Papers with Code 아카이브

regression

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

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/VCL3D/3D60

$360^o$ Surface Regression with a Hyper-Sphere Loss

https://github.com/VCL3D/SphericalViewSynthesis

$360^o$ Surface Regression with a Hyper-Sphere Loss

https://github.com/VL97/Pose-estimation-using-Deep-CNN-encoder

DeepPose: Human Pose Estimation via Deep Neural Networks

https://github.com/VNOpenAI/tf-blazepose

BlazePose: On-device Real-time Body Pose tracking

https://github.com/Vatshayan/Final-Year-Machine-Learning-Stock-Price-Prediction-Project

Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models

https://github.com/Vatshayan/Final-Year-Project-Stock-Price-Prediction-by-Deep-Learning

Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models

https://github.com/Vivek-23-Titan/Crowd_Detection

Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection

https://github.com/WenjieDu/SAITS

BRITS: Bidirectional Recurrent Imputation for Time Series

https://github.com/Westlake-AI/openmixup

Switch EMA: A Free Lunch for Better Flatness and Sharpness

https://github.com/Windxy/BayesianCNN-Based-on-Paddle

Weight Uncertainty in Neural Networks

https://github.com/WuJie1010/TSP-PRL

TALL: Temporal Activity Localization via Language Query

https://github.com/WuJie1010/Temporally-language-grounding

TALL: Temporal Activity Localization via Language Query

https://github.com/XiaoouPan/tfHuber

Iteratively Reweighted $\ell_1$-Penalized Robust Regression

https://github.com/XueningZhu/dlsa_r

Least Squares Approximation for a Distributed System

https://github.com/YRussac/WeightedLinearBandits

Weighted Linear Bandits for Non-Stationary Environments

https://github.com/YutongWangUMich/UCI_classification

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

https://github.com/ZC119/richardson_tracking

High-Speed Tracking with Kernelized Correlation Filters

https://github.com/ZJULearning/graph_level_drug_discovery

Learning Graph-Level Representation for Drug Discovery

https://github.com/ZJULearning/pixel_link

PixelLink: Detecting Scene Text via Instance Segmentation

https://github.com/ZLHe0/TransFusion

TransFusion: Covariate-Shift Robust Transfer Learning for High-Dimensional Regression

https://github.com/ZekuiQin/SiamKPN

Fully Convolutional Online Tracking

https://github.com/ZhangShiyue/QGforQA

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

https://github.com/ZhenhuaShi/CDR-FCM-RDpA

FCM-RDpA: TSK Fuzzy Regression Model Construction Using Fuzzy C-Means Clustering, Regularization, DropRule, and Powerball AdaBelief

https://github.com/Zhiwei-Z/PrompLimitTest

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

https://github.com/Zhiwei-Z/SeqPromp

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

https://github.com/Zhiwei-Z/prompzzw

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

https://github.com/ZiyiLiubird/DeepMARL-PyTorch

Distributional Reinforcement Learning with Quantile Regression

https://github.com/Zzh-tju/DIoU-SSD-pytorch

Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

https://github.com/Zzh-tju/DIoU-darknet

Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

https://github.com/Zzh-tju/DIoU-pytorch-detectron

Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

https://github.com/Zzh-tju/ultralytics-YOLOv3-Cluster-NMS

Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

https://github.com/aaronhan223/htsf

Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series

https://github.com/abhran/Pixel-Recursive-Super-resolution

Pixel Recursive Super Resolution

https://github.com/abogatskiy/pelican-nano

PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics

https://github.com/acca3003/ngboostR

NGBoost: Natural Gradient Boosting for Probabilistic Prediction

https://github.com/adamribaudo/ds5500-capstone

Churn Prediction with Sequential Data and Deep Neural Networks. A Comparative Analysis

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