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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/changmin-yu/structured-recognition-neurips-2022
공식 코드Structured Recognition for Generative Models with Explaining Away
https://github.com/changwoo-lee/logisticbeta-reproduce
공식 코드Logistic-beta processes for dependent random probabilities with beta marginals
https://github.com/changyi7231/MEF
공식 코드https://github.com/chrvt/inflation-deflation
공식 코드Density estimation on low-dimensional manifolds: an inflation-deflation approach
https://github.com/clbonet/Spherical_Sliced-Wasserstein
공식 코드https://github.com/clementchadebec/benchmark_VAE
공식 코드Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case
https://github.com/colinqiyangli/adacat
공식 코드AdaCat: Adaptive Categorical Discretization for Autoregressive Models
https://github.com/conormdurkan/autoregressive-energy-machines
공식 코드https://github.com/cpempire/pWGD
공식 코드Projected Wasserstein gradient descent for high-dimensional Bayesian inference
https://github.com/ctrojan/DiffusionDnC
공식 코드https://github.com/cvlab-stonybrook/exrac
공식 코드Count What You Want: Exemplar Identification and Few-shot Counting of Human Actions in the Wild
https://github.com/czp16/cde-offline-rl
공식 코드Learning from Sparse Offline Datasets via Conservative Density Estimation
https://github.com/daltondurant/EnGM-PHD
공식 코드Kernel-Based Ensemble Gaussian Mixture Probability Hypothesis Density Filter
https://github.com/danielshkao/deceptivehuman
공식 코드InceptionHuman: Controllable Prompt-to-NeRF for Photorealistic 3D Human Generation
https://github.com/dannyjameswilliams/tksd
공식 코드https://github.com/dannyjameswilliams/truncsm
공식 코드Score Matching for Truncated Density Estimation on a Manifold
https://github.com/dargilboa/mdma
공식 코드https://github.com/darrenjkt/ms3d
공식 코드MS3D: Leveraging Multiple Detectors for Unsupervised Domain Adaptation in 3D Object Detection
https://github.com/datake/robustdistrl
공식 코드https://github.com/davenza/pybnesian
공식 코드https://github.com/davenza/spbn-experiments
공식 코드https://github.com/davidreiman/spectre
공식 코드Fully probabilistic quasar continua predictions near Lyman-α with conditional neural spline flows
https://github.com/daviesl/trjp
공식 코드https://github.com/davzha/DESP
공식 코드Set Prediction without Imposing Structure as Conditional Density Estimation
https://github.com/dbarzilai7/kernel_reconstruction
공식 코드Querying Kernel Methods Suffices for Reconstructing their Training Data
https://github.com/ddehun/density
공식 코드DEnsity: Open-domain Dialogue Evaluation Metric using Density Estimation
https://github.com/de-simone/MarkovChainDensityEstimator
공식 코드https://github.com/deepmind/deepmind-research/tree/master/gated_linear_networks
공식 코드https://github.com/dehshibi/advise
공식 코드ADVISE: ADaptive Feature Relevance and VISual Explanations for Convolutional Neural Networks
https://github.com/deltafloflo/knnxkde
공식 코드https://github.com/dereklhansen/flowselect
공식 코드Normalizing Flows for Knockoff-free Controlled Feature Selection
https://github.com/dimonenka/DEDPUL
공식 코드DEDPUL: Difference-of-Estimated-Densities-based Positive-Unlabeled Learning
https://github.com/diningphil/graph-mixture-density-networks
공식 코드https://github.com/djordjemila/sdn
공식 코드Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling
https://github.com/duvenaud/warped-mixtures
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.