs/2306.08183 (2023) - [i65]Md. Zahid Hasan, Jiajing Chen, Jiyang Wang, Mohammed Shaiqur Rahman, Ameya Joshi, Senem Velipasalar, Chinmay Hegde, Anuj Sharma, Soumik Sarkar:
Vision-Language Models can Identify Distracted Driver Behavior from Naturalistic Videos. CoRR abs/2306.10159 (2023) - [i64]Sudipta Banerjee, Govind Mittal, Ameya Joshi, Chinmay Hegde, Nasir D. Memon:
Identity-Preserving Aging of Face Images via Latent Diffusion Models. CoRR abs/2307.08585 (2023) - [i63]Minh Pham, Kelly O. Marshall, Chinmay Hegde:
Circumventing Concept Erasure Methods For Text-to-Image Generative Models. CoRR abs/2308.01508 (2023) - [i62]Benjamin Feuer, Ameya Joshi, Minh Pham, Chinmay Hegde:
Distributionally Robust Classification on a Data Budget. CoRR abs/2308.03821 (2023) - [i61]Anushrut Jignasu, Kelly O. Marshall, Baskar Ganapathysubramanian, Aditya Balu, Chinmay Hegde, Adarsh Krishnamurthy:
Towards Foundational AI Models for Additive Manufacturing: Language Models for G-Code Debugging, Manipulation, and Comprehension. CoRR abs/2309.02465 (2023) - [i60]Feyza Duman Keles, Chinmay Hegde:
On the Fine-Grained Hardness of Inverting Generative Models. CoRR abs/2309.05795 (2023) - [i59]Naren Dhyani, Jianqiao Mo, Minsu Cho, Ameya Joshi, Siddharth Garg, Brandon Reagen, Chinmay Hegde:
PriViT: Vision Transformers for Fast Private Inference. CoRR abs/2310.04604 (2023) - [i58]Benjamin Feuer, Yurong Liu, Chinmay Hegde, Juliana Freire:
ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Models. CoRR abs/2310.18208 (2023) - [i57]Benjamin Feuer, Chinmay Hegde:
Exploring Dataset-Scale Indicators of Data Quality. CoRR abs/2311.04016 (2023) - [i56]Ashutosh Nirala, Ameya Joshi, Chinmay Hegde, Soumik Sarkar:
Fast Certification of Vision-Language Models Using Incremental Randomized Smoothing. CoRR abs/2311.09024 (2023) - [i55]Benjamin Feuer, Chinmay Hegde, Niv Cohen:
Scaling TabPFN: Sketching and Feature Selection for Tabular Prior-Data Fitted Networks. CoRR abs/2311.10609 (2023) - 2022
- [j21]Anjana Deva Prasad, Aditya Balu, Harshil Shah, Soumik Sarkar, Chinmay Hegde, Adarsh Krishnamurthy:
NURBS-Diff: A Differentiable Programming Module for NURBS. Comput. Aided Des. 146: 103199 (2022) - [j20]Minsu Cho, Zahra Ghodsi, Brandon Reagen, Siddharth Garg, Chinmay Hegde:
Sphynx: A Deep Neural Network Design for Private Inference. IEEE Secur. Priv. 20(5): 22-34 (2022) - [j19]Zhanhong Jiang, Chao Liu, Young M. Lee, Chinmay Hegde, Soumik Sarkar, Dongxiang Jiang:
The Stochastic Augmented Lagrangian method for domain adaptation. Knowl. Based Syst. 235: 107593 (2022) - [j18]Thanh Van Nguyen, Gauri Jagatap, Chinmay Hegde:
Provable Compressed Sensing With Generative Priors via Langevin Dynamics. IEEE Trans. Inf. Theory 68(11): 7410-7422 (2022) - [c69]Zhanhong Jiang, Xian Yeow Lee, Sin Yong Tan, Kai Liang Tan, Aditya Balu, Young M. Lee, Chinmay Hegde, Soumik Sarkar:
MDPGT: Momentum-Based Decentralized Policy Gradient Tracking. AAAI 2022: 9377-9385 - [c68]Zhanhong Jiang, Aditya Balu, Xian Yeow Lee, Young M. Lee, Chinmay Hegde, Soumik Sarkar:
Distributed Online Non-convex Optimization with Composite Regret. Allerton 2022: 1-8 - [c67]Thanh Van Nguyen, Gauri Jagatap, Chinmay Hegde:
Inverse Imaging with Generative Priors Via Langevin Dynamics. ICASSP 2022: 8672-8676 - [c66]Minsu Cho, Ameya Joshi, Brandon Reagen, Siddharth Garg, Chinmay Hegde:
Selective Network Linearization for Efficient Private Inference. ICML 2022: 3947-3961 - [i54]Minsu Cho, Ameya Joshi, Siddharth Garg, Brandon Reagen, Chinmay Hegde:
Selective Network Linearization for Efficient Private Inference. CoRR abs/2202.02340 (2022) - [i53]Ameya Joshi, Minh Pham, Minsu Cho, Leonid Boytsov, Filipe Condessa, J. Zico Kolter, Chinmay Hegde:
Smooth-Reduce: Leveraging Patches for Improved Certified Robustness. CoRR abs/2205.06154 (2022) - [i52]Benjamin Feuer, Ameya Joshi, Chinmay Hegde:
A Meta-Analysis of Distributionally-Robust Models. CoRR abs/2206.07565 (2022) - [i51]Minh Pham, Minsu Cho, Ameya Joshi, Chinmay Hegde:
Revisiting Self-Distillation. CoRR abs/2206.08491 (2022) - [i50]Feyza Duman Keles, Pruthuvi Mahesakya Wijewardena, Chinmay Hegde:
On The Computational Complexity of Self-Attention. CoRR abs/2209.04881 (2022) - [i49]Zhanhong Jiang, Aditya Balu, Xian Yeow Lee, Young M. Lee, Chinmay Hegde, Soumik Sarkar:
Distributed Online Non-convex Optimization with Composite Regret. CoRR abs/2209.10105 (2022) - [i48]Govind Mittal, Jiraphon Yenphraphai, Chinmay Hegde, Nasir D. Memon:
Gotcha: A Challenge-Response System for Real-Time Deepfake Detection. CoRR abs/2210.06186 (2022) - [i47]Benjamin Feuer, Ameya Joshi, Chinmay Hegde:
Caption supervision enables robust learners. CoRR abs/2210.07396 (2022) - [i46]Aarshvi Gajjar, Chinmay Hegde, Christopher Musco:
Active Learning for Single Neuron Models with Lipschitz Non-Linearities. CoRR abs/2210.13601 (2022) - [i45]Biswajit Khara, Ethan Herron, Zhanhong Jiang, Aditya Balu, Chih-Hsuan Yang, Kumar Saurabh, Anushrut Jignasu, Soumik Sarkar, Chinmay Hegde, Adarsh Krishnamurthy, Baskar Ganapathysubramanian:
Neural PDE Solvers for Irregular Domains. CoRR abs/2211.03241 (2022) - 2021
- [j17]Viraj Shah, Chinmay Hegde:
Sparse signal recovery from modulo observations. EURASIP J. Adv. Signal Process. 2021(1): 1-17 (2021) - [j16]Zhanhong Jiang, Aditya Balu, Chinmay Hegde, Soumik Sarkar:
On Consensus-Optimality Trade-offs in Collaborative Deep Learning. Frontiers Artif. Intell. 4: 573731 (2021) - [j15]Gauri Jagatap, Ameya Joshi, Animesh Basak Chowdhury, Siddharth Garg, Chinmay Hegde:
Adversarially Robust Learning via Entropic Regularization. Frontiers Artif. Intell. 4: 780843 (2021) - [j14]Xian Yeow Lee, Joshua R. Waite, Chih-Hsuan Yang, Balaji Sesha Sarath Pokuri, Ameya Joshi, Aditya Balu, Chinmay Hegde, Baskar Ganapathysubramanian, Soumik Sarkar:
Fast inverse design of microstructures via generative invariance networks. Nat. Comput. Sci. 1(3): 229-238 (2021) - [j13]Thanh Van Nguyen, Raymond K. W. Wong, Chinmay Hegde:
Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis. IEEE Trans. Inf. Theory 67(7): 4669-4692 (2021) - [c65]Aditya Balu, Zhanhong Jiang, Sin Yong Tan, Chinmay Hegde, Young M. Lee, Soumik Sarkar:
Decentralized Deep Learning Using Momentum-Accelerated Consensus. ICASSP 2021: 3675-3679 - [c64]Yasaman Esfandiari, Sin Yong Tan, Zhanhong Jiang, Aditya Balu, Ethan Herron, Chinmay Hegde, Soumik Sarkar:
Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data. ICML 2021: 3036-3046 - [c63]Minsu Cho, Aditya Balu, Ameya Joshi, Anjana Deva Prasad, Biswajit Khara, Soumik Sarkar, Baskar Ganapathysubramanian, Adarsh Krishnamurthy, Chinmay Hegde:
Differentiable Spline Approximations. NeurIPS 2021: 20270-20282 - [c62]Jiangyuan Li, Thanh Van Nguyen, Chinmay Hegde, Ka Wai Wong:
Implicit Sparse Regularization: The Impact of Depth and Early Stopping. NeurIPS 2021: 28298-28309 - [c61]Aditya Balu, Sergio Botelho, Biswajit Khara, Vinay Rao, Soumik Sarkar, Chinmay Hegde, Adarsh Krishnamurthy, Santi Adavani, Baskar Ganapathysubramanian:
Distributed multigrid neural solvers on megavoxel domains. SC 2021: 49 - [i44]Thanh Van Nguyen, Gauri Jagatap, Chinmay Hegde:
Provable Compressed Sensing with Generative Priors via Langevin Dynamics. CoRR abs/2102.12643 (2021) - [i43]Yasaman Esfandiari, Sin Yong Tan, Zhanhong Jiang, Aditya Balu, Ethan Herron, Chinmay Hegde, Soumik Sarkar:
Cross-Gradient Aggregation for Decentralized Learning from Non-IID data. CoRR abs/2103.02051 (2021) - [i42]Aditya Balu, Sergio Botelho, Biswajit Khara, Vinay Rao, Chinmay Hegde, Soumik Sarkar, Santi Adavani, Adarsh Krishnamurthy, Baskar Ganapathysubramanian:
Distributed Multigrid Neural Solvers on Megavoxel Domains. CoRR abs/2104.14538 (2021) - [i41]Viraj Shah, Rakib Hyder, M. Salman Asif, Chinmay Hegde:
Provably Convergent Algorithms for Solving Inverse Problems Using Generative Models. CoRR abs/2105.06371 (2021) - [i40]Minsu Cho, Zahra Ghodsi, Brandon Reagen, Siddharth Garg, Chinmay Hegde:
Sphynx: ReLU-Efficient Network Design for Private Inference. CoRR abs/2106.11755 (2021) - [i39]Jiangyuan Li, Thanh Van Nguyen, Chinmay Hegde, Raymond K. W. Wong:
Implicit Sparse Regularization: The Impact of Depth and Early Stopping. CoRR abs/2108.05574 (2021) - [i38]Minsu Cho, Aditya Balu, Ameya Joshi, Anjana Deva Prasad, Biswajit Khara, Soumik Sarkar, Baskar Ganapathysubramanian, Adarsh Krishnamurthy, Chinmay Hegde:
Differentiable Spline Approximations. CoRR abs/2110.01532 (2021) - [i37]Biswajit Khara, Aditya Balu, Ameya Joshi, Soumik Sarkar, Chinmay Hegde, Adarsh Krishnamurthy, Baskar Ganapathysubramanian:
NeuFENet: Neural Finite Element Solutions with Theoretical Bounds for Parametric PDEs. CoRR abs/2110.01601 (2021) - [i36]Ameya Joshi, Gauri Jagatap, Chinmay Hegde:
Adversarial Token Attacks on Vision Transformers. CoRR abs/2110.04337 (2021) - [i35]Zhanhong Jiang, Xian Yeow Lee, Sin Yong Tan, Kai Liang Tan, Aditya Balu, Young M. Lee, Chinmay Hegde, Soumik Sarkar:
MDPGT: Momentum-based Decentralized Policy Gradient Tracking. CoRR abs/2112.02813 (2021) - 2020
- [j12]Gauri Jagatap, Zhengyu Chen, Seyedehsara Nayer, Chinmay Hegde, Namrata Vaswani:
Sample Efficient Fourier Ptychography for Structured Data. IEEE Trans. Computational Imaging 6: 344-357 (2020) - [c60]Ameya Joshi, Minsu Cho, Viraj Shah, Balaji Sesha Sarath Pokuri, Soumik Sarkar, Baskar Ganapathysubramanian, Chinmay Hegde:
InvNet: Encoding Geometric and Statistical Invariances in Deep Generative Models. AAAI 2020: 4377-4384 - [c59]Xian Yeow Lee, Sambit Ghadai, Kai Liang Tan, Chinmay Hegde, Soumik Sarkar:
Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents. AAAI 2020: 4577-4584 - [c58]M. Salman Asif, Chinmay Hegde:
The Benefits of Side Information for Structured Phase Retrieval. EUSIPCO 2020: 775-778 - [c57]Gauri Jagatap, Chinmay Hegde:
High Dynamic Range Imaging Using Deep Image Priors. ICASSP 2020: 9289-9293 - [c56]Sergio Botelho, Ameya Joshi, Biswajit Khara, Vinay Rao, Soumik Sarkar, Chinmay Hegde, Santi Adavani, Baskar Ganapathysubramanian:
Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models. MLHPC/AI4S@SC 2020: 50-63 - [i34]Minsu Cho, Ameya Joshi, Chinmay Hegde:
ESPN: Extremely Sparse Pruned Networks. CoRR abs/2006.15741 (2020) - [i33]Minsu Cho, Mohammadreza Soltani, Chinmay Hegde:
Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery. CoRR abs/2007.04087 (2020) - [i32]Sergio Botelho, Ameya Joshi, Biswajit Khara, Soumik Sarkar, Chinmay Hegde, Santi Adavani, Baskar Ganapathysubramanian:
Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models. CoRR abs/2007.12792 (2020) - [i31]Gauri Jagatap, Animesh Basak Chowdhury, Siddharth Garg, Chinmay Hegde:
Adversarially Robust Learning via Entropic Regularization. CoRR abs/2008.12338 (2020) - [i30]Aditya Balu, Zhanhong Jiang, Sin Yong Tan, Chinmay Hegde, Young M. Lee, Soumik Sarkar:
Decentralized Deep Learning using Momentum-Accelerated Consensus. CoRR abs/2010.11166 (2020)
2010 – 2019
- 2019
- [j11]Thanh Van Nguyen, Raymond K. W. Wong, Chinmay Hegde:
Provably Accurate Double-Sparse Coding. J. Mach. Learn. Res. 20: 141:1-141:43 (2019) - [j10]Gauri Jagatap, Chinmay Hegde:
Sample-Efficient Algorithms for Recovering Structured Signals From Magnitude-Only Measurements. IEEE Trans. Inf. Theory 65(7): 4434-4456 (2019) - [j9]Mohammadreza Soltani, Chinmay Hegde:
Fast and Provable Algorithms for Learning Two-Layer Polynomial Neural Networks. IEEE Trans. Signal Process. 67(13): 3361-3371 (2019) - [c55]Rakib Hyder, Chinmay Hegde, M. Salman Asif:
Fourier Phase Retrieval with Side Information Using Generative Prior. ACSSC 2019: 759-763 - [c54]Mohammadreza Soltani, Swayambhoo Jain, Abhinav V. Sambasivan, Chinmay Hegde:
Leaming Structured Signals Using GAN s with Applications in Denoising and Demixing. ACSSC 2019: 2127-2131 - [c53]Thanh Van Nguyen, Raymond K. W. Wong, Chinmay Hegde:
On the Dynamics of Gradient Descent for Autoencoders. AISTATS 2019: 2858-2867 - [c52]Amitangshu Mukherjee, Ameya Joshi, Soumik Sarkar, Chinmay Hegde:
Attribute-Controlled Traffic Data Augmentation Using Conditional Generative Models. CVPR Workshops 2019: 83-87 - [c51]Viraj Shah, Chinmay Hegde:
Signal Reconstruction From Modulo Observations. GlobalSIP 2019: 1-5 - [c50]Minsu Cho, Chinmay Hegde:
Reducing the Search Space for Hyperparameter Optimization Using Group Sparsity. ICASSP 2019: 3627-3631 - [c49]Rakib Hyder, Viraj Shah, Chinmay Hegde, M. Salman Asif:
Alternating Phase Projected Gradient Descent with Generative Priors for Solving Compressive Phase Retrieval. ICASSP 2019: 7705-7709 - [c48]Ameya Joshi, Amitangshu Mukherjee, Soumik Sarkar, Chinmay Hegde:
Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers. ICCV 2019: 4772-4782 - [c47]Gauri Jagatap, Chinmay Hegde:
Linearly Convergent Algorithms for Learning Shallow Residual Networks. ISIT 2019: 1797-1801 - [c46]Gauri Jagatap, Chinmay Hegde:
Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors. NeurIPS 2019: 14803-14813 - [i29]Rakib Hyder, Viraj Shah, Chinmay Hegde, M. Salman Asif:
Alternating Phase Projected Gradient Descent with Generative Priors for Solving Compressive Phase Retrieval. CoRR abs/1903.02707 (2019) - [i28]Chinmay Hegde, Fritz Keinert, Eric S. Weber:
A Kaczmarz Algorithm for Solving Tree Based Distributed Systems of Equations. CoRR abs/1904.05732 (2019) - [i27]Ameya Joshi, Amitangshu Mukherjee, Soumik Sarkar, Chinmay Hegde:
Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers. CoRR abs/1904.08489 (2019) - [i26]Minsu Cho, Chinmay Hegde:
Reducing The Search Space For Hyperparameter Optimization Using Group Sparsity. CoRR abs/1904.11095 (2019) - [i25]Viraj Shah, Ameya Joshi, Sambuddha Ghosal, Balaji Sesha Sarath Pokuri, Soumik Sarkar, Baskar Ganapathysubramanian, Chinmay Hegde:
Encoding Invariances in Deep Generative Models. CoRR abs/1906.01626 (2019) - [i24]Minsu Cho, Mohammadreza Soltani, Chinmay Hegde:
One-Shot Neural Architecture Search via Compressive Sensing. CoRR abs/1906.02869 (2019) - [i23]Gauri Jagatap, Chinmay Hegde:
Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors. CoRR abs/1906.08763 (2019) - [i22]Xian Yeow Lee, Sambit Ghadai, Kai Liang Tan, Chinmay Hegde, Soumik Sarkar:
Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents. CoRR abs/1909.02583 (2019) - [i21]Zhanhong Jiang, Aditya Balu, Sin Yong Tan, Young M. Lee, Chinmay Hegde, Soumik Sarkar:
On Higher-order Moments in Adam. CoRR abs/1910.06878 (2019) - [i20]Thanh Van Nguyen, Raymond K. W. Wong, Chinmay Hegde:
Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis. CoRR abs/1911.11983 (2019) - 2018
- [c45]Thanh Van Nguyen, Raymond K. W. Wong, Chinmay Hegde:
A Provable Approach for Double-Sparse Coding. AAAI 2018: 3852-3859 - [c44]Mohammadreza Soltani, Chinmay Hegde:
Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix Estimation. AISTATS 2018: 1417-1426 - [c43]Chinmay Hegde:
Algorithmic Aspects of Inverse Problems Using Generative Models. Allerton 2018: 166-172 - [c42]M. Salman Asif, Chinmay Hegde:
Phase Retrieval for Signals in Union of Subspaces. GlobalSIP 2018: 356-359 - [c41]Viraj Shah, Chinmay Hegde:
Solving Linear Inverse Problems Using Gan Priors: An Algorithm with Provable Guarantees. ICASSP 2018: 4609-4613 - [c40]Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani:
Sub-Diffraction Imaging Using Fourier Ptychography and Structured Sparsity. ICASSP 2018: 6493-6497 - [c39]Zhengyu Chen, Gauri Jagatap, Seyedehsara Nayer, Chinmay Hegde, Namrata Vaswani:
Low Rank Fourier Ptychography. ICASSP 2018: 6538-6542 - [c38]Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani:
Model Corrected Low Rank Ptychography. ICIP 2018: 3988-3992 - [c37]Thanh Van Nguyen, Akshay Soni, Chinmay Hegde:
On Learning Sparsely Used Dictionaries from Incomplete Samples. ICML 2018: 3766-3775 - [c36]Mohammadreza Soltani, Chinmay Hegde:
Fast Low-Rank Matrix Estimation for Ill-Conditioned Matrices. ISIT 2018: 371-375 - [c35]Gauri Jagatap, Chinmay Hegde:
Towards Sample-Optimal Methods for Solving Random Quadratic Equations with Structure. ISIT 2018: 2296-2300 - [c34]Pranamesh Chakraborty, Anuj Sharma, Chinmay Hegde:
Freeway Traffic Incident Detection from Cameras: A Semi-Supervised Learning Approach. ITSC 2018: 1840-1845 - [i19]Viraj Shah, Chinmay Hegde:
Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guarantees. CoRR abs/1802.08406 (2018) - [i18]Thanh Van Nguyen, Akshay Soni, Chinmay Hegde:
On Learning Sparsely Used Dictionaries from Incomplete Samples. CoRR abs/1804.09217 (2018) - [i17]Zhanhong Jiang, Aditya Balu, Chinmay Hegde, Soumik Sarkar:
On Consensus-Optimality Trade-offs in Collaborative Deep Learning. CoRR abs/1805.12120 (2018) - [i16]Thanh Van Nguyen, Raymond K. W. Wong, Chinmay Hegde:
Autoencoders Learn Generative Linear Models. CoRR abs/1806.00572 (2018) - [i15]Gauri Jagatap, Chinmay Hegde:
Learning ReLU Networks via Alternating Minimization. CoRR abs/1806.07863 (2018) - [i14]M. Salman Asif, Chinmay Hegde:
Phase Retrieval for Signals in Union of Subspaces. CoRR abs/1807.06222 (2018) - [i13]Chinmay Hegde:
Algorithmic Aspects of Inverse Problems Using Generative Models. CoRR abs/1810.03587 (2018) - [i12]Rahul Singh, Viraj Shah, Balaji Sesha Sarath Pokuri, Soumik Sarkar, Baskar Ganapathysubramanian, Chinmay Hegde:
Physics-aware Deep Generative Models for Creating Synthetic Microstructures. CoRR abs/1811.09669 (2018) - [i11]Viraj Shah, Chinmay Hegde:
Signal Reconstruction from Modulo Observations. CoRR abs/1812.00557 (2018) - 2017
- [j8]Mohammadreza Soltani, Chinmay Hegde:
Fast Algorithms for Demixing Sparse Signals From Nonlinear Observations. IEEE Trans. Signal Process. 65(16): 4209-4222 (2017) - [c33]Viraj Shah, Mohammadreza Soltani, Chinmay Hegde:
Reconstruction from periodic nonlinearities, with applications to HDR imaging. ACSSC 2017: 863-867 - [c32]Charlie Hubbard, Chinmay Hegde:
Parallel computing heuristics for low-rank matrix completion. GlobalSIP 2017: 764-768 - [c31]Mohammadreza Soltani, Chinmay Hegde:
Demixing structured superposition signals from periodic and aperiodic nonlinear observations. GlobalSIP 2017: 1165-1169 - [c30]Mohammadreza Soltani, Chinmay Hegde:
Stable recovery of sparse vectors from random sinusoidal feature maps. ICASSP 2017: 6384-6388 - [c29]Gauri Jagatap, Chinmay Hegde:
Fast, Sample-Efficient Algorithms for Structured Phase Retrieval. NIPS 2017: 4917-4927 - [c28]Zhanhong Jiang, Aditya Balu, Chinmay Hegde, Soumik Sarkar:
Collaborative Deep Learning in Fixed Topology Networks. NIPS 2017: 5904-5914 - [i10]Gauri Jagatap, Chinmay Hegde:
Phase Retrieval Using Structured Sparsity: A Sample Efficient Algorithmic Framework. CoRR abs/1705.06412 (2017) - [i9]Zhanhong Jiang, Aditya Balu, Chinmay Hegde, Soumik Sarkar:
Collaborative Deep Learning in Fixed Topology Networks. CoRR abs/1706.07880 (2017) - [i8]Thanh Van Nguyen, Raymond K. W. Wong, Chinmay Hegde:
A Provable Approach for Double-Sparse Coding. CoRR abs/1711.03638 (2017) - [i7]Aditya Balu, Thanh Van Nguyen, Apurva Kokate, Chinmay Hegde, Soumik Sarkar:
A Forward-Backward Approach for Visualizing Information Flow in Deep Networks. CoRR abs/1711.06221 (2017) - 2016
- [c27]Mohammadreza Soltani, Chinmay Hegde:
Demixing sparse signals from nonlinear observations. ACSSC 2016: 615-619 - [c26]Mohammadreza Soltani, Chinmay Hegde:
A fast iterative algorithm for demixing sparse signals from nonlinear observations. GlobalSIP 2016: 167-171 - [c25]Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
A Nearly-Linear Time Framework for Graph-Structured Sparsity. IJCAI 2016: 4165-4169 - [c24]Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
Fast recovery from a union of subspaces. NIPS 2016: 4394-4402 - [c23]Chinmay Hegde:
A fast algorithm for demixing signals with structured sparsity. SPCOM 2016: 1-5 - [c22]Chinmay Hegde:
Bilevel feature selection in nearly-linear time. SSP 2016: 1-5 - 2015
- [j7]Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
Fast Algorithms for Structured Sparsity. Bull. EATCS 117 (2015) - [j6]Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
Approximation Algorithms for Model-Based Compressive Sensing. IEEE Trans. Inf. Theory 61(9): 5129-5147 (2015) - [j5]Chinmay Hegde, Aswin C. Sankaranarayanan, Wotao Yin, Richard G. Baraniuk:
NuMax: A Convex Approach for Learning Near-Isometric Linear Embeddings. IEEE Trans. Signal Process. 63(22): 6109-6121 (2015) - [c21]Ludwig Schmidt, Chinmay Hegde, Piotr Indyk, Ligang Lu, Xingang Chi, Detlef Hohl:
Seismic feature extraction using steiner tree methods. ICASSP 2015: 1647-1651 - [c20]Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
A Nearly-Linear Time Framework for Graph-Structured Sparsity. ICML 2015: 928-937 - [c19]Jayadev Acharya, Ilias Diakonikolas, Chinmay Hegde, Jerry Zheng Li, Ludwig Schmidt:
Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms. PODS 2015: 249-263 - [i6]Chinmay Hegde, Oncel Tuzel, Fatih Porikli:
Efficient Upsampling of Natural Images. CoRR abs/1503.00040 (2015) - 2014
- [c18]Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
Nearly Linear-Time Model-Based Compressive Sensing. ICALP (1) 2014: 588-599 - [c17]Chinmay Hegde, Aswin C. Sankaranarayanan, Richard G. Baraniuk:
LIE operators for compressive sensing. ICASSP 2014: 2342-2346 - [c16]Ludwig Schmidt, Chinmay Hegde, Piotr Indyk, Jonathan Kane, Ligang Lu, Detlef Hohl:
Automatic fault localization using the generalized Earth Mover's distance. ICASSP 2014: 8134-8138 - [c15]Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
A fast approximation algorithm for tree-sparse recovery. ISIT 2014: 1842-1846 - [c14]Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
Approximation-Tolerant Model-Based Compressive Sensing. SODA 2014: 1544-1561 - [i5]Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
Approximation Algorithms for Model-Based Compressive Sensing. CoRR abs/1406.1579 (2014) - 2013
- [c13]Elyot Grant, Chinmay Hegde, Piotr Indyk:
Nearly optimal linear embeddings into very low dimensions. GlobalSIP 2013: 973-976 - 2012
- [j4]Chinmay Hegde, Richard G. Baraniuk:
Signal Recovery on Incoherent Manifolds. IEEE Trans. Inf. Theory 58(12): 7204-7214 (2012) - [c12]Chinmay Hegde, Richard G. Baraniuk:
SPIN: Iterative signal recovery on incoherent manifolds. ISIT 2012: 1296-1300 - [c11]Chinmay Hegde, Aswin C. Sankaranarayanan, Richard G. Baraniuk:
Near-isometric linear embeddings of manifolds. SSP 2012: 728-731 - [c10]Devin K. Grady, Mark Moll, Chinmay Hegde, Aswin C. Sankaranarayanan, Richard G. Baraniuk, Lydia E. Kavraki:
Multi-objective sensor-based replanning for a car-like robot. SSRR 2012: 1-6 - [c9]Devin K. Grady, Mark Moll, Chinmay Hegde, Aswin C. Sankaranarayanan, Richard G. Baraniuk, Lydia E. Kavraki:
Multi-robot target verification with reachability constraints. SSRR 2012: 1-6 - [i4]Chinmay Hegde, Richard G. Baraniuk:
Signal Recovery on Incoherent Manifolds. CoRR abs/1202.1595 (2012) - 2011
- [j3]Chinmay Hegde, Richard G. Baraniuk:
Sampling and Recovery of Pulse Streams. IEEE Trans. Signal Process. 59(4): 1505-1517 (2011) - [c8]Aswin C. Sankaranarayanan, Chinmay Hegde, Sriram Nagaraj, Richard G. Baraniuk:
Go with the flow: Optical flow-based transport operators for image manifolds. Allerton 2011: 1824-1831 - 2010
- [j2]Mark A. Davenport, Chinmay Hegde, Marco F. Duarte, Richard G. Baraniuk:
Joint Manifolds for Data Fusion. IEEE Trans. Image Process. 19(10): 2580-2594 (2010) - [j1]Richard G. Baraniuk, Volkan Cevher, Marco F. Duarte, Chinmay Hegde:
Model-based compressive sensing. IEEE Trans. Inf. Theory 56(4): 1982-2001 (2010) - [c7]Mark A. Davenport, Chinmay Hegde, Marco F. Duarte, Richard G. Baraniuk:
High Dimensional Data Fusion via Joint Manifold Learning. AAAI Fall Symposium: Manifold Learning and Its Applications 2010 - [c6]Stephen R. Schnelle, Jason N. Laska, Chinmay Hegde, Marco F. Duarte, Mark A. Davenport, Richard G. Baraniuk:
Texas Hold 'Em algorithms for distributed compressive sensing. ICASSP 2010: 2886-2889 - [c5]Chinmay Hegde, Richard G. Baraniuk:
Compressive sensing of a superposition of pulses. ICASSP 2010: 3934-3937 - [i3]Chinmay Hegde, Richard G. Baraniuk:
Sampling and Recovery of Pulse Streams. CoRR abs/1004.3273 (2010)
2000 – 2009
- 2009
- [c4]Chinmay Hegde, Richard G. Baraniuk:
Compressive sensing of streams of pulses. Allerton 2009: 44-51 - [c3]Marco F. Duarte, Chinmay Hegde, Volkan Cevher, Richard G. Baraniuk:
Recovery of compressible signals in unions of subspaces. CISS 2009: 175-180 - [i2]Mark A. Davenport, Chinmay Hegde, Marco F. Duarte, Richard G. Baraniuk:
A Theoretical Analysis of Joint Manifolds. CoRR abs/0901.0760 (2009) - 2008
- [c2]Volkan Cevher, Marco F. Duarte, Chinmay Hegde, Richard G. Baraniuk:
Sparse Signal Recovery Using Markov Random Fields. NIPS 2008: 257-264 - [i1]Richard G. Baraniuk, Volkan Cevher, Marco F. Duarte, Chinmay Hegde:
Model-Based Compressive Sensing. CoRR abs/0808.3572 (2008) - 2007
- [c1]Chinmay Hegde, Michael B. Wakin, Richard G. Baraniuk:
Random Projections for Manifold Learning. NIPS 2007: 641-648
Coauthor Index
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