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cs.LG2026
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…
cs.LG2025
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…
cs.LG2023
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…