14 citations · 42 across the 23 of their papers we have counts for
9 papers · 1 filter
Generalized Score Matching for Parameter Estimation on Convex Domains
Nishanth Shetty, Saisuchith Mahajan, Chandra Sekhar Seelamantula
Maximum likelihood (ML) estimation is a principled and statistically efficient approach for learning probabilistic models. However, for unnormalized models, ML estimation requires…
Dale meets Langevin: A Multiplicative Denoising Diffusion Model
Nishanth Shetty, Madhava Prasath, Chandra Sekhar Seelamantula
Exponentiated gradient descent (EGD), a biologically motivated optimisation algorithm that respects Dale's law, produces log-normally distributed synaptic weights at convergence, i…
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…
Wavelet Design in a Learning Framework
Dhruv Jawali, Abhishek Kumar, Chandra Sekhar Seelamantula
Wavelets have proven to be highly successful in several signal and image processing applications. Wavelet design has been an active field of research for over two decades, with the…
Learning Generative Prior with Latent Space Sparsity Constraints
Vinayak Killedar, Praveen Kumar Pokala, Chandra Sekhar Seelamantula
We address the problem of compressed sensing using a deep generative prior model and consider both linear and learned nonlinear sensing mechanisms, where the nonlinear one involves…
Quantized Proximal Averaging Network for Analysis Sparse Coding
Kartheek Kumar Reddy Nareddy, Mani Madhoolika Bulusu, Praveen Kumar Pokala +1
We solve the analysis sparse coding problem considering a combination of convex and non-convex sparsity promoting penalties. The multi-penalty formulation results in an iterative a…