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Density Estimation

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하위 태스크: 1
평가표: 14
보존된 평가 기록: 65

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/alan-turing-institute/memorization

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On Memorization in Probabilistic Deep Generative Models

https://github.com/alshedivat/nphmm

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Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices

https://github.com/andreanne-lemay/gray_zone_assessment

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Monte Carlo dropout increases model repeatability

https://github.com/andyshih12/hyperspn

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HyperSPNs: Compact and Expressive Probabilistic Circuits

https://github.com/anewgithubname/Truncated-Score-Matching

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Estimating Density Models with Truncation Boundaries using Score Matching

https://github.com/anibalsilva1/TabGenwithTCL

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Tabular data generation with tensor contraction layers and transformers

https://github.com/anmolkabra/score-matching-rl

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Exponential Family Model-Based Reinforcement Learning via Score Matching

https://github.com/annaguo-bios/flexcausal

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Average Causal Effect Estimation in DAGs with Hidden Variables: Extensions of Back-Door and Front-Door Criteria

https://github.com/anthonycaterini/cif-vi

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Variational Inference with Continuously-Indexed Normalizing Flows

https://github.com/apple/embedding-atlas

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A Scalable Approach to Clustering Embedding Projections

https://github.com/apple/ml-tarflow

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Normalizing Flows are Capable Generative Models

https://github.com/aravind0706/flowpp

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Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

https://github.com/aravindsrinivas/flowpp

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Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

https://github.com/balintmate/flowification

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Flowification: Everything is a Normalizing Flow

https://github.com/baptistar/atm

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On the representation and learning of monotone triangular transport maps

https://github.com/barnhilldave/tml

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Tropical Geometric Tools for Machine Learning: the TML package

https://github.com/batuceng/max_quantile

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Conformalized High-Density Quantile Regression via Dynamic Prototypes-based Probability Density Estimation

https://github.com/bayesiains/density-deconvolution

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Density Deconvolution with Normalizing Flows

https://github.com/bayesiains/nsf

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Neural Spline Flows

https://github.com/beautifuldog01/document-set-expansion-pude

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Document Set Expansion with Positive-Unlabelled Learning Using Intractable Density Estimation

https://github.com/bee-hive/kdbirl_public

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Kernel Density Bayesian Inverse Reinforcement Learning

https://github.com/benjione/sequentialmeasuretransport.jl

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Sequential transport maps using SoS density estimation and $α$-divergences

https://github.com/bghojogh/density-based-classifiers

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Probabilistic Classification by Density Estimation Using Gaussian Mixture Model and Masked Autoregressive Flow

https://github.com/bips-hb/arf_paper

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Adversarial random forests for density estimation and generative modeling

https://github.com/bips-hb/arfpy

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arfpy: A python package for density estimation and generative modeling with adversarial random forests

https://github.com/biweidai/SIG

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Sliced Iterative Normalizing Flows

https://github.com/bnubald/dce-schlieren-density-reconstruction

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Density reconstruction from schlieren images through Bayesian nonparametric models

https://github.com/bnubald/pof-schlieren-density-reconstruction

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Density reconstruction from schlieren images through Bayesian nonparametric models

https://github.com/bohu615/structured-generative-modeling

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Information Theoretic Structured Generative Modeling

https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge

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The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

https://github.com/buggyyang/hier-video-compression

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Hierarchical Autoregressive Modeling for Neural Video Compression

https://github.com/buggyyang/youtube-nt

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Hierarchical Autoregressive Modeling for Neural Video Compression

https://github.com/burning489/conditionalfollmerflow

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Deep conditional distribution learning via conditional Föllmer flow

https://github.com/c-siu/rdad

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Detection of Small Holes by the Scale-Invariant Robust Density-Aware Distance (RDAD) Filtration

https://github.com/calvinmccarter/unmasking-trees

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Unmasking Trees for Tabular Data

https://github.com/carvalhaes-ai/audience-expansion

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Reframing Audience Expansion through the Lens of Probability Density Estimation

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