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cs.LG2026
Symbolic Density Estimators for Unnormalized Distributions
Vikas Kanaujia, Riyansha Singh, Shashank Sharma +1
Estimating the symbolic or analytical form of probability density functions (PDFs) from observed samples is a fundamental challenge in statistical and computational modelling. This…
cs.LG2026
FUND: Density Flow for Sampling Unnormalised Distributions
Vikas Kanaujia, Vipul Arora
Efficient sampling from Boltzmann distributions is central to modelling complex physical systems. Markov Chain Monte Carlo (MCMC) methods suffer from critical slowing down, high au…
cs.LG2026
SIMANF: Sample Free Learning of Unnormalized Distributions via Simulated Annealing in Normalizing Flows
Vikas Kanaujia
Efficiently learning and sampling from high dimensional, multimodal unnormalized distributions without target samples remains a challenging problem. Although normalizing flows can…