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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/tranbahien/data-mollification
공식 코드One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models
https://github.com/trikpachu/DDE
공식 코드https://github.com/tyuxie/artree
공식 코드ARTree: A Deep Autoregressive Model for Phylogenetic Inference
https://github.com/ucbdrive/hd3
공식 코드Hierarchical Discrete Distribution Decomposition for Match Density Estimation
https://github.com/ucla-starai/moat
공식 코드https://github.com/uditarora/ood-text-emnlp
공식 코드https://github.com/utvisionlab/mixest
공식 코드https://github.com/vaibhavsaxena11/inverse-sigma-srn
공식 코드Generalizable Pose Estimation Using Implicit Scene Representations
https://github.com/val-iisc/css-ccnn
공식 코드Completely Self-Supervised Crowd Counting via Distribution Matching
https://github.com/valentyn1997/infs
공식 코드https://github.com/var-skip/var-skip
공식 코드Variable Skipping for Autoregressive Range Density Estimation
https://github.com/vicaws/arbitragerepair
공식 코드https://github.com/vicgalle/vis
공식 코드Variationally Inferred Sampling Through a Refined Bound for Probabilistic Programs
https://github.com/victorletzelter/code-rmcl
공식 코드https://github.com/vincentstimper/resampled-base-flows
공식 코드https://github.com/viniciusmikuni/permutationinvariantad
공식 코드High-dimensional and Permutation Invariant Anomaly Detection
https://github.com/visinf/mar-scf
공식 코드https://github.com/visinf/pixelpyramids
공식 코드PixelPyramids: Exact Inference Models from Lossless Image Pyramids
https://github.com/vislearn/fff
공식 코드https://github.com/vlgiitr/deep_cache_replacement
공식 코드DEAP Cache: Deep Eviction Admission and Prefetching for Cache
https://github.com/voletiv/mrcnf
공식 코드https://github.com/vorausrobotik/voraus-ad-dataset
공식 코드The voraus-AD Dataset for Anomaly Detection in Robot Applications
https://github.com/w-wu/has_cnf
공식 코드It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimation
https://github.com/wangleiphy/MongeAmpereFlow
공식 코드https://github.com/will-consagra/neuropmd
공식 코드NeuroPMD: Neural Fields for Density Estimation on Product Manifolds
https://github.com/wuwushrek/taylanets
공식 코드https://github.com/xinweizhang/daebm
공식 코드Persistently Trained, Diffusion-assisted Energy-based Models
https://github.com/xiongjiechen/normalizing-flows-dpfs
공식 코드Conditional Measurement Density Estimation in Sequential Monte Carlo via Normalizing Flow
https://github.com/xzleo/hinge-wasserstein
공식 코드Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks
https://github.com/yang-song/score_sde
공식 코드Score-Based Generative Modeling through Stochastic Differential Equations
https://github.com/yangzhangcst/afa-graph
공식 코드https://github.com/yaniv-shulman/rsklpr
공식 코드https://github.com/yankeegsj/bla
공식 코드https://github.com/yaoshuwang/SelNet-Estimation
공식 코드Consistent and Flexible Selectivity Estimation for High-Dimensional Data
https://github.com/yenchic/Morse_Smale
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.