Generative Models
Developing generative models for spatiotemporal forecasting and studying the dynamics, stability, and failure modes of iterative AI feedback.
DiffusionGenerative
Projects
Publications
A Markovian View of Iterative-Feedback Loops in Image Generative Models: Neural Resonance and Model Collapse
2026
Vibhas Kumar Vats, David Crandall, Samuel Goree • ArXiv-preprint
This paper investigates why generative models can degrade when they are repeatedly trained on AI-generated data. We introduce the idea of neural resonance, where iterative feedback pushes models toward a low-dimensional latent structure, and show the conditions under which this happens. We also analyze several model families and describe common patterns of collapse that can help guide future mitigation strategies.
DiffusionGenerative ModelsDeep LearningNeural Resonance
Geospatial Diffusion for Land Cover Imperviousness Change Forecasting
2025
Vibhas Kumar Vats, Debvrat Varshney, Bhartendu Pandey, Christa Brelsford, Philipe Dias • SIGSPATIAL
This paper presents a generative AI approach for forecasting land-use and land-cover change over time. Using a diffusion model conditioned on historical and auxiliary data, we predict future imperviousness patterns across the United States at decadal scale. Our results show that generative models can provide useful large-scale forecasts and open new directions for physically informed Earth system prediction.
Generative ModelsDiffusionGeoAILulc