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Found 15 papers, 6 papers with code
Stochastic Localization via Iterative Posterior Sampling
Building upon score-based learning, new interest in stochastic localization techniques has recently emerged.
Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy
Extracting consistent statistics between relevant free-energy minima of a molecular system is essential for physics, chemistry and biology.
Balanced Training of Energy-Based Models with Adaptive Flow Sampling
Energy-based models (EBMs) are versatile density estimation models that directly parameterize an unnormalized log density.
On Sampling with Approximate Transport Maps
Transport maps can ease the sampling of distributions with non-trivial geometries by transforming them into distributions that are easier to handle.
Local-Global MCMC kernels: the best of both worlds
Recent works leveraging learning to enhance sampling have shown promising results, in particular by designing effective non-local moves and global proposals.
Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods
Normalizing flows can generate complex target distributions and thus show promise in many applications in Bayesian statistics as an alternative or complement to MCMC for sampling posteriors.
Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks
In the feature-learning regime, this dual formulation justifies using a two time-scale gradient ascent-descent (GDA) training algorithm in which one updates concurrently the particles in the sample space and the neurons in the parameter space of the energy.
On the interplay between data structure and loss function in classification problems
One of the central puzzles in modern machine learning is the ability of heavily overparametrized models to generalize well.
Practical Phase Retrieval: Low-Photon Holography with Untrained Priors
To the best of our knowledge, this is the first work to consider a dataset-free machine learning approach for holographic phase retrieval.
Phase Retrieval with Holography and Untrained Priors: Tackling the Challenges of Low-Photon Nanoscale Imaging
Phase retrieval is the inverse problem of recovering a signal from magnitude-only Fourier measurements, and underlies numerous imaging modalities, such as Coherent Diffraction Imaging (CDI).
Mean-field inference methods for neural networks
We also provide references for past and current directions of research on neural networks relying on mean-field methods.
Entropy and mutual information in models of deep neural networks
We examine a class of deep learning models with a tractable method to compute information-theoretic quantities.
A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines
Restricted Boltzmann machines (RBMs) are energy-based neural-networks which are commonly used as the building blocks for deep architectures neural architectures.
Inferring Sparsity: Compressed Sensing using Generalized Restricted Boltzmann Machines
In this work, we consider compressed sensing reconstruction from $M$ measurements of $K$-sparse structured signals which do not possess a writable correlation model.
Training Restricted Boltzmann Machines via the Thouless-Anderson-Palmer Free Energy
Restricted Boltzmann machines are undirected neural networks which have been shown to be effective in many applications, including serving as initializations for training deep multi-layer neural networks.
