Papers with Code 아카이브

regression

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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/GauthierDmn/question_answering

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

https://github.com/GauthierDmn/question_generation

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

https://github.com/GeorgeDUT/MetaRLSAS

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

https://github.com/Giveupfree/SiamCAR-CAM

SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking

https://github.com/Glutamat42/epistemic_ensemble_toy_dataset

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

https://github.com/GoingMyWay/dopamine_reward_decomposition

Implicit Quantile Networks for Distributional Reinforcement Learning

https://github.com/Guang000/Awesome-Dataset-Distillation

Dataset Meta-Learning from Kernel Ridge-Regression

https://github.com/Guanghan/ROLO

Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking

https://github.com/HKUST-KnowComp/R-Net

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

https://github.com/HSE-LAMBDA/roerich

Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation

https://github.com/Habush/nam_jax

Neural Additive Models: Interpretable Machine Learning with Neural Nets

https://github.com/HelmholtzAI-Consultants-Munich/PySDDR

Semi-Structured Distributional Regression -- Extending Structured Additive Models by Arbitrary Deep Neural Networks and Data Modalities

https://github.com/HilbertXu/MAML-Tensorflow

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

https://github.com/HomoSapienLCY/Contextual_Regression

Contextual Regression: An Accurate and Conveniently Interpretable Nonlinear Model for Mining Discovery from Scientific Data

https://github.com/Hui-Li/multi-task-learning-example-PyTorch

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

https://github.com/HuiHu1/Distributed-Private-Fair-Learning

Fair Kernel Regression via Fair Feature Embedding in Kernel Space

https://github.com/IBCNServices/GENESIM

Interpreting Tree Ensembles with inTrees

https://github.com/IBM/UQ360

Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervals

https://github.com/InfoX-SEU/DAE_AQA

Auto-Encoding Score Distribution Regression for Action Quality Assessment

https://github.com/Instinct323/wiou/blob/v2/iou.py

Focal and Efficient IOU Loss for Accurate Bounding Box Regression

https://github.com/IntelLabs/bayesian-torch

Weight Uncertainty in Neural Networks

https://github.com/Irautak/FingerFlex

FingerFlex: Inferring Finger Trajectories from ECoG signals

https://github.com/Irisfee/spotify_analysis

Hit Song Prediction for Pop Music by Siamese CNN with Ranking Loss

https://github.com/IyarLin/paper_results_reproduction

More Data Can Hurt for Linear Regression: Sample-wise Double Descent

https://github.com/JWLou/m3DFaceTrack

Real-time 3D Facial Tracking via Cascaded Compositional Learning

https://github.com/JWSoh/MZSR

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

https://github.com/JakeColtman/bartpy

BART: Bayesian additive regression trees

https://github.com/JaryHuang/awesome_SSD_FPN_GIoU

Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression

https://github.com/JeonMinkyu/MAML_Pytorch

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

https://github.com/Jia-Research-Lab/Simple-SR

LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyond

https://github.com/Jllamoza/bbva_attrition

Distributed and parallel time series feature extraction for industrial big data applications

https://github.com/Jllamoza/bbva_attrition_FRESH

Distributed and parallel time series feature extraction for industrial big data applications

https://github.com/JoegameZhou/PLR_Mindspore_zj

Partial-Label Regression

https://github.com/JohannesMaxWel/neural_random_forests

Neural Random Forests

https://github.com/JoonHyung-Park/DeepEnsemble

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

https://github.com/Junghwan-brian/SDE-Net

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

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