Papers with Code 아카이브

Normalising Flows

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Harry45/emuflow

공식 코드

$\mathtt{emuflow}$: Normalising Flows for Joint Cosmological Analysis

https://github.com/L0SG/NanoFlow

공식 코드

NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity

https://github.com/Tom-Ryder/VIforSSMs

공식 코드

The Neural Moving Average Model for Scalable Variational Inference of State Space Models

https://github.com/akashsengupta1997/humaniflow

공식 코드

HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution Estimation

https://github.com/bghojogh/density-based-classifiers

공식 코드

Probabilistic Classification by Density Estimation Using Gaussian Mixture Model and Masked Autoregressive Flow

https://github.com/biomedia-mira/deepscm

공식 코드

Deep Structural Causal Models for Tractable Counterfactual Inference

https://github.com/bodin-e/linear-combinations-of-latents

공식 코드

Linear combinations of latents in generative models: subspaces and beyond

https://github.com/chaiyujin/glow-pytorch

공식 코드

MoGlow: Probabilistic and controllable motion synthesis using normalising flows

https://github.com/coqui-ai/TTS

공식 코드

OverFlow: Putting flows on top of neural transducers for better TTS

https://github.com/ibalazevic/lp-ssl

공식 코드

Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows

https://github.com/jrmcornish/cif

공식 코드

Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows

https://github.com/jrmcornish/lgf

공식 코드

Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows

https://github.com/llaurabatt/frugal-flows

공식 코드

Marginal Causal Flows for Validation and Inference

https://github.com/lollcat/FAB-2021

공식 코드

Bootstrap Your Flow

https://github.com/malteal/ot-decorrelation

공식 코드

Decorrelation using Optimal Transport

https://github.com/nabu-hep/nabu-hep

공식 코드

Communicating Likelihoods with Normalising Flows

https://github.com/nicola-decao/BNAF

공식 코드

Block Neural Autoregressive Flow

https://github.com/pawni/BayesByHypernet

공식 코드

Implicit Weight Uncertainty in Neural Networks

https://github.com/shivammehta25/OverFlow

공식 코드

OverFlow: Putting flows on top of neural transducers for better TTS

https://github.com/tennessee-wallaceh/tailnflows

공식 코드

Flexible Tails for Normalising Flows, with Application to the Modelling of Financial Return Data

https://github.com/thu-ml/vflow

공식 코드

VFlow: More Expressive Generative Flows with Variational Data Augmentation

https://github.com/vsimkus/variational-gibbs-inference

공식 코드

Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data

https://github.com/weiyumou/ldu-flow

공식 코드

Sinusoidal Flow: A Fast Invertible Autoregressive Flow

https://github.com/xiongjiechen/normalizing-flows-dpfs

공식 코드

Normalizing Flow-based Differentiable Particle Filters

https://github.com/yolu1055/WoodburyTransformations

공식 코드

Woodbury Transformations for Deep Generative Flows

https://github.com/Naagar/Glow_NormalizingFlow_implimentation

Block Neural Autoregressive Flow

https://github.com/beauCoker/bayesian_neural_networks

Implicit Weight Uncertainty in Neural Networks

https://github.com/biomedia-mira/causal-gen

Deep Structural Causal Models for Tractable Counterfactual Inference

https://github.com/layer6ai-labs/rectangular-flows

Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows

https://github.com/metachenyiyan/BreezeForest

Block Neural Autoregressive Flow

https://github.com/rongguangw/flow-scm

Deep Structural Causal Models for Tractable Counterfactual Inference

https://github.com/simonalexanderson/MoGlow

MoGlow: Probabilistic and controllable motion synthesis using normalising flows

https://github.com/simonalexanderson/StyleGestures

MoGlow: Probabilistic and controllable motion synthesis using normalising flows

https://github.com/sshish/NF

Block Neural Autoregressive Flow

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.