41 Repositories
Python normalizing-flows Libraries
[CVPR 2022] Deep Equilibrium Optical Flow Estimation
Deep Equilibrium Optical Flow Estimation This is the official repo for the paper Deep Equilibrium Optical Flow Estimation (CVPR 2022), by Shaojie Bai*
Kube OpenID Connect is an application that can be used to easily enable authentication flows via OIDC for a kubernetes cluster
Kube OpenID Connect is an application that can be used to easily enable authentication flows via OIDC for a kubernetes cluster. Kubernetes supports OpenID Connect Tokens as a way to identify users who access the cluster. Kube OpenID Connect helps users with it's plugin to authenticate an get kubectl config.
Generative Autoregressive, Normalized Flows, VAEs, Score-based models (GANVAS)
GANVAS-models This is an implementation of various generative models. It contains implementations of the following: Autoregressive Models: PixelCNN, G
Tutorials and implementations for "Self-normalizing networks"
Self-Normalizing Networks Tutorials and implementations for "Self-normalizing networks"(SNNs) as suggested by Klambauer et al. (arXiv pre-print). Vers
Reinforcement learning algorithms in RLlib
raylab Reinforcement learning algorithms in RLlib and PyTorch. Installation pip install raylab Quickstart Raylab provides agents and environments to b
Net2net - Network-to-Network Translation with Conditional Invertible Neural Networks
Net2Net Code accompanying the NeurIPS 2020 oral paper Network-to-Network Translation with Conditional Invertible Neural Networks Robin Rombach*, Patri
A proper portfolio tracker. Featuring historical allocation, cash flows and real returns.
Python Portfolio Analytics A portfolio tracker featuring account transactions, historical allocation, dividends and splits management and endless perf
A short term landscape evolution using a path sampling method to solve water and sediment flow continuity equations and model mass flows over complex topographies.
r.sim.terrain A short-term landscape evolution model that simulates topographic change for both steady state and dynamic flow regimes across a range o
PyTorch implementation of normalizing flow models
PyTorch implementation of normalizing flow models
Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)
Maximum Likelihood Training of Score-Based Diffusion Models This repo contains the official implementation for the paper Maximum Likelihood Training o
Experiments for Neural Flows paper
Neural Flows: Efficient Alternative to Neural ODEs [arxiv] TL;DR: We directly model the neural ODE solutions with neural flows, which is much faster a
The code release of paper Low-Light Image Enhancement with Normalizing Flow
[AAAI 2022] Low-Light Image Enhancement with Normalizing Flow Paper | Project Page Low-Light Image Enhancement with Normalizing Flow Yufei Wang, Renji
The best solution of the Weather Prediction track in the Yandex Shifts challenge
yandex-shifts-weather The repository contains information about my solution for the Weather Prediction track in the Yandex Shifts challenge https://re
[AAAI2022] Source code for our paper《Suppressing Static Visual Cues via Normalizing Flows for Self-Supervised Video Representation Learning》
SSVC The source code for paper [Suppressing Static Visual Cues via Normalizing Flows for Self-Supervised Video Representation Learning] samples of the
Official implementation of Densely connected normalizing flows
Densely connected normalizing flows This repository is the official implementation of NeurIPS 2021 paper Densely connected normalizing flows. Poster a
Denoising Normalizing Flow
Denoising Normalizing Flow Christian Horvat and Jean-Pascal Pfister 2021 We combine Normalizing Flows (NFs) and Denoising Auto Encoder (DAE) by introd
NeurIPS'21 Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows
NeurIPS'21 Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows This repo contains the code for the paper Tractable Densit
FlowTorch is a PyTorch library for learning and sampling from complex probability distributions using a class of methods called Normalizing Flows
FlowTorch is a PyTorch library for learning and sampling from complex probability distributions using a class of methods called Normalizing Flows.
Universal Probability Distributions with Optimal Transport and Convex Optimization
Sylvester normalizing flows for variational inference Pytorch implementation of Sylvester normalizing flows, based on our paper: Sylvester normalizing
Probabilistic-Monocular-3D-Human-Pose-Estimation-with-Normalizing-Flows
Probabilistic-Monocular-3D-Human-Pose-Estimation-with-Normalizing-Flows This is the official implementation of the ICCV 2021 Paper "Probabilistic Mono
Code for the Paper: Alexandra Lindt and Emiel Hoogeboom.
Discrete Denoising Flows This repository contains the code for the experiments presented in the paper Discrete Denoising Flows [1]. To give a short ov
Normalizing Flows with a resampled base distribution
Resampling Base Distributions of Normalizing Flows Normalizing flows are a popular class of models for approximating probability distributions. Howeve
Diffusion Normalizing Flow (DiffFlow) Neurips2021
Diffusion Normalizing Flow (DiffFlow) Reproduce setup environment The repo heavily depends on jam, a personal toolbox developed by Qsh.zh. The API may
Simulation-based inference for the Galactic Center Excess
Simulation-based inference for the Galactic Center Excess Siddharth Mishra-Sharma and Kyle Cranmer Abstract The nature of the Fermi gamma-ray Galactic
PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech
PortaSpeech - PyTorch Implementation PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech. Model Size Module Nor
PortaSpeech - PyTorch Implementation
PortaSpeech - PyTorch Implementation PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech. Model Size Module Nor
TensorFlow implementation of "Variational Inference with Normalizing Flows"
[TensorFlow 2] Variational Inference with Normalizing Flows TensorFlow implementation of "Variational Inference with Normalizing Flows" [1] Concept Co
A Python library for Deep Probabilistic Modeling
Abstract DeeProb-kit is a Python library that implements deep probabilistic models such as various kinds of Sum-Product Networks, Normalizing Flows an
PyTorch implementations of normalizing flow and its variants.
PyTorch implementations of normalizing flow and its variants.
A collection of repositories used to realise various end-to-end high-level synthesis (HLS) flows centering around the CIRCT project.
circt-hls What is this?: A collection of repositories used to realise various end-to-end high-level synthesis (HLS) flows centering around the CIRCT p
This python module is an easy-to-use port of the text normalization used in the paper "Not low-resource anymore: Aligner ensembling, batch filtering, and new datasets for Bengali-English machine translation". It is intended to be used for normalizing / cleaning Bengali and English text.
normalizer This python module is an easy-to-use port of the text normalization used in the paper "Not low-resource anymore: Aligner ensembling, batch
Official PyTorch code for WACV 2022 paper "CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows"
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows WACV 2022 preprint:https://arxiv.org/abs/2107.1
Python ELT Studio, an application for building ELT (and ETL) data flows.
The Python Extract, Load, Transform Studio is an application for performing ELT (and ETL) tasks. Under the hood the application consists of a two parts.
Slack bot for monitoring your Metaflow flows!
Metaflowbot - Slack Bot for your Metaflow flows! Metaflowbot makes it fun and easy to monitor your Metaflow runs, past and present. Imagine starting a
Code for "Causal autoregressive flows" - AISTATS, 2021
Code for "Causal Autoregressive Flow" This repository contains code to run and reproduce experiments presented in Causal Autoregressive Flows, present
Official implementation of the paper DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows
DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows Official implementation of the paper DeFlow: Learning Complex Im
This is the code for our paper DAAIN: Detection of Anomalous and AdversarialInput using Normalizing Flows
Merantix-Labs: DAAIN This is the code for our paper DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows which can be found at
Implementation of "Fast and Flexible Temporal Point Processes with Triangular Maps" (Oral @ NeurIPS 2020)
Fast and Flexible Temporal Point Processes with Triangular Maps This repository includes a reference implementation of the algorithms described in "Fa
Stochastic Normalizing Flows
Stochastic Normalizing Flows We introduce stochasticity in Boltzmann-generating flows. Normalizing flows are exact-probability generative models that
Official PyTorch implementation of "ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows"
ArtFlow Official PyTorch implementation of the paper: ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows Jie An*, Siyu Huang*, Yibing
Official code for "Parser-Free Virtual Try-on via Distilling Appearance Flows", CVPR 2021
Parser-Free Virtual Try-on via Distilling Appearance Flows, CVPR 2021 Official code for CVPR 2021 paper 'Parser-Free Virtual Try-on via Distilling App