2 citations · 5 across the 7 of their papers we have counts for
7 papers
Extreme Meta-Classification for Large-Scale Zero-Shot Retrieval
Sachin Yadav, Deepak Saini, Anirudh Buvanesh +6
We develop accurate and efficient solutions for large-scale retrieval tasks where novel (zero-shot) items can arrive continuously at a rapid pace. Conventional Siamese-style approa…
CROSS-JEM: Accurate and Efficient Cross-encoders for Short-text Ranking Tasks
Bhawna Paliwal, Deepak Saini, Mudit Dhawan +6
Ranking a set of items based on their relevance to a given query is a core problem in search and recommendation. Transformer-based ranking models are the state-of-the-art approache…
Graph Regularized Encoder Training for Extreme Classification
Anshul Mittal, Shikhar Mohan, Deepak Saini +10
Deep extreme classification (XC) aims to train an encoder architecture and an accompanying classifier architecture to tag a data point with the most relevant subset of labels from…
Insights into Closed-form IPM-GAN Discriminator Guidance for Diffusion Modeling
Aadithya Srikanth, Siddarth Asokan, Nishanth Shetty +1
Diffusion models are a state-of-the-art generative modeling framework that transform noise to images via Langevin sampling, guided by the score, which is the gradient of the logari…
Data Interpolants -- That's What Discriminators in Higher-order Gradient-regularized GANs Are
Siddarth Asokan, Chandra Sekhar Seelamantula
We consider the problem of optimizing the discriminator in generative adversarial networks (GANs) subject to higher-order gradient regularization. We show analytically, via the lea…
Spider GAN: Leveraging Friendly Neighbors to Accelerate GAN Training
Siddarth Asokan, Chandra Sekhar Seelamantula
Training Generative adversarial networks (GANs) stably is a challenging task. The generator in GANs transform noise vectors, typically Gaussian distributed, into realistic data suc…
Teaching a GAN What Not to Learn
Siddarth Asokan, Chandra Sekhar Seelamantula
Generative adversarial networks (GANs) were originally envisioned as unsupervised generative models that learn to follow a target distribution. Variants such as conditional GANs, a…