5 citations · 5 across the 4 of their papers we have counts for
6 papers
Pre-Generating Multi-Difficulty PDE Data for Few-Shot Neural PDE Solvers
Naman Choudhary, Vedant Singh, Ameet Talwalkar +3
A key aspect of learned partial differential equation (PDE) solvers is that the main cost often comes from generating training data with classical solvers rather than learning the…
CompactPrompt: A Unified Pipeline for Prompt Data Compression in LLM Workflows
Joong Ho Choi, Jiayang Zhao, Jeel Shah +5
Large Language Models (LLMs) deliver powerful reasoning and generation capabilities but incur substantial run-time costs when operating in agentic workflows that chain together len…
On Conditioning the Input Noise for Controlled Image Generation with Diffusion Models
Vedant Singh, Surgan Jandial, Ayush Chopra +3
Conditional image generation has paved the way for several breakthroughs in image editing, generating stock photos and 3-D object generation. This continues to be a significant are…
On Saliency Maps and Adversarial Robustness
Puneet Mangla, Vedant Singh, Vineeth N Balasubramanian
A Very recent trend has emerged to couple the notion of interpretability and adversarial robustness, unlike earlier efforts which solely focused on good interpretations or robustne…
On the benefits of defining vicinal distributions in latent space
Puneet Mangla, Vedant Singh, Shreyas Jayant Havaldar +1
The vicinal risk minimization (VRM) principle is an empirical risk minimization (ERM) variant that replaces Dirac masses with vicinal functions. There is strong numerical and theor…
Auto-Encoding Progressive Generative Adversarial Networks For 3D Multi Object Scenes
Vedant Singh, Manan Oza, Himanshu Vaghela +1
3D multi object generative models allow us to synthesize a large range of novel 3D multi object scenes and also identify objects, shapes, layouts and their positions. But multi obj…