Papers with Code 아카이브

regression

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코드

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

https://github.com/mayer79/missRanger

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

https://github.com/maziarraissi/ParametricGP

Parametric Gaussian Process Regression for Big Data

https://github.com/meghshukla/MPII-Human-Pose-Visualization

Bayesian Uncertainty and Expected Gradient Length -- Regression: Two Sides Of The Same Coin?

https://github.com/mejbah/AutoPerf

A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions

https://github.com/melikaemami/Generalization-Error-of-GLMs

Generalization Error of Generalized Linear Models in High Dimensions

https://github.com/meteahishali/srl-soa

Representation Based Regression for Object Distance Estimation

https://github.com/michel-liu/grouppose

QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Query

https://github.com/michel-liu/grouppose-paddle

QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Query

https://github.com/microsoft/CBM

Cyclic Boosting -- an explainable supervised machine learning algorithm

https://github.com/microsoft/archai

Efficient Forward Architecture Search

https://github.com/mikehuisman/revisiting-learned-optimizers

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

https://github.com/mikgur/MADE_CV_1000_facial_landmarks

Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression

https://github.com/mindspore-ai/contrib/blob/master/application/AccSGD-Parallelizing-Stochastic-Gradient-Descent/AccSGD.py

Parallelizing Stochastic Gradient Descent for Least Squares Regression: mini-batching, averaging, and model misspecification

https://github.com/mindspore-ai/models/tree/master/research/cv/tall

TALL: Temporal Activity Localization via Language Query

https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/PolarizedSelfAttention.py

Polarized Self-Attention: Towards High-quality Pixel-wise Regression

https://github.com/ming71/UCAS-AOD-benchmark

Dynamic Anchor Learning for Arbitrary-Oriented Object Detection

https://github.com/minoring/PRNet

Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network

https://github.com/minseop-aitrics/FewshotLearning

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

https://github.com/mitmul/deeppose

DeepPose: Human Pose Estimation via Deep Neural Networks

https://github.com/mivg/sled

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

https://github.com/mkartikkumar/re3-tensorflow

Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects

https://github.com/mkazmier/torchmtlr

Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework

https://github.com/mlr-org/mlr

mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions

https://github.com/modeeric/eegvit-tcnet

ViT2EEG: Leveraging Hybrid Pretrained Vision Transformers for EEG Data

https://github.com/mohsenshahhosseini/GEM-ITH-Ensemble

Optimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problems

https://github.com/mojivalipour/symbolicgpt

SymbolicGPT: A Generative Transformer Model for Symbolic Regression

https://github.com/monologg/EncT5

EncT5: A Framework for Fine-tuning T5 as Non-autoregressive Models

https://github.com/mpimd-csc/RK4-SinDy

Discovery of Nonlinear Dynamical Systems using a Runge-Kutta Inspired Dictionary-based Sparse Regression Approach

https://github.com/mrahtz/learning-from-human-preferences

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/mrfanuel/denoising-modulo-samples-k-NN-regression

Denoising modulo samples: k-NN regression and tightness of SDP relaxation

https://github.com/mrsalehi/ground-sentence-video

TALL: Temporal Activity Localization via Language Query

https://github.com/mrvoh/meta_learning_multilingual_doc_classification

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

https://github.com/mshehrozsajjad/Age-Classification

Rank consistent ordinal regression for neural networks with application to age estimation

https://github.com/mvaldenegro/keras-uncertainty

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/natdebru/OpenCV-Video-Label

Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects

https://github.com/nehap25/rlwithgp

Manifold Gaussian Processes for Regression

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