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Found 8 papers, 5 papers with code
Amortizing intractable inference in diffusion models for vision, language, and control
Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors in downstream tasks poses an intractable posterior inference problem.
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem in science.
Improved off-policy training of diffusion samplers
We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function.
HyperShot: Few-Shot Learning by Kernel HyperNetworks
Few-shot models aim at making predictions using a minimal number of labeled examples from a given task.
Non-Gaussian Gaussian Processes for Few-Shot Regression
This makes the GP posterior locally non-Gaussian, therefore we name our method Non-Gaussian Gaussian Processes (NGGPs).
Flow-based SVDD for anomaly detection
We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools.
OneFlow: One-class flow for anomaly detection based on a minimal volume region
We propose OneFlow - a flow-based one-class classifier for anomaly (outlier) detection that finds a minimal volume bounding region.
Data adaptation in HANDY economy-ideology model
However, despite their ability to better forecasting, the problem of an appropriate fitting ground truth data to those, high-dimensional and nonlinear models seems to be inevitable.
