https://github.com/marcojira/fld
공식 코드Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples
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/martenlienen/bsi
공식 코드https://github.com/martius-lab/solver-differentiation-identity
공식 코드Backpropagation through Combinatorial Algorithms: Identity with Projection Works
https://github.com/matejgrcic/DenseFlow
공식 코드https://github.com/maxramgraber/triangular-transport-toolbox
공식 코드https://github.com/mbilos/neural-flows-experiments
공식 코드https://github.com/meaten/flowchain-iccv2023
공식 코드Fast Inference and Update of Probabilistic Density Estimation on Trajectory Prediction
https://github.com/mgermain/MADE
공식 코드https://github.com/mike-liuliu/gl_index
공식 코드A New Index for Clustering Evaluation Based on Density Estimation
https://github.com/mines-opt-ml/JKO_Flow
공식 코드Taming Hyperparameter Tuning in Continuous Normalizing Flows Using the JKO Scheme
https://github.com/mkarppa/deann
공식 코드DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search
https://github.com/mkhangg/refined_panoptic_mapping
공식 코드Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots
https://github.com/mkirchler/dependent_data_flows
공식 코드https://github.com/ml-postech/gm-vae
공식 코드https://github.com/mlvlab/monotoneflows
공식 코드https://github.com/mrana6/euclideanizing_flows
공식 코드Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems
https://github.com/myeongjong/npcov
공식 코드https://github.com/naist-icb/bmd-gan
공식 코드https://github.com/naru-project/naru
공식 코드https://github.com/naturomics/DLF
공식 코드https://github.com/nbiclab/ddn
공식 코드Deconvolutional Density Network: Modeling Free-Form Conditional Distributions
https://github.com/neocxi/pixelsnail-public
공식 코드https://github.com/nf4hep/normalizingflowshd
공식 코드Comparative Study of Coupling and Autoregressive Flows through Robust Statistical Tests
https://github.com/ngm29/astro-lsbi
공식 코드Cosmological Parameter Estimation with Sequential Linear Simulation-based Inference
https://github.com/ngushchin/lightsb
공식 코드https://github.com/nialljeffrey/evidencenetworksdemo
공식 코드Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison
https://github.com/nicola-decao/BNAF
공식 코드https://github.com/noamroze/moser_flow
공식 코드Moser Flow: Divergence-based Generative Modeling on Manifolds
https://github.com/nttcslab/rg-cache
공식 코드Reflectance-Guided, Contrast-Accumulated Histogram Equalization
https://github.com/nttcslab/rope
공식 코드Reflectance-Oriented Probabilistic Equalization for Image Enhancement
https://github.com/ocarinamat/SumProductGraphMod
공식 코드https://github.com/odunkel/huproso
공식 코드Normalizing Flows on the Product Space of SO(3) Manifolds for Probabilistic Human Pose Modeling
https://github.com/okviman/efficient-mixtures
공식 코드Efficient Mixture Learning in Black-Box Variational Inference
https://github.com/olgakuznetsova/logconcavegraphical
공식 코드Log-concave density estimation in undirected graphical models
https://github.com/openai/glow
공식 코드https://github.com/openai/pixel-cnn
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.