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Chirag Agarwal
Chirag Agarwal
Research Scientist, Adobe; Research Affiliate, Harvard University
Verifisert e-postadresse på adobe.com - Startside
Tittel
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Sitert av
År
CoroNet: a deep network architecture for enhanced identification of COVID-19 from chest x-ray images
C Agarwal, S Khobahi, D Schonfeld, M Soltanalian
Medical Imaging 2021: Computer-Aided Diagnosis 11597, 484-490, 2021
68*2021
Sam: The sensitivity of attribution methods to hyperparameters
N Bansal, C Agarwal, A Nguyen
Proceedings of the ieee/cvf conference on computer vision and pattern …, 2020
432020
Explaining image classifiers by removing input features using generative models
C Agarwal, A Nguyen
Proceedings of the Asian Conference on Computer Vision, 2020
39*2020
Accurate segmentation of lung fields on chest radiographs using deep convolutional networks
MR Arbabshirani, AH Dallal, C Agarwal, A Patel, G Moore
SPIE Medical Imaging, 2017
392017
Towards a unified framework for fair and stable graph representation learning
C Agarwal, H Lakkaraju, M Zitnik
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial …, 2021
342021
Automatic estimation of heart boundaries and cardiothoracic ratio from chest x-ray images
AH Dallal, C Agarwal, MR Arbabshirani, A Patel, G Moore
SPIE Medical Imaging, 2017
232017
Estimating Example Difficulty using Variance of Gradients
C Agarwal, D D'souza, S Hooker
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
172020
Towards the unification and robustness of perturbation and gradient based explanations
S Agarwal, S Jabbari, C Agarwal, S Upadhyay, S Wu, H Lakkaraju
International Conference on Machine Learning, 110-119, 2021
162021
Improving robustness to adversarial examples by encouraging discriminative features
C Agarwal, A Nguyen, D Schonfeld
2019 IEEE International Conference on Image Processing (ICIP), 3801-3505, 2019
142019
Probing gnn explainers: A rigorous theoretical and empirical analysis of gnn explanation methods
C Agarwal, M Zitnik, H Lakkaraju
International Conference on Artificial Intelligence and Statistics, 8969-8996, 2022
10*2022
Convolutional neural network steganalysis's application to steganography
M Sharifzadeh, C Agarwal, M Aloraini, D Schonfeld
2017 IEEE Visual Communications and Image Processing (VCIP), 1-4, 2017
102017
Exploring counterfactual explanations through the lens of adversarial examples: A theoretical and empirical analysis
M Pawelczyk, C Agarwal, S Joshi, S Upadhyay, H Lakkaraju
International Conference on Artificial Intelligence and Statistics, 4574-4594, 2022
9*2022
Statistical sequential analysis for object-based video forgery detection
M Aloraini, M Sharifzadeh, C Agarwal, D Schonfeld
Electronic Imaging 2019 (5), 543-1-543-7, 2019
92019
Enhanced data hiding method using DWT based on Saliency model
C Agarwal, A Bose, S Maiti, N Islam, SK Sarkar
2013 IEEE International Conference on Signal Processing, Computing and …, 2013
92013
The shape and simplicity biases of adversarially robust ImageNet-trained CNNs
P Chen, C Agarwal, A Nguyen
arXiv preprint arXiv:2006.09373, 2020
8*2020
Unsupervised quantification of abdominal fat from CT images using Greedy Snakes
C Agarwal, AH Dallal, MR Arbabshirani, A Patel, G Moore
SPIE Medical Imaging, 2017
82017
Deep-url: A model-aware approach to blind deconvolution based on deep unfolded richardson-lucy network
C Agarwal, S Khobahi, A Bose, M Soltanalian, D Schonfeld
2020 IEEE International Conference on Image Processing (ICIP), 3299-3303, 2020
72020
A new parallel message-distribution technique for cost-based steganography
M Sharifzadeh, C Agarwal, M Salarian, D Schonfeld
arXiv preprint arXiv:1705.08616, 2017
72017
A tale of two long tails
D D'souza, Z Nussbaum, C Agarwal, S Hooker
arXiv preprint arXiv:2107.13098, 2021
52021
Using adipose measures from health care provider-based imaging data for discovery
EDK Cha, Y Veturi, C Agarwal, A Patel, MR Arbabshirani, ...
Journal of obesity 2018, 2018
52018
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Artikler 1–20