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9 results for microcanonical

New microcanonical models approximate non-Gaussian processes with long-range correlations.

problem Approximating non-Gaussian stationary processes with long-range correlations.
method Microcanonical models conditioned by energy vector, gradient descent, multiscale energy vectors.
result Microcanonical gradient descent processes converge and capture sparsity.

A new method called MCLMC avoids dissipation in sampling from canonical distributions.

problem Sampling from canonical distributions without dissipation.
method Microcanonical Langevin Monte Carlo (MCLMC) as a dissipation-free system of SDE.
result MCLMC converges faster than HMC for lattice φ^4 models.

Bayesian method infers network communities without violating imposed patterns.

problem Characterize hidden structure of networks composed of modules.
method Nonparametric Bayesian inference of microcanonical stochastic block model.
result Inference of hierarchical modular structure with deep Bayesian hierarchies and efficient algorithm.

Wavelet scattering spectra model non-Gaussian time-series, proving scale invariance for self-similar processes.

problem Modeling non-Gaussian time-series with stationary increments.
method Complex wavelet transform for scale variations, joint correlation matrix for scale dependencies, second wavelet transform for diagonalization, maximum entropy models conditioned by scattering spectra coefficients.
result Scattering spectra of self-similar processes are scale invariant, allowing statistical testing and generation of new time-series.

Ray tracing sampler improves neural network sampling efficiency and resilience.

problem Sampling neural network posterior distributions efficiently and robustly.
method Markov Chain Monte Carlo using ray tracing through likelihood space.
result Significantly higher resilience to gradient heating compared to HMC.

Study on convergence of exponential probability measures with applications to maximum entropy models and SGLD.

problem Characterizing the limit of probability measures with exponential densities as temperature approaches zero.
method Quantitative bounds on Wasserstein distance using geometric measure theory tools.
result Established quantitative convergence results for norm-like potentials under invertibility conditions.

A weak law of large numbers is established for a sequence of systems of N classical point particles with logarithmic pair potential in $\bbR^n$, or $\bbS^n$, $n\in \bbN$, which are distributed according to the configurational microcanonical measure δ(EH)δ(E-H), or rather some regularization thereof, where H is the configu…

2000-02-22abs ↗pdf ↗