https://github.com/opendilab/GenerativeRL
공식 코드Revisiting Generative Policies: A Simpler Reinforcement Learning Algorithmic Perspective
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/pablo-lemos/cosmoswag
공식 코드Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks
https://github.com/patrickrchao/dist_shift_exp
공식 코드Statistical Estimation Under Distribution Shift: Wasserstein Perturbations and Minimax Theory
https://github.com/patrickrchao/distribution-shift-experiments
공식 코드Statistical Estimation Under Distribution Shift: Wasserstein Perturbations and Minimax Theory
https://github.com/paulinebourigault/ghkernelanomalydetect
공식 코드Kernel-Based Anomaly Detection Using Generalized Hyperbolic Processes
https://github.com/pawelc/NeuralLikelihoods
공식 코드https://github.com/pawelc/NeuralLikelihoods0
공식 코드https://github.com/pbecker93/ExpectedInformationMaximization
공식 코드Expected Information Maximization: Using the I-Projection for Mixture Density Estimation
https://github.com/peterbjorgensen/DeepDFT
공식 코드https://github.com/ppilar/pcgan
공식 코드Probabilistic Matching of Real and Generated Data Statistics in Generative Adversarial Networks
https://github.com/privateyoung/Youtube-NT
공식 코드Hierarchical Autoregressive Modeling for Neural Video Compression
https://github.com/probsys/hierarchical-irm
공식 코드https://github.com/puerto-santana/pyashmm
공식 코드Context-specific kernel-based hidden Markov model for time series analysis
https://github.com/qiangfu09/nula
공식 코드Mean-field underdamped Langevin dynamics and its spacetime discretization
https://github.com/qinglong-tian/tabpfn_study
공식 코드https://github.com/qsh-zh/DiffFlow
공식 코드https://github.com/quantlet/hedging_cc
공식 코드https://github.com/rafasj13/binnedsemiparametricbn
공식 코드https://github.com/rfeinman/detecting-adversarial-samples
공식 코드https://github.com/rittlern/probabilistic_forecasting
공식 코드A Deep Learning Approach to Probabilistic Forecasting of Weather
https://github.com/rizbicki/FlexCoDE
공식 코드Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation
https://github.com/robert-giaquinto/gradient-boosted-normalizing-flows
공식 코드https://github.com/rtqichen/residual-flows
공식 코드https://github.com/ruishu/bcde
공식 코드https://github.com/rxian/fair-regression
공식 코드https://github.com/s-sahoo/mulan
공식 코드https://github.com/saandeepa93/FlowCon_OOD
공식 코드FlowCon: Out-of-Distribution Detection using Flow-Based Contrastive Learning
https://github.com/sajadn/Exemplar-VAE
공식 코드Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation
https://github.com/sbrblee/DivRareGen
공식 코드https://github.com/selflein/EBM-OOD-Detection
공식 코드https://github.com/seongsukim-ml/gpwno
공식 코드Gaussian Plane-Wave Neural Operator for Electron Density Estimation
https://github.com/sg5g10/hmm
공식 코드Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs
https://github.com/shangtai/githubcascadedmodel
공식 코드Sparse Density Trees and Lists: An Interpretable Alternative to High-Dimensional Histograms
https://github.com/shenghaowu/counterfactual-generative-models
공식 코드Counterfactual Generative Models for Time-Varying Treatments
https://github.com/shermanlian/spatial-entropy-loss
공식 코드Equipping Diffusion Models with Differentiable Spatial Entropy for Low-Light Image Enhancement
https://github.com/shijiew97/neuralg
공식 코드Neural-g: A Deep Learning Framework for Mixing Density Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.