Papers with Code 아카이브

Density Estimation

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평가표: 14
보존된 평가 기록: 65

The goal of Density Estimation is to give an accurate description of the underlying probabilistic density distribution of an observable data set with unknown density.

Source: Contrastive Predictive Coding Based Feature for Automatic Speaker Verification

코드

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

https://github.com/CUVL/Neural-Manifold-Ordinary-Differential-Equations

Neural Manifold Ordinary Differential Equations

https://github.com/CW-Huang/sdeflow-light

Denoising Diffusion Probabilistic Models

https://github.com/CharlesArnal/IWAE_replication_project

Importance Weighted Autoencoders

https://github.com/ClaraBing/flow

Glow: Generative Flow with Invertible 1x1 Convolutions

https://github.com/CompPhysVienna/paper_NF_for_rare_events

Density estimation using Real NVP

https://github.com/D-hash-code/ffjord-rnode-finalweek-mnist

How to train your neural ODE: the world of Jacobian and kinetic regularization

https://github.com/D3-AI/Orion

Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series

https://github.com/Daniel-H-99/CRD

Glow: Generative Flow with Invertible 1x1 Convolutions

https://github.com/Daniil-Selikhanovych/h-vae

Importance Weighted Autoencoders

https://github.com/Daulbaev/IRDM

Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs

https://github.com/Discover304/SinGlow

Glow: Generative Flow with Invertible 1x1 Convolutions

https://github.com/DylanWusee/pointconv_pytorch

PointConv: Deep Convolutional Networks on 3D Point Clouds

https://github.com/ECE685-FinalProject/3D-object-recognition

PointConv: Deep Convolutional Networks on 3D Point Clouds

https://github.com/Ending2015a/toy_gradlogp

Deep Energy Estimator Networks

https://github.com/EugenHotaj/pytorch-generative/blob/master/pytorch_generative/models/autoregressive/gated_pixel_cnn.py

Conditional Image Generation with PixelCNN Decoders

https://github.com/EugenHotaj/pytorch-generative/blob/master/pytorch_generative/models/autoregressive/made.py

MADE: Masked Autoencoder for Distribution Estimation

https://github.com/EugenHotaj/pytorch-generative/blob/master/pytorch_generative/models/autoregressive/nade.py

Neural Autoregressive Distribution Estimation

https://github.com/EugenHotaj/pytorch-generative/blob/master/pytorch_generative/models/autoregressive/pixel_snail.py

PixelSNAIL: An Improved Autoregressive Generative Model

https://github.com/FlorentinCDX/Roundtrip/tree/master/Roundtrip

Roundtrip: A Deep Generative Neural Density Estimator

https://github.com/FrancescoCrecchi/AE_Detector

Detecting Adversarial Examples through Nonlinear Dimensionality Reduction

https://github.com/Gabriel-Macias/iwae_tutorial

Importance Weighted Autoencoders

https://github.com/Ghailen-Ben-Achour/PointConv_segmentation

PointConv: Deep Convolutional Networks on 3D Point Clouds

https://github.com/GiadaSimionato/Analysis_and_Comparison_of_Deep_Learning_Methods_for_Jazz_Music_Generation

Deep Learning for Music

https://github.com/GinGinWang/MTQ

Masked Autoregressive Flow for Density Estimation

https://github.com/HardikPrabhu/Energy-Time-series-anomaly-detection

DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density Estimation

https://github.com/Hramchenko/diffusion_distiller

Progressive Distillation for Fast Sampling of Diffusion Models

https://github.com/Hyeongmin-Cho/Detecting-Adversarial-Samples-from-Artifacts-pytorch

Detecting Adversarial Samples from Artifacts

https://github.com/IPL-UV/gauss4eo

Information Theory Measures via Multidimensional Gaussianization

https://github.com/IPL-UV/jaxkern

Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciences

https://github.com/Ipsedo/MusicDiffusion

Denoising Diffusion Probabilistic Models

https://github.com/Janspiry/Palette-Image-to-Image-Diffusion-Models

Denoising Diffusion Probabilistic Models

https://github.com/JohanYe/IWAE-pytorch

Importance Weighted Autoencoders

https://github.com/JohanZYe/IWAE-pytorch

Importance Weighted Autoencoders

https://github.com/JoonyoungYi/CFNADE-keras

Neural Autoregressive Distribution Estimation

https://github.com/KiUngSong/Generative-Models

Glow: Generative Flow with Invertible 1x1 Convolutions

https://github.com/Kismuz/crypto_spread_test

Representation Learning: A Review and New Perspectives

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