Nishanth Shetty — accepted AI-conference papers

2025

Monte Carlo Score Matching for Image Generation

Nishanth Shetty, Chandra Sekhar Seelamantula

ICASSP 2025accepted

Score-based models are state-of-the-art generative models for image generation. We propose a novel loss namely the Monte Carlo Score Matching (MCSM) loss as an approximation of the original score matching loss. MCSM leverages a Taylor-series expansion of the score function to approximate the expensi…

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2024

Momentum-Imbued Langevin Dynamics (MILD) for Faster Sampling

Nishanth Shetty, Manikanta Bandla, Nishit Neema, Siddarth Asokan, Chandra Sekhar Seelamantula

ICASSP 2024accepted

Score-based generative models have emerged as the state-of-the-art in generative modeling. In this paper, we introduce a novel sampling scheme that can be combined with pre-trained score-based diffusion models to speed up sampling by a factor of two to five in terms of the number of function evaluat…

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