Papers with Code 아카이브

regression

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

2

평가표

6

보존된 평가 기록

15

코드

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

https://github.com/PhIMaL/DeePyMoD_torch

DeepMoD: Deep learning for Model Discovery in noisy data

https://github.com/PhilipBAdams/faiss-learned-termination-prior-weighted

Link and code: Fast indexing with graphs and compact regression codes

https://github.com/PhilippBach/hdm

High-Dimensional Metrics in R

https://github.com/PhilippBach/hdm_prev

High-Dimensional Metrics in R

https://github.com/Ping-C/CertifiedObjectDetection

Detection as Regression: Certified Object Detection by Median Smoothing

https://github.com/Pongpisit-Thanasutives/CHS

Improved identification accuracy in equation learning via comprehensive $\boldsymbol{R^2}$-elimination and Bayesian model selection

https://github.com/PopovicMilica/causal_nets

Deep Neural Networks for Estimation and Inference

https://github.com/QUVA-Lab/Spherical_Regression

Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on n-Spheres

https://github.com/Qinhj07/ATOMCode

Acquisition of Localization Confidence for Accurate Object Detection

https://github.com/R-Gaurav/train-delay-estimation

Estimating Train Delays in a Large Rail Network Using a Zero Shot Markov Model

https://github.com/R-Gaurav/train-delay-estimation-ITSC2018

Estimating Train Delays in a Large Rail Network Using a Zero Shot Markov Model

https://github.com/RachitChhaya/RegularizedRegressionCoresets

On Coresets For Regularized Regression

https://github.com/RemyMess/auto-sig-encoder

Learning from the past, predicting the statistics for the future, learning an evolving system

https://github.com/ReubenDo/U-HVED

NiftyNet: a deep-learning platform for medical imaging

https://github.com/RichardMinsooGo-ML/Pytorch-Complex-Yolo-Yolov3

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

https://github.com/RichardMinsooGo-ML/Pytorch-Complex-Yolo-Yolov4

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

https://github.com/RichardMinsooGo-ML/Pytorch-Yolo-3d-Yolov3

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

https://github.com/RichardMinsooGo-ML/Pytorch-Yolo-3d-Yolov4

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

https://github.com/RobvanGastel/meta-in-context-learning

Transformers learn in-context by gradient descent

https://github.com/Rose-STL-Lab/Spatiotemporal_UQ

Quantifying Uncertainty in Deep Spatiotemporal Forecasting

https://github.com/RuijieZhu94/mmdepth

BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation

https://github.com/RuiminChen/GIou_loss_caffe

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

https://github.com/RuiminChen/GIouloss_CIouloss_caffe

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

https://github.com/Runist/torch_maml

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

https://github.com/SPEAR3-ML/MG-GPO

Multi-objective multi-generation Gaussian process optimizer for design optimization

https://github.com/SSS135/aiqn-vae

Autoregressive Quantile Networks for Generative Modeling

https://github.com/STAC-USC/NNK_graph_construction

Neighborhood and Graph Constructions using Non-Negative Kernel Regression

https://github.com/SajithMR/Counting_plant_parts

Improving Object Counting with Heatmap Regulation

https://github.com/Sarathismg/Categorized-Crowd-Count-Test-on-Decidenet-

DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation

https://github.com/Sense-X/TSD

Revisiting the Sibling Head in Object Detector

https://github.com/SergiBragos/DLAI-2021-project

Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network

https://github.com/SeungeonBaek/discrete-agents-test

Distributional Reinforcement Learning with Quantile Regression

https://github.com/SeungyounShin/Adaptive-Wing-Loss-for-Robust-Face-Alignment-via-Heatmap-Regression

Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression

https://github.com/SharadGitHub/Self-Supervised-Autoencoder

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

https://github.com/Shualite/UBNet

Sliding Line Point Regression for Shape Robust Scene Text Detection

https://github.com/Shujian2015/meta-embedding-paper-list

Linear Ensembles of Word Embedding Models

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