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/StatsDLMathsRecomSys/Self-attention-with-Functional-Time-Representation-Learning
A Temporal Kernel Approach for Deep Learning with Continuous-time Information
https://github.com/Synerise/booking-challenge
An efficient manifold density estimator for all recommendation systems
https://github.com/Synerise/kdd-cup-2021
An efficient manifold density estimator for all recommendation systems
https://github.com/THHHomas/mls
https://github.com/Tan-Furukawa/badzupa
Laplace approximation for logistic Gaussian process density estimation and regression
https://github.com/X-LANCE/VoiceFlow-TTS
Score-Based Generative Modeling through Stochastic Differential Equations
https://github.com/YHL04/ddpm
https://github.com/Yiannis128/plain_c_nn_benchmark
NeuroCodeBench: a plain C neural network benchmark for software verification
https://github.com/Young98CN/pointconv_pytorch
https://github.com/Zhangyanbo/iResNetLab
https://github.com/acids-ircam/diffusion_models
https://github.com/addtt/variational-diffusion-models
https://github.com/adrianjav/heterogeneous_vaes
https://github.com/ag1988/dss
https://github.com/albarji/toy-diffusion
https://github.com/albertfgu/diffwave-sashimi
https://github.com/alexey-pronkin/annealed
https://github.com/amoretti86/PSVO
https://github.com/andreasfloros/diffusion
https://github.com/andrecavalcante/iwae
https://github.com/andrecianflone/vector_quantization
https://github.com/andyweizhao/capsule
Fast Dynamic Routing Based on Weighted Kernel Density Estimation
https://github.com/andyweizhao/capsule_text_classification
Fast Dynamic Routing Based on Weighted Kernel Density Estimation
https://github.com/angzhifan/Importance_Weighted_Autoencoders
https://github.com/anordertoreclaim/PixelCNN
https://github.com/apsyx/mvae
https://github.com/arpitbansal297/cold-diffusion-models
https://github.com/ars-ashuha/real-nvp-pytorch
https://github.com/aspuru-guzik-group/phoenics
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https://github.com/astrid-merckling/SRL4RL
https://github.com/aurelio-amerio/concretedropout
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
https://github.com/beresandras/clear-diffusion-keras
https://github.com/bing-jian/gmmreg
https://github.com/biweidai/SINF
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.