Bayesian Inference

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

7 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 747 papers with code (2,226 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Weight Uncertainty in Neural Networks
  20 May 2015 38 repositories listed Syntology ran 12 of 15 samples · 3 unverified · 7 pointer-only (licence)
  We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop.
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
  6 Jun 2015 29 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 4 pointer-only (licence)
  In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost.
• Variational Autoencoders for Collaborative Filtering
  16 Feb 2018 18 repositories listed Syntology ran 2 of 21 samples · 19 unverified · 1 pointer-only (licence)
  This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research.
• Semi-Supervised Learning with Deep Generative Models
  20 Jun 2014 18 repositories listed Syntology ran 0 of 7 samples · 7 unverified
  The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis.
• Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
  16 Aug 2016 15 repositories listed Syntology ran 0 of 5 samples · 5 unverified
  We propose a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization.
• Bayesian regression and Bitcoin
  6 Oct 2014 15 repositories listed Syntology ran 10 of 13 samples · 3 unverified · 8 pointer-only (licence)
  In this paper, we discuss the method of Bayesian regression and its efficacy for predicting price variation of Bitcoin, a recently popularized virtual, cryptographic currency.
• A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
  16 Dec 2015 14 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  Recent results at the intersection of Bayesian modelling and deep learning offer a Bayesian interpretation of common deep learning techniques such as dropout.
• Variational Dropout and the Local Reparameterization Trick
  8 Jun 2015 12 repositories listed Syntology ran 5 of 6 samples · 1 unverified · 5 pointer-only (licence)
  Our method allows inference of more flexibly parameterized posteriors; specifically, we propose variational dropout, a generalization of Gaussian dropout where the dropout rates are learned, often leading to better…
• Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows
  18 May 2018 10 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  We present Sequential Neural Likelihood (SNL), a new method for Bayesian inference in simulator models, where the likelihood is intractable but simulating data from the model is possible.
• Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference
  15 Jun 2018 9 repositories listed
  On multiple datasets in supervised learning settings (MNIST, CIFAR-10, CIFAR-100), this variational inference method achieves performances equivalent to frequentist inference in identical architectures, while the two…
• A Simple Baseline for Bayesian Uncertainty in Deep Learning
  7 Feb 2019 8 repositories listed Syntology ran 6 of 17 samples · 11 unverified
  We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning.
• TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
  5 Jul 2022 7 repositories listed Syntology ran 1 of 4 samples · 3 unverified · 1 pointer-only (licence)
  We present TabPFN, a trained Transformer that can do supervised classification for small tabular datasets in less than a second, needs no hyperparameter tuning and is competitive with state-of-the-art classification…
• Inferring COVID-19 spreading rates and potential change points for case number forecasts
  2 Apr 2020 7 repositories listed
  As COVID-19 is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies.
• Deep Neural Networks as Gaussian Processes
  1 Nov 2017 7 repositories listed
  As such, previous work has not identified that these kernels can be used as covariance functions for GPs and allow fully Bayesian prediction with a deep neural network.
• A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
  8 Jan 2019 6 repositories listed Syntology ran 4 of 14 samples · 10 unverified · 1 pointer-only (licence)
  In this paper, Bayesian Convolutional Neural Network (BayesCNN) using Variational Inference is proposed, that introduces probability distribution over the weights.
• Stochastic Backpropagation and Approximate Inference in Deep Generative Models
  16 Jan 2014 6 repositories listed
  We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning.
• MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics
  19 Sep 2008 6 repositories listed Syntology ran 0 of 5 samples · 5 unverified
  We present further development and the first public release of our multimodal nested sampling algorithm, called MultiNest.
• Neural Clustering Processes
  28 Dec 2018 5 repositories listed
  Probabilistic clustering models (or equivalently, mixture models) are basic building blocks in countless statistical models and involve latent random variables over discrete spaces.
• Rule-based Bayesian regression
  2 Aug 2020 4 repositories listed
  We introduce a novel rule-based approach for handling regression problems.
• Fast and Accurate Forecasting of COVID-19 Deaths Using the SIkJ$α$ Model
  10 Jul 2020 4 repositories listed
  Many of these methods are based on traditional epidemiological model which rely on simulations or Bayesian inference to simultaneously learn many parameters at a time.
• Probabilistic Autoencoder
  9 Jun 2020 4 repositories listed Syntology ran 1 of 13 samples · 12 unverified
  The PAE is fast and easy to train and achieves small reconstruction errors, high sample quality, and good performance in downstream tasks.
• Variational Bayesian Monte Carlo
  12 Oct 2018 4 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  We introduce here a novel sample-efficient inference framework, Variational Bayesian Monte Carlo (VBMC).
• SoftCVI: Contrastive variational inference with self-generated soft labels
  22 Jul 2024 3 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Here, we introduce Soft Contrastive Variational Inference (SoftCVI), which allows a family of variational objectives to be derived through a contrastive estimation framework.
• The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison
  21 May 2024 3 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  First, we demonstrate the application of this paradigm on a simulated cosmic shear analysis for a Stage IV survey in 37- and 39-dimensional parameter spaces, comparing ΛCDM and a dynamical dark energy model…
• Learning by example: fast reliability-aware seismic imaging with normalizing flows
  13 Apr 2021 3 repositories listed
  To arrive at this result, we train the NF on pairs of low- and high-fidelity migrated images.
• Variational Inference for Deblending Crowded Starfields
  4 Feb 2021 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  In images collected by astronomical surveys, stars and galaxies often overlap visually.
• Bayesian Inference of Minimally Complex Models with Interactions of Arbitrary Order
  2 Aug 2020 3 repositories listed
  Finally, because MCMs have interactions of any order, they can reveal the presence of important high-order dependencies in the data, providing a new approach to explore high-order dependencies in complex systems.
• Multi-marginal optimal transport and probabilistic graphical models
  25 Jun 2020 3 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  We study multi-marginal optimal transport problems from a probabilistic graphical model perspective.
• BayesFlow: Learning complex stochastic models with invertible neural networks
  13 Mar 2020 3 repositories listed
  In addition, our method incorporates a summary network trained to embed the observed data into maximally informative summary statistics.
• Neural Tangents: Fast and Easy Infinite Neural Networks in Python
  5 Dec 2019 3 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  Neural Tangents is a library designed to enable research into infinite-width neural networks.

Syntology lines on 20 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.