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Found 8 papers, 1 papers with code
Robust Inverse Graphics via Probabilistic Inference
How do we infer a 3D scene from a single image in the presence of corruptions like rain, snow or fog?
Training Chain-of-Thought via Latent-Variable Inference
Large language models (LLMs) solve problems more accurately and interpretably when instructed to work out the answer step by step using a ``chain-of-thought'' (CoT) prompt.
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images
The problem of inferring object shape from a single 2D image is underconstrained.
MCMC Should Mix: Learning Energy-Based Model with Flow-Based Backbone
However, MCMC sampling of EBMs in high-dimensional data space is generally not mixing, because the energy function, which is usually parametrized by deep network, is highly multi-modal in the data space.
MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC
Learning energy-based model (EBM) requires MCMC sampling of the learned model as an inner loop of the learning algorithm.
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware
Markov chain Monte Carlo (MCMC) is widely regarded as one of the most important algorithms of the 20th century.
NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
Hamiltonian Monte Carlo is a powerful algorithm for sampling from difficult-to-normalize posterior distributions.
Length bias in Encoder Decoder Models and a Case for Global Conditioning
Encoder-decoder networks are popular for modeling sequences probabilistically in many applications.
