Papers with Code 아카이브

regression

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

6

보존된 평가 기록

15

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/SilviaLauraPintea/DejaVu

Deja Vu: Motion Prediction in Static Images

https://github.com/SimonLliu/CSS_and_up_term

One-shot Face Recognition by Promoting Underrepresented Classes

https://github.com/SinghJasdeep/Projecting-Conflicting-Gradients

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/SonginCV/GMPHD_MAF

High-Speed Tracking with Kernelized Correlation Filters

https://github.com/SonginCV/GMPHD_SAF

High-Speed Tracking with Kernelized Correlation Filters

https://github.com/SonginCV/MAF_HDA

High-Speed Tracking with Kernelized Correlation Filters

https://github.com/SourangshuGhosh/BTCpredictor

Bayesian regression and Bitcoin

https://github.com/SourangshuGhosh/BitCoinPricepredictor

Bayesian regression and Bitcoin

https://github.com/SourangshuGhosh/Doubly-Stochastic-DGP

Doubly Stochastic Variational Inference for Deep Gaussian Processes

https://github.com/SourangshuGhosh/Doubly-Stochastic-Deep-Gaussian-Process

Doubly Stochastic Variational Inference for Deep Gaussian Processes

https://github.com/StanfordASL/SCOD

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

https://github.com/StanfordASL/moca

Meta-Learning Priors for Efficient Online Bayesian Regression

https://github.com/Statwolf/LocalLinearForest

Local Linear Forests

https://github.com/Sudhir22/conformalInference

Distribution-Free Predictive Inference For Regression

https://github.com/SulemanKhurram/ThesisExperiments

Weight Uncertainty in Neural Networks

https://github.com/Sunarker/Safeguarded-Dynamic-Label-Regression-for-Noisy-Supervision

Safeguarded Dynamic Label Regression for Generalized Noisy Supervision

https://github.com/SuperKam91/bnn

Weight Uncertainty in Neural Networks

https://github.com/Superlee506/Correlation_Filtering_Tracking_Lib

High-Speed Tracking with Kernelized Correlation Filters

https://github.com/TPiazza21/Thesis

Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain

https://github.com/TTL30/PPE-Deep-Distribituion-Regression

Deep Distribution Regression

https://github.com/Tan-Furukawa/badzupa

Laplace approximation for logistic Gaussian process density estimation and regression

https://github.com/TangShengqin/DynamicRCNN-mmdet

Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training

https://github.com/Tao-Mind/QDD_Net

SQuAD: 100,000+ Questions for Machine Comprehension of Text

https://github.com/TeaPearce/Deep_Learning_Prediction_Intervals

High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach

https://github.com/Tencent/ActionDetection-DBG

Fast Learning of Temporal Action Proposal via Dense Boundary Generator

https://github.com/TheBurrProject/A-Comparison-of-Machine-Learning-Algorithms-Applied-to-American-Legislature-Polarization

A Comparison of Machine Learning Algorithms Applied to American Legislature Polarization

https://github.com/ThirstyScholar/bayes-by-backprop

Weight Uncertainty in Neural Networks

https://github.com/ThomasGoerttler/similarity-analysis-of-maml

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/Tikquuss/meta_XLM

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/Tnorm/SVM_cookietest

Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks

https://github.com/TrustAI/AdversarialDriving

Adversarial Driving: Attacking End-to-End Autonomous Driving

https://github.com/TsaiLintung/fastdid

Difference-in-Differences when Parallel Trends Holds Conditional on Covariates

https://github.com/UBCDingXin/cGAN-KD

Distilling and Transferring Knowledge via cGAN-generated Samples for Image Classification and Regression

https://github.com/UlionTse/mlgb

Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

https://github.com/V0LsTeR/DQN_heap

Implicit Quantile Networks for Distributional Reinforcement Learning

https://github.com/V0LsTeR/dopamine_prioritized_buffer

Implicit Quantile Networks for Distributional Reinforcement Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.