Normalising Flows

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

No dataset record in the archive lists this task.

Subtasks archive 2025-07-28

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

Most implemented papers archive 2025-07-28

26 shown of 26 papers with code (49 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.

• Block Neural Autoregressive Flow
  9 Apr 2019 4 repositories listed Syntology ran 2 of 4 samples · 2 unverified
  Recently, as an alternative to hand-crafted bijections, Huang et al.
• Deep Structural Causal Models for Tractable Counterfactual Inference
  11 Jun 2020 3 repositories listed Syntology ran 12 of 20 samples · 8 unverified · 3 pointer-only (licence)
  We formulate a general framework for building structural causal models (SCMs) with deep learning components.
• Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows
  30 Sep 2019 3 repositories listed
  We show that normalising flows become pathological when used to model targets whose supports have complicated topologies.
• MoGlow: Probabilistic and controllable motion synthesis using normalising flows
  16 May 2019 3 repositories listed Syntology ran 1 of 2 samples · 1 unverified · 1 pointer-only (licence)
  Data-driven modelling and synthesis of motion is an active research area with applications that include animation, games, and social robotics.
• OverFlow: Putting flows on top of neural transducers for better TTS
  13 Nov 2022 2 repositories listed
  Neural HMMs are a type of neural transducer recently proposed for sequence-to-sequence modelling in text-to-speech.
• Implicit Weight Uncertainty in Neural Networks
  3 Nov 2017 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Modern neural networks tend to be overconfident on unseen, noisy or incorrectly labelled data and do not produce meaningful uncertainty measures.
• Communicating Likelihoods with Normalising Flows
  13 Feb 2025 1 repository listed
  We present a machine-learning-based workflow to model an unbinned likelihood from its samples.
• Marginal Causal Flows for Validation and Inference
  2 Nov 2024 1 repository listed Syntology ran 0 of 5 samples · 5 unverified · 5 pointer-only (licence)
  Investigating the marginal causal effect of an intervention on an outcome from complex data remains challenging due to the inflexibility of employed models and the lack of complexity in causal benchmark datasets, which…
• emuflow: Normalising Flows for Joint Cosmological Analysis
  2 Sep 2024 1 repository listed
  In this paper, we show that this can be achieved by emulating marginal posterior distributions via normalising flows.
• Linear combinations of latents in generative models: subspaces and beyond
  16 Aug 2024 1 repository listed Syntology ran 2 of 3 samples · 1 unverified
  Sampling from generative models has become a crucial tool for applications like data synthesis and augmentation.
• Normalizing Flow-based Differentiable Particle Filters
  3 Mar 2024 1 repository listed
  Recently, there has been a surge of interest in incorporating neural networks into particle filters, e.
• Flexible Tails for Normalising Flows, with Application to the Modelling of Financial Return Data
  1 Nov 2023 1 repository listed
  We propose a transformation capable of altering the tail properties of a distribution, motivated by extreme value theory, which can be used as a layer in a normalizing flow to approximate multivariate heavy tailed…
• Probabilistic Classification by Density Estimation Using Gaussian Mixture Model and Masked Autoregressive Flow
  16 Oct 2023 1 repository listed
  A family of density estimators is mixture models, such as Gaussian Mixture Model (GMM) by expectation maximization.
• NEnv: Neural Environment Maps for Global Illumination
  26 Jul 2023 1 repository listed
  We propose NEnv, a deep-learning fully-differentiable method, capable of compressing and learning to sample from a single environment map.
• Decorrelation using Optimal Transport
  11 Jul 2023 1 repository listed
  Being able to decorrelate a feature space from protected attributes is an area of active research and study in ethics, fairness, and also natural sciences.
• HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution Estimation
  11 May 2023 1 repository listed Syntology ran 3 of 6 samples · 3 unverified
  Monocular 3D human pose and shape estimation is an ill-posed problem since multiple 3D solutions can explain a 2D image of a subject.
• Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data
  25 Nov 2021 1 repository listed
  We address this gap by introducing variational Gibbs inference (VGI), a new general-purpose method to estimate the parameters of statistical models from incomplete data.
• Bootstrap Your Flow
  22 Nov 2021 1 repository listed
  Normalizing flows are flexible, parameterized distributions that can be used to approximate expectations from intractable distributions via importance sampling.
• Sinusoidal Flow: A Fast Invertible Autoregressive Flow
  26 Oct 2021 1 repository listed
  Normalising flows offer a flexible way of modelling continuous probability distributions.
• Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows
  6 Jul 2020 1 repository listed
  In this work, we propose a probabilistically principled general approach to SSL that considers the distribution over label predictions, for labels of different complexity, from "one-hot" vectors to binary vectors and…
• Robust model training and generalisation with Studentising flows
  11 Jun 2020 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Normalising flows are tractable probabilistic models that leverage the power of deep learning to describe a wide parametric family of distributions, all while remaining trainable using maximum likelihood.
• NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity
  11 Jun 2020 1 repository listed Syntology ran 4 of 10 samples · 6 unverified
  Normalizing flows (NFs) have become a prominent method for deep generative models that allow for an analytic probability density estimation and efficient synthesis.
• Style-Controllable Speech-Driven Gesture Synthesis Using Normalising Flows
  25 May 2020 1 repository listed
  In interactive scenarios, systems for generating natural animations on the fly are key to achieving believable and relatable characters.
• Woodbury Transformations for Deep Generative Flows
  27 Feb 2020 1 repository listed Syntology ran 1 of 3 samples · 2 unverified · 3 pointer-only (licence)
  In this paper, we introduce Woodbury transformations, which achieve efficient invertibility via the Woodbury matrix identity and efficient determinant calculation via Sylvester's determinant identity.
• VFlow: More Expressive Generative Flows with Variational Data Augmentation
  22 Feb 2020 1 repository listed Syntology ran 0 of 9 samples · 9 unverified
  Generative flows are promising tractable models for density modeling that define probabilistic distributions with invertible transformations.
• The Neural Moving Average Model for Scalable Variational Inference of State Space Models
  2 Oct 2019 1 repository listed
  Variational inference has had great success in scaling approximate Bayesian inference to big data by exploiting mini-batch training.

Syntology lines on 11 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.