awesome-normalizing-flows
Awesome resources on normalizing flows.
https://github.com/janosh/awesome-normalizing-flows
Last synced: about 5 hours ago
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📦 Packages <small>(13)</small>
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<img src="assets/pytorch.svg" alt="PyTorch" height="20px"> PyTorch Packages
- FrEIA - heidelberg.de/vislearn)
- FlowTorch Docs
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📝 Publications <small>(60)</small>
- FInC Flow: Fast and Invertible k×k Convolutions for Normalizing Flows
- Invertible Generative Modeling using Linear Rational Splines
- Normalizing Flows for Probabilistic Modeling and Inference
- Unconstrained Monotonic Neural Networks
- [Code
- [Code
- [Code
- [Code
- [Code
- [Code
- ManiFlow: Implicitly Representing Manifolds with Normalizing Flows
- Graphical Normalizing Flows
- Multi-scale Attention Flow for Probabilistic Time Series Forecasting
- Adaptive Monte Carlo augmented with normalizing flows
- [Code
- Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods
- Equivariant Flows: exact likelihood generative learning for symmetric densities
- iUNets: Fully invertible U-Nets with Learnable Upand Downsampling
- Normalizing Flows with Multi-Scale Autoregressive Priors
- [Tweet - flow)]
- [Tweet
- [Tweet
- Integer Discrete Flows and Lossless Compression
- Invertible Monotone Operators for Normalizing Flows
- E(n) Equivariant Normalizing Flows
- CInC Flow: Characterizable Invertible 3x3 Convolution
- Improving Normalizing Flows via Better Orthogonal Parameterizations - Casado et al.<br>
- Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
- [OpenReview.net - ts)]
- Haar Wavelet based Block Autoregressive Flows for Trajectories
- SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
- Why Normalizing Flows Fail to Detect Out-of-Distribution Data
- [Tweet
- The Convolution Exponential and Generalized Sylvester Flows
- Stochastic Normalizing Flows
- Stochastic Normalizing Flows (SNF)
- MaCow: Masked Convolutional Generative Flow
- Emerging Convolutions for Generative Normalizing Flows
- FloWaveNet : A Generative Flow for Raw Audio
- Convolutional Normalizing Flows
- Masked Autoregressive Flow for Density Estimation
- Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
- Variational Inference with Normalizing Flows
- Transferable Boltzmann Generators
- Normalizing Flows: An Introduction and Review of Current Methods
- Adaptive Monte Carlo augmented with normalizing flows
- [Video
- [Video
- [Tweet
- [Tweet - flow)]
- [Tweet
- [Tweet
- [Code
- Orthogonalizing Convolutional Layers with the Cayley Transform
- AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows
- [Code
- [Code
- [Code
- Flows for simultaneous manifold learning and density estimation
- Gaussianization Flows
- [Code
- Gradient Boosted Normalizing Flows
- [Code
- Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
- Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification
- Noise Regularization for Conditional Density Estimation
- [Blog
- MintNet: Building Invertible Neural Networks with Masked Convolutions
- [Code
- Densely connected normalizing flows
- [Code
- Invertible Convolutional Flow
- [Code
- Invertible Convolutional Networks
- Neural Spline Flows
- Graph Normalizing Flows
- Fast Flow Reconstruction via Robust Invertible n x n Convolution
- Block Neural Autoregressive Flow
- [Code
- [Code
- [Code
- FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
- Glow: Generative Flow with Invertible 1x1 Convolutions
- Neural Autoregressive Flows
- [Code
- Sylvester Normalizing Flow for Variational Inference
- Improving Variational Inference with Inverse Autoregressive Flow
- [Code
- Density estimation using Real NVP - Dickstein et al.<br>
- Masked Autoencoder for Distribution Estimation
- Non-linear Independent Components Estimation
- Iterative Gaussianization: from ICA to Random Rotations - Valls et al.<br>
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📝 Publications <small>(57)</small>
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🛠️ Applications <small>(8)</small>
- Amazon Science
- On the Sentence Embeddings from Pre-trained Language Models
- Targeted free energy estimation via learned mappings
- Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows
- [Video
- SRFlow: Learning the Super-Resolution Space with Normalizing Flow
- NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
- Analyzing Inverse Problems with Invertible Neural Networks
- Latent Space Policies for Hierarchical Reinforcement Learning
- [Video
- [Video
- Targeted free energy estimation via learned mappings
- Normalizing Kalman Filters for Multivariate Time Series Analysis
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📺 Videos <small>(8)</small>
- Normalizing Flows - Motivations, The Big Idea & Essential Foundations
- Normalizing Flows - book.github.io/slopes-expectations.html)<br>
- full series
- Introduction to Normalizing Flows
- Stan
- Flow Models - cs294-158-sp20/home)<br>
- What are normalizing flows?
- A primer on normalizing flows - dinh.github.io)<br>
- Graph Normalizing Flows
- Sylvester Normalizing Flow for Variational Inference
- Normalizing Flows - Motivations, The Big Idea & Essential Foundations
- Normalizing Flows - book.github.io/slopes-expectations.html)<br>
- Introduction to Normalizing Flows
- Flow Models - cs294-158-sp20/home)<br>
- What are normalizing flows?
- A primer on normalizing flows - dinh.github.io)<br>
- Graph Normalizing Flows
- Sylvester Normalizing Flow for Variational Inference
- A primer on normalizing flows - dinh.github.io)<br>
- What are normalizing flows?
- Graph Normalizing Flows
- Sylvester Normalizing Flow for Variational Inference
- Normalizing Flows - Motivations, The Big Idea & Essential Foundations
- Normalizing Flows - book.github.io/slopes-expectations.html)<br>
- Introduction to Normalizing Flows
- Flow Models - cs294-158-sp20/home)<br>
- full series
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🧑💻 Repos <small>(18)</small>
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<img src="assets/pytorch.svg" alt="PyTorch" height="20px"> PyTorch Repos
- Density Estimation with Neural ODEs and Density Estimation with FFJORDs
- pytorch-flows
- StyleFlow
- [Docs
- Unconstrained Monotonic Neural Networks (UMNN)
- NICE: Non-linear Independent Components Estimation
- Normalizing Flows - Introduction (Part 1)
- Graphical Normalizing Flows
- Density Estimation with Neural ODEs and Density Estimation with FFJORDs
- pytorch-normalizing-flows
- pytorch_flows - ircam](https://github.com/acids-ircam)
- normalizing_flows
- pytorch-flows
- DeeProb-kit
- Parts 2 and 3 coming later
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<img src="assets/other.svg" alt="Other" height="20px"> Other Repos
- Normalizing Flows Overview
- Normalizing Flows Overview
- Normalizing Flows Overview
- Deep Density Destructors - based as well as Gaussianization methods which are similar to normalizing flows except they converge iteratively instead of fully parametrized. That is, they still use bijective transforms, compute the Jacobian, check the likelihood and you can still sample and get probability density estimates. The only difference is you repeat the following two steps until convergence:
- Destructive Deep Learning (ddl)
- Theano
- NormFlows
- `autograd` - level.
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<img src="assets/tensorflow.svg" alt="TensorFlow" height="20px"> TensorFlow Repos
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<img src="assets/jax.svg" alt="JAX" height="20px"> JAX Repos
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🌐 Blog Posts <small>(5)</small>
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<img src="assets/other.svg" alt="Other" height="20px"> Other Repos
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📦 Packages <small>(15)</small>
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<img src="assets/pytorch.svg" alt="PyTorch" height="20px"> PyTorch Packages
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<img src="assets/jax.svg" alt="JAX" height="20px"> JAX Packages
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📦 Packages <small>(14)</small>
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🚧 Contributing
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📝 Publications <small>(60)</small>
92
🧑💻 Repos <small>(18)</small>
29
📺 Videos <small>(8)</small>
27
📦 Packages <small>(15)</small>
18
🛠️ Applications <small>(8)</small>
13
🌐 Blog Posts <small>(5)</small>
5
📦 Packages <small>(13)</small>
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📦 Packages <small>(14)</small>
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📝 Publications <small>(57)</small>
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Keywords
normalizing-flows
13
density-estimation
7
pytorch
5
deep-learning
5
invertible-neural-networks
3
neural-networks
3
julia-language
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variational-autoencoder
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variational-inference
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faces
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julialang
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stylegan
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flows
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continuous-normalizing-flows
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jax
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tensorflow
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machine-learning
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normalizing-flow
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glow
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monotonic
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neural-network
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generative-model
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infinitesimal-normalizing-flows
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infinitesimal-continuous-normalizing-flows
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stat
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statistics
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probabilistic-programming
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data-science
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pdf
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masked-convolutions
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flow-models
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deep-generative-model
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boosting
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black-box-attacks
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adversarial-machine-learning
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generative-models
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sum-product-networks
1