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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/Kim-Dongjun/Soft-Truncation
공식 코드https://github.com/L0SG/NanoFlow
공식 코드NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity
https://github.com/LabForComputationalVision/universal_inverse_problem
공식 코드Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser
https://github.com/Lindsay-Lab/substation-seg
공식 코드Improving satellite imagery segmentation using multiple Sentinel-2 revisits
https://github.com/MaStatLab/boostPM
공식 코드Unsupervised tree boosting for learning probability distributions
https://github.com/MichaelChirico/portland
공식 코드https://github.com/Mr8ND/cdetools_applications
공식 코드https://github.com/NiallJeffrey/MomentNetworks
공식 코드Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks
https://github.com/NickHoernle/semantic_loss
공식 코드MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks
https://github.com/NivC/SINBAD
공식 코드https://github.com/ParthaEth/Regularized_autoencoders-RAE-
공식 코드https://github.com/Patrickens/sbmfi_public/tree/master/arxiv_polytope
공식 코드https://github.com/PhIMaL/temporal_normalizing_flows
공식 코드https://github.com/SAP-samples/security-research-membership-inference-against-generative-networks
공식 코드Reconstruction and Membership Inference Attacks against Generative Models
https://github.com/SJYuCNEL/Fault_detection_PMIM
공식 코드Interpretable Fault Detection using Projections of Mutual Information Matrix
https://github.com/SPOClab-ca/layerwise-anomaly
공식 코드How is BERT surprised? Layerwise detection of linguistic anomalies
https://github.com/SarahQiong/CTDGMR
공식 코드Gaussian Mixture Reduction with Composite Transportation Divergence
https://github.com/StatsDLMathsRecomSys/Inductive-representation-learning-on-temporal-graphs
공식 코드A Temporal Kernel Approach for Deep Learning with Continuous-time Information
https://github.com/UCLA-StarAI/Strudel
공식 코드Strudel: Learning Structured-Decomposable Probabilistic Circuits
https://github.com/VLL-HD/HINT
공식 코드HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference
https://github.com/Victorletzelter/VoronoiWTA
공식 코드Winner-takes-all learners are geometry-aware conditional density estimators
https://github.com/XuezheMax/macow
공식 코드https://github.com/XuezheMax/wolf
공식 코드Decoupling Global and Local Representations via Invertible Generative Flows
https://github.com/Yiming-M/CLIP-EBC
공식 코드CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
https://github.com/Yingjun-Du/VID
공식 코드https://github.com/a-vzer/waveletflowpytorch
공식 코드Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing Flows
https://github.com/aaronhan223/slcp
공식 코드https://github.com/adamqlmeng/paddingflow
공식 코드PaddingFlow: Improving Normalizing Flows with Padding-Dimensional Noise
https://github.com/adjidieng/REM
공식 코드https://github.com/aegis1863/cea
공식 코드Counterfactual experience augmented off-policy reinforcement learning
https://github.com/aiqz/bype-vae
공식 코드https://github.com/airalcorn2/deformer
공식 코드The DEformer: An Order-Agnostic Distribution Estimating Transformer
https://github.com/ajaychawda58/uadad
공식 코드Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings
https://github.com/ajayjain/lmconv
공식 코드https://github.com/akuzina/dvp_vae
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.