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Found 11 papers, 9 papers with code
SE(3) Equivariant Augmented Coupling Flows
Coupling normalizing flows allow for fast sampling and density evaluation, making them the tool of choice for probabilistic modeling of physical systems.
normflows: A PyTorch Package for Normalizing Flows
It allows to build normalizing flow models from a suite of base distributions, flow layers, and neural networks.
Flow Annealed Importance Sampling Bootstrap
Normalizing flows are tractable density models that can approximate complicated target distributions, e. g. Boltzmann distributions of physical systems.
Probing the Robustness of Independent Mechanism Analysis for Representation Learning
One aim of representation learning is to recover the original latent code that generated the data, a task which requires additional information or inductive biases.
AutoML Two-Sample Test
Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts.
Bootstrap Your Flow
Normalizing flows are flexible, parameterized distributions that can be used to approximate expectations from intractable distributions via importance sampling.
Resampling Base Distributions of Normalizing Flows
Normalizing flows are a popular class of models for approximating probability distributions.
Ranked #47 on
Image Generation
on CIFAR-10
(bits/dimension metric)
Independent mechanism analysis, a new concept?
Specifically, our approach is motivated by thinking of each source as independently influencing the mixing process.
Gradient-based tuning of Hamiltonian Monte Carlo hyperparameters
Existing approaches for automating this task either optimise a proxy for mixing speed or consider the HMC chain as an implicit variational distribution and optimize a tractable lower bound that is too loose to be useful in practice.
A machine learning route between band mapping and band structure
Electronic band structure (BS) and crystal structure are the two complementary identifiers of solid state materials.
Data Analysis, Statistics and Probability Materials Science Computational Physics
Multidimensional Contrast Limited Adaptive Histogram Equalization
Contrast enhancement is an important preprocessing technique for improving the performance of downstream tasks in image processing and computer vision.
