Papers with Code 아카이브
Density Estimation
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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/MStypulkowski/mlinpl-generative-workshop
https://github.com/MarcCote/NADE
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Denoising_Diffusion_Probabilistic_Models
https://github.com/Mgdln94/Low-rank-joint-CF---HTF
Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density Estimation
https://github.com/Michedev/DDPM-Pytorch
https://github.com/MindCode-4/code-7/tree/main/gaussian_adaptive_attention
https://github.com/MindSpore-scientific-2/code-10/tree/main/temporal-normalizing-flows
https://github.com/MindSpore-scientific-2/code-11/tree/main/nystrom-attention
https://github.com/MindSpore-scientific-2/code-4/tree/main/iwae
https://github.com/MindSpore-scientific-2/code-5/tree/main/iwae
https://github.com/MindSpore-scientific-2/code-8/tree/main/nystrom-attention
https://github.com/MindSpore-scientific/code-1/tree/main/temporal-normalizing-flows
https://github.com/MindSpore-scientific/code-10/tree/main/iwae
https://github.com/MindSpore-scientific/code-11/tree/main/iwae
https://github.com/MindSpore-scientific/code-11/tree/main/temporal-normalizing-flows
https://github.com/MindSpore-scientific/code-13/tree/main/nystrom-attention
https://github.com/MindSpore-scientific/code-4/tree/main/iwae
https://github.com/MindSpore-scientific/code-8/tree/main/iwae
https://github.com/NVIDIA/radtts
https://github.com/Naagar/Glow_NormalizingFlow_implimentation
https://github.com/NamyongPark/EvoKG
https://github.com/NiallJeffrey/DeepMass
Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks
https://github.com/P4ppenheimer/NormalizingFlows
https://github.com/P4ppenheimer/NormalizingFlows_rnvp
https://github.com/PaddlePaddle/PaddleScience
FunDiff: Diffusion Models over Function Spaces for Physics-Informed Generative Modeling
https://github.com/PsorTheDoctor/microarray-data
https://github.com/R-ramljak/MNO_mobdensity
On the estimation of spatial density from mobile network operator data
https://github.com/RotemMayo/NAF
https://github.com/RuqiBai/mixture_flow
https://github.com/SJHNJU/Gated_PixelCNN
https://github.com/SJHNJU/gated-pixelcnn
https://github.com/SamArgt/AudioSourceSep
https://github.com/Sarathismg/Categorized-Crowd-Count-Test-on-Decidenet-
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation
https://github.com/SinclairHudson/RePaint
https://github.com/SrinjaySarkar/REAL-NVP
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.