no code implementations • 22 Dec 2019 • Kartik Sharma, Ashutosh Aggarwal, Tanay Singhania, Deepak Gupta, Ashish Khanna
Previously, steganography has been combined with cryptography and neural networks separately.
1 code implementation • 25 Dec 2021 • Kartik Sharma, Samidha Verma, Sourav Medya, Arnab Bhattacharya, Sayan Ranu
In this work, we study this problem and show that GNNs remain vulnerable even when the downstream task and model are unknown.
no code implementations • 7 Jul 2022 • Kartik Sharma, Mohit Raghavendra, Yeon Chang Lee, Anand Kumar M, Srijan Kumar
Modeling such dynamics of signed networks is crucial to understanding the evolution of polarization in the network and enabling effective prediction of the signed structure (i. e., link signs and signed weights) in the future.
1 code implementation • 8 Dec 2022 • Kartik Sharma, Yeon-Chang Lee, Sivagami Nambi, Aditya Salian, Shlok Shah, Sang-Wook Kim, Srijan Kumar
In this survey, we first identify 80 papers on GNN-based SocialRS after annotating 2151 papers by following the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta-Analysis).
1 code implementation • CVPR 2023 • Harsh Rangwani, Lavish Bansal, Kartik Sharma, Tejan Karmali, Varun Jampani, R. Venkatesh Babu
We find that one reason for degradation is the collapse of latents for each class in the $\mathcal{W}$ latent space.
Ranked #1 on Conditional Image Generation on ImageNet-LT
no code implementations • 2 Jun 2023 • Jaykumar Kakkad, Jaspal Jannu, Kartik Sharma, Charu Aggarwal, Sourav Medya
Graph neural networks (GNNs) are powerful graph-based deep-learning models that have gained significant attention and demonstrated remarkable performance in various domains, including natural language processing, drug discovery, and recommendation systems.
no code implementations • 26 Feb 2024 • Gaurav Verma, MinJe Choi, Kartik Sharma, Jamelle Watson-Daniels, Sejoon Oh, Srijan Kumar
It is desirable to understand the roles of these two modules in modeling domain-specific visual attributes to inform the design of future models and streamline the interpretability efforts on the current models.
no code implementations • EMNLP (CMCL) 2020 • Kartik Sharma, Richard Futrell, Samar Husain
In this work, we investigate whether the order and relative distance of preverbal dependents in Hindi, an SOV language, is affected by factors motivated by efficiency considerations during comprehension/production.
no code implementations • NAACL (CMCL) 2021 • Kartik Sharma, Niyati Bafna, Samar Husain
The models differ in their use of prior context during the prediction process – the context is either noisy or noise-free.