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/gtshs2/BetaLSTM
공식 코드https://github.com/gudovskiy/contextflow
공식 코드https://github.com/haaclassic/treeformer
공식 코드https://github.com/hamrel-cxu/localflowmatching
공식 코드https://github.com/hanhsienhuang/CNF-TPR
공식 코드Accelerating Continuous Normalizing Flow with Trajectory Polynomial Regularization
https://github.com/happygu0524/multicontrol
공식 코드Controllable Text Generation via Probability Density Estimation in the Latent Space
https://github.com/hazyresearch/state-spaces
공식 코드https://github.com/henrysky/stars_foundation_diffusion
공식 코드Estimating Probability Densities with Transformer and Denoising Diffusion
https://github.com/hiendn/liftedlikelihood
공식 코드https://github.com/hoemr/dequantified-diffusion-bridge-density-ratio-estimation
공식 코드Dequantified Diffusion-Schr{ö}dinger Bridge for Density Ratio Estimation
https://github.com/hojonathanho/diffusion
공식 코드https://github.com/holywater2/GPWNO
공식 코드Gaussian Plane-Wave Neural Operator for Electron Density Estimation
https://github.com/hungntt/xai_thyroid
공식 코드https://github.com/intel/rho-diffusion
공식 코드$ρ$-Diffusion: A diffusion-based density estimation framework for computational physics
https://github.com/ioangatop/srVAE
공식 코드https://github.com/itsmattei/ken
공식 코드Less is KEN: a Universal and Simple Non-Parametric Pruning Algorithm for Large Language Models
https://github.com/iurteaga/bandits
공식 코드Nonparametric Gaussian Mixture Models for the Multi-Armed Bandit
https://github.com/ivanpeng0414/nonparametric-estimation-via-variance-reduced-sketching
공식 코드Multivariate Density Estimation via Variance-Reduced Sketching
https://github.com/izzak98/dist-pred
공식 코드https://github.com/jacarvalho/nopg
공식 코드https://github.com/jacobjinkelly/easy-neural-ode
공식 코드https://github.com/jakubmicorek/MULDE-Multiscale-Log-Density-Estimation-via-Denoising-Score-Matching-for-Video-Anomaly-Detection
공식 코드MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection
https://github.com/janfb/mcabc
공식 코드https://github.com/jbkinney/14_maxent
공식 코드Unification of field theory and maximum entropy methods for learning probability densities
https://github.com/jeremiastraub/diffusion
공식 코드https://github.com/jind11/rede
공식 코드Towards Zero and Few-shot Knowledge-seeking Turn Detection in Task-orientated Dialogue Systems
https://github.com/joaggi/incremental-anomaly-detection-using-quantum-measurements
공식 코드https://github.com/joeybose/HyperbolicNF
공식 코드https://github.com/johannbrehmer/manifold-flow
공식 코드Flows for simultaneous manifold learning and density estimation
https://github.com/johertrich/Proximal_Residual_Flows
공식 코드https://github.com/jongharyu/split-knn-rules
공식 코드https://github.com/jrmcornish/cif
공식 코드Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows
https://github.com/jrmcornish/lgf
공식 코드Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows
https://github.com/juselara1/dmae
공식 코드https://github.com/justc2/datastructdensityest
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.