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/4m4npr33t/PICANNs
공식 코드https://github.com/AIgen/QOOB
공식 코드Nested conformal prediction and quantile out-of-bag ensemble methods
https://github.com/AWehenkel/DAG-NF
공식 코드https://github.com/AWehenkel/UMNN
공식 코드https://github.com/AlCorreia/cm-tpm
공식 코드https://github.com/AmitRozner/Anomaly-Detection-with-Variance-Stabilized-Density-Estimation
공식 코드Anomaly Detection with Variance Stabilized Density Estimation
https://github.com/AndresDanielPerez/2211.04806-ML-Likelihood-with-KDE
공식 코드https://github.com/AndyShih12/SSDC
공식 코드https://github.com/CW-Huang/CP-Flow
공식 코드https://github.com/CW-Huang/NAF
공식 코드https://github.com/ComputationalRadiationPhysics/InFlow
공식 코드InFlow: Robust outlier detection utilizing Normalizing Flows
https://github.com/DeepGraphLearning/GraphAF
공식 코드GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
https://github.com/DevashishPrasad/Smart-Traffic-Junction
공식 코드HOG, LBP and SVM based Traffic Density Estimation at Intersection
https://github.com/DylanWusee/pointconv
공식 코드https://github.com/ESA-PhiLab/PhilEO-Bench
공식 코드https://github.com/ESA-PhiLab/PhilEO-MajorTOM
공식 코드Scaling-Up the Pretraining of the Earth Observation Foundation Model PhilEO to the MajorTOM Dataset
https://github.com/EmoryMLIP/OT-Flow
공식 코드OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport
https://github.com/Ghadjeres/schoenberg-rao
공식 코드Schoenberg-Rao distances: Entropy-based and geometry-aware statistical Hilbert distances
https://github.com/Guang000/Awesome-Dataset-Distillation
공식 코드https://github.com/Hosein47/IEA-GAN
공식 코드https://github.com/Huk-David/Ratio-Copula
공식 코드Your copula is a classifier in disguise: classification-based copula density estimation
https://github.com/IDM-UWB/StateNoisePDFIdentification
공식 코드Uncovering Process Noise in LTV Systems via Kernel Deconvolution
https://github.com/IPL-UV/rbig
공식 코드Gaussianizing the Earth: Multidimensional Information Measures for Earth Data Analysis
https://github.com/IPL-UV/rbig_jax
공식 코드Gaussianizing the Earth: Multidimensional Information Measures for Earth Data Analysis
https://github.com/IPL-UV/rbig_matlab
공식 코드Information Theory Measures via Multidimensional Gaussianization
https://github.com/IPL-UV/sakame
공식 코드Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciences
https://github.com/IgorSadoune/multi-level-auction-generator
공식 코드Implementing a Hierarchical Deep Learning Approach for Simulating Multi-Level Auction Data
https://github.com/J0nasSeng/federated-spn
공식 코드https://github.com/Jack-Poole/Statistic-Alignment
공식 코드On statistic alignment for domain adaptation in structural health monitoring
https://github.com/JavierUrenaPhDProjects/artificial_fluorescent_dataset
공식 코드Vision Transformers for Weakly-Supervised Microorganism Enumeration
https://github.com/JavierUrenaPhDProjects/vits_for_WSC
공식 코드Vision Transformers for Weakly-Supervised Microorganism Enumeration
https://github.com/Joaggi/Fast-Kernel-Density-Estimation-with-Density-Matrices-and-Random-Fourier-Features
공식 코드Fast Kernel Density Estimation with Density Matrices and Random Fourier Features
https://github.com/Joaggi/anomaly-detection-density-matrix-kernel-density-estimation
공식 코드AD-DMKDE: Anomaly Detection through Density Matrices and Fourier Features
https://github.com/Joaggi/demande
공식 코드Quantum Adaptive Fourier Features for Neural Density Estimation
https://github.com/Joaggi/lean-dmkde
공식 코드LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.