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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…
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…
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…
SCORENF: Score-based Normalizing Flows for Sampling Unnormalized distributions
Vikas Kanaujia, Vipul Arora
Unnormalized probability distributions are central to modeling complex physical systems across various scientific domains. Traditional sampling methods, such as Markov Chain Monte…
AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learning
Vikas Kanaujia, Mathias S. Scheurer, Vipul Arora
Deep generative models complement Markov-chain-Monte-Carlo methods for efficiently sampling from high-dimensional distributions. Among these methods, explicit generators, such as N…