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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/shounakchattopadhyay/NN-DM
공식 코드https://github.com/sifanexisted/fundiff
공식 코드FunDiff: Diffusion Models over Function Spaces for Physics-Informed Generative Modeling
https://github.com/silviogalesso/dense-ood-knns
공식 코드Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection
https://github.com/sinzlab/cgnf
공식 코드Multi-hypothesis 3D human pose estimation metrics favor miscalibrated distributions
https://github.com/sissa-data-science/dadapy
공식 코드https://github.com/sixuli/ddpm_and_kde
공식 코드https://github.com/smsharma/fermi-gce-flows
공식 코드A neural simulation-based inference approach for characterizing the Galactic Center $γ$-ray excess
https://github.com/sorul/xnb
공식 코드https://github.com/spaceml-org/karman
공식 코드High-Cadence Thermospheric Density Estimation enabled by Machine Learning on Solar Imagery
https://github.com/stat-ml/TTDE
공식 코드https://github.com/stefanwm13/just-cluster-it-public
공식 코드https://github.com/studying910/EIW-Flow
공식 코드https://github.com/sudamphy/fpoperatorde
공식 코드https://github.com/supershiye/UniformTransformationModule
공식 코드Uniform Transformation: Refining Latent Representation in Variational Autoencoders
https://github.com/svishwa/crowdcount-cascaded-mtl
공식 코드CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting
https://github.com/swyoon/variationally-weighted-kernel-density-estimation
공식 코드https://github.com/takahashihiroshi/vae_iop
공식 코드https://github.com/talreiss/accurate-interpretable-vad
공식 코드https://github.com/talwagner/lsq
공식 코드Fast Private Kernel Density Estimation via Locality Sensitive Quantization
https://github.com/tanganke/hmr
공식 코드https://github.com/taohan10200/drnet
공식 코드DR.VIC: Decomposition and Reasoning for Video Individual Counting
https://github.com/tcl9876/visual-vae
공식 코드https://github.com/tennessee-wallaceh/tailnflows
공식 코드https://github.com/tensorflow/tensor2tensor
공식 코드On the Discrepancy between Density Estimation and Sequence Generation
https://github.com/thjashin/multires-conv
공식 코드https://github.com/thu-ml/i-dode
공식 코드Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs
https://github.com/thu-ml/vflow
공식 코드VFlow: More Expressive Generative Flows with Variational Data Augmentation
https://github.com/tliu68/autogmm
공식 코드AutoGMM: Automatic and Hierarchical Gaussian Mixture Modeling in Python
https://github.com/tomviering/RiskMonotonicity
공식 코드https://github.com/tonellolab/codine-copula-estimator
공식 코드https://github.com/tpopordanoska/calibration-object-detection
공식 코드Beyond Classification: Definition and Density-based Estimation of Calibration in Object Detection
https://github.com/tpospisi/DeepCDE
공식 코드https://github.com/tpospisi/NNKCDE
공식 코드https://github.com/tpospisi/cdetools
공식 코드https://github.com/tpospisi/rfcde
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.