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Gagan Madan
Gagan Madan
Google Inc.
Verified email at google.com
Title
Cited by
Cited by
Year
Node-level differentially private graph neural networks
A Daigavane, G Madan, A Sinha, AG Thakurta, G Aggarwal, P Jain
arXiv preprint arXiv:2111.15521, 2021
382021
Block-value symmetries in probabilistic graphical models
G Madan, A Anand, P Singla
arXiv preprint arXiv:1807.00643, 2018
52018
Topic modeling using distributed word embeddings
RS Randhawa, P Jain, G Madan
arXiv preprint arXiv:1603.04747, 2016
52016
A study of autoregressive decoders for multi-tasking in computer vision
L Beyer, B Wan, G Madan, F Pavetic, A Steiner, A Kolesnikov, AS Pinto, ...
arXiv preprint arXiv:2303.17376, 2023
32023
Analyzing the Efficacy of an LLM-Only Approach for Image-based Document Question Answering
N Hegde, S Paul, G Madan, G Aggarwal
arXiv preprint arXiv:2309.14389, 2023
12023
Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components
S Pal, P Varshney, G Madan, P Jain, A Thakurta, G Aggarwal, P Shenoy, ...
International Conference on Artificial Intelligence and Statistics, 1702-1710, 2024
2024
Is it an i or an l: Test-time Adaptation of Text Line Recognition Models
D Tula, S Paul, G Madan, P Garst, R Ingle, G Aggarwal
arXiv preprint arXiv:2308.15037, 2023
2023
Weakly supervised information extraction from inscrutable handwritten document images
S Paul, G Madan, A Mishra, N Hegde, P Kumar, G Aggarwal
International Conference on Document Analysis and Recognition, 445-463, 2023
2023
Private and Efficient Meta-Learning with Low Rank and Sparse decomposition
S Pal, P Varshney, G Madan, AG Thakurta, G Aggarwal, P Shenoy, ...
2022
A Survey of Distant Supervision Methods using PGMs
G Madan
arXiv preprint arXiv:1705.03751, 2017
2017
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Articles 1–10