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Found 6 papers, 1 papers with code
PrACTiS: Perceiver-Attentional Copulas for Time Series
Transformers incorporating copula structures have demonstrated remarkable performance in time series prediction.
Semi-Empirical Objective Functions for Neural MCMC Proposal Optimization
Current objective functions used for training neural MCMC proposal distributions implicitly rely on architectural restrictions to yield sensible optimization results, which hampers the development of highly expressive neural MCMC proposal architectures.
Semi-Empirical Objective Functions for MCMC Proposal Optimization
Current objective functions used for training neural MCMC proposal distributions implicitly rely on architectural restrictions to yield sensible optimization results, which hampers the development of highly expressive neural MCMC proposal architectures.
Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows
We introduce Projected Latent Markov Chain Monte Carlo (PL-MCMC), a technique for sampling from the high-dimensional conditional distributions learned by a normalizing flow.
Perception-Distortion Trade-off with Restricted Boltzmann Machines
In this work, we introduce a new procedure for applying Restricted Boltzmann Machines (RBMs) to missing data inference tasks, based on linearization of the effective energy function governing the distribution of observations.
Machine Learning for the Zwicky Transient Facility
The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night.
Instrumentation and Methods for Astrophysics
