Variational Inference

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

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

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

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 880 papers with code (2,274 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.

• Auto-Encoding Variational Bayes
  20 Dec 2013 144 repositories listed Syntology ran 112 of 199 samples · 87 unverified · 103 pointer-only (licence)
  First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods.
• Adversarial Autoencoders
  18 Nov 2015 29 repositories listed Syntology ran 8 of 12 samples · 4 unverified · 9 pointer-only (licence)
  In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the…
• Variational Inference with Normalizing Flows
  21 May 2015 17 repositories listed Syntology ran 19 of 37 samples · 18 unverified · 12 pointer-only (licence)
  The choice of approximate posterior distribution is one of the core problems in variational inference.
• 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.
• 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.
• Topic Modeling in Embedding Spaces
  8 Jul 2019 12 repositories listed Syntology ran 3 of 13 samples · 10 unverified
  To this end, we develop the Embedded Topic Model (ETM), a generative model of documents that marries traditional topic models with word embeddings.
• PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows
  28 Jun 2019 12 repositories listed Syntology ran 2 of 33 samples · 31 unverified
  Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes and the second level is the distribution of points given a shape.
• Learning Latent Dynamics for Planning from Pixels
  12 Nov 2018 9 repositories listed Syntology ran 2 of 7 samples · 5 unverified · 1 pointer-only (licence)
  Planning has been very successful for control tasks with known environment dynamics.
• 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…
• Gaussian Processes for Big Data
  26 Sep 2013 9 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  We introduce stochastic variational inference for Gaussian process models.
• Neural Spline Flows
  10 Jun 2019 8 repositories listed Syntology ran 1 of 3 samples · 2 unverified
  A normalizing flow models a complex probability density as an invertible transformation of a simple base density.
• Variational Continual Learning
  29 Oct 2017 8 repositories listed Syntology ran 3 of 4 samples · 1 unverified · 3 pointer-only (licence)
  This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks.
• Doubly Stochastic Variational Inference for Deep Gaussian Processes
  24 May 2017 8 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 1 pointer-only (licence)
  Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice.
• Improving Variational Inference with Inverse Autoregressive Flow
  15 Jun 2016 8 repositories listed Syntology ran 9 of 23 samples · 14 unverified · 3 pointer-only (licence)
  The framework of normalizing flows provides a general strategy for flexible variational inference of posteriors over latent variables.
• Sliced Score Matching: A Scalable Approach to Density and Score Estimation
  17 May 2019 7 repositories listed
  However, it has been so far limited to simple, shallow models or low-dimensional data, due to the difficulty of computing the Hessian of log-density functions.
• FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
  2 Oct 2018 7 repositories listed Syntology ran 4 of 5 samples · 1 unverified
  The result is a continuous-time invertible generative model with unbiased density estimation and one-pass sampling, while allowing unrestricted neural network architectures.
• Unsupervised Data Imputation via Variational Inference of Deep Subspaces
  8 Mar 2019 6 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  In this work, we introduce a general probabilistic model that describes sparse high dimensional imaging data as being generated by a deep non-linear embedding.
• Multi-Object Representation Learning with Iterative Variational Inference
  1 Mar 2019 6 repositories listed Syntology ran 1 of 6 samples · 5 unverified
  Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities.
• 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.
• Variational Approaches for Auto-Encoding Generative Adversarial Networks
  15 Jun 2017 6 repositories listed
  In this paper, we develop a principle upon which auto-encoders can be combined with generative adversarial networks by exploiting the hierarchical structure of the generative model.
• Autoencoding Variational Inference For Topic Models
  4 Mar 2017 6 repositories listed Syntology ran 1 of 8 samples · 7 unverified · 1 pointer-only (licence)
  A promising approach to address this problem is autoencoding variational Bayes (AEVB), but it has proven diffi- cult to apply to topic models in practice.
• Variational Inference: A Review for Statisticians
  4 Jan 2016 6 repositories listed
  One of the core problems of modern statistics is to approximate difficult-to-compute probability densities.
• Neural Variational Inference for Text Processing
  19 Nov 2015 6 repositories listed Syntology ran 1 of 4 samples · 3 unverified · 1 pointer-only (licence)
  We validate this framework on two very different text modelling applications, generative document modelling and supervised question answering.
• Pathfinder: Parallel quasi-Newton variational inference
  9 Aug 2021 5 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  Pathfinder returns draws from the approximation with the lowest estimated Kullback-Leibler (KL) divergence to the true posterior.
• A Probabilistic Formulation of Unsupervised Text Style Transfer
  10 Feb 2020 5 repositories listed Syntology ran 18 of 25 samples · 7 unverified · 12 pointer-only (licence)
  Across all style transfer tasks, our approach yields substantial gains over state-of-the-art non-generative baselines, including the state-of-the-art unsupervised machine translation techniques that our approach…
• Bayesian Neural Networks With Maximum Mean Discrepancy Regularization
  2 Mar 2020 4 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  Bayesian Neural Networks (BNNs) are trained to optimize an entire distribution over their weights instead of a single set, having significant advantages in terms of, e.
• Scalable Gradients for Stochastic Differential Equations
  5 Jan 2020 4 repositories listed Syntology ran 1 of 5 samples · 4 unverified · 2 pointer-only (licence)
  The adjoint sensitivity method scalably computes gradients of solutions to ordinary differential equations.
• Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning
  1 Jul 2019 4 repositories listed Syntology ran 1 of 12 samples · 11 unverified · 2 pointer-only (licence)
  The Radial BNN is motivated by avoiding a sampling problem in 'mean-field' variational inference (MFVI) caused by the so-called 'soap-bubble' pathology of multivariate Gaussians.
• Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
  19 Jun 2019 4 repositories listed
  Semantic dependency parsing aims to identify semantic relationships between words in a sentence that form a graph.
• 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).

Syntology lines on 25 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.