no code implementations • 15 Sep 2024 • Bhawna Paliwal, Deepak Saini, Mudit Dhawan, Siddarth Asokan, Nagarajan Natarajan, Surbhi Aggarwal, Pankaj Malhotra, Jian Jiao, Manik Varma
In response, we propose Cross-encoders with Joint Efficient Modeling (CROSS-JEM), a novel ranking approach that enables transformer-based models to jointly score multiple items for a query, maximizing parameter utilization.
no code implementations • 28 Feb 2024 • Anshul Mittal, Shikhar Mohan, Deepak Saini, Siddarth Asokan, Suchith C. Prabhu, Lakshya Kumar, Pankaj Malhotra, Jain jiao, Amit Singh, Sumeet Agarwal, Soumen Chakrabarti, Purushottam Kar, Manik Varma
The paper notices that in these settings, it is much more effective to use graph data to regularize encoder training than to implement a GCN.
no code implementations • 2 Jun 2023 • Siddarth Asokan, Nishanth Shetty, Aadithya Srikanth, Chandra Sekhar Seelamantula
Generative adversarial networks (GANs) comprise a generator, trained to learn the underlying distribution of the desired data, and a discriminator, trained to distinguish real samples from those output by the generator.
no code implementations • 1 Jun 2023 • Siddarth Asokan, Chandra Sekhar Seelamantula
We show analytically, via the least-squares (LSGAN) and Wasserstein (WGAN) GAN variants, that the discriminator optimization problem is one of interpolation in $n$-dimensions.
2 code implementations • CVPR 2023 • Siddarth Asokan, Chandra Sekhar Seelamantula
We demonstrate the efficacy of the Spider approach on DCGAN, conditional GAN, PGGAN, StyleGAN2 and StyleGAN3.
1 code implementation • NeurIPS 2020 • Siddarth Asokan, Chandra Sekhar Seelamantula
Generative adversarial networks (GANs) were originally envisioned as unsupervised generative models that learn to follow a target distribution.