Papers with Code 아카이브

regression

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하위 태스크

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코드

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

https://github.com/neural-structured-additive-learning/deepregression

deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression

https://github.com/ngiann/ApproximateVI.jl

Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression

https://github.com/ngoix/ocrf

One Class Splitting Criteria for Random Forests

https://github.com/ngorbach/Variational_Gradient_Matching_for_Dynamical_Systems

Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs

https://github.com/nianticlabs/relpose-gnn

Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervision

https://github.com/nickfrosst/neural_additive_models

Neural Additive Models: Interpretable Machine Learning with Neural Nets

https://github.com/niklasschmitz/MultiresolutionKernelApproximationSlides

Multiresolution Kernel Approximation for Gaussian Process Regression

https://github.com/nisl-msu/adaptivesampling

Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation

https://github.com/nkolot/GraphCMR

Convolutional Mesh Regression for Single-Image Human Shape Reconstruction

https://github.com/nlarki/ParcelLab-Task

Boosting Algorithms for Delivery Time Prediction in Transportation Logistics

https://github.com/nliulab/sim-shapley

Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression

https://github.com/nnzzll/scn

Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localization

https://github.com/nomercy77/Implementing-Bayesian-CNN

Weight Uncertainty in Neural Networks

https://github.com/noraschneider/anchordataaugmentation

Anchor Data Augmentation

https://github.com/novicestone/meta-cetm

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

https://github.com/nrupatunga/PY-GOTURN

Learning to Track at 100 FPS with Deep Regression Networks

https://github.com/nrupatunga/goturn-pytorch

Learning to Track at 100 FPS with Deep Regression Networks

https://github.com/nunompmoniz/ElectionData

Real-time 2019 Portuguese Parliament Election Results Dataset

https://github.com/nutonomy/nuscenes-devkit

CoverNet: Multimodal Behavior Prediction using Trajectory Sets

https://github.com/nvlabs/gbrl_sb3

Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

https://github.com/nyk510/simple-ngboost

NGBoost: Natural Gradient Boosting for Probabilistic Prediction

https://github.com/nyu-larx/attack_metarl

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

https://github.com/o-waring/subseasonal_forecasting

Improving Subseasonal Forecasting in the Western U.S. with Machine Learning

https://github.com/oist-cnru/GLean

A Novel Predictive-Coding-Inspired Variational RNN Model for Online Prediction and Recognition

https://github.com/omacshane/basicbayesDL

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

https://github.com/omegafragger/DDU

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

https://github.com/opconty/pixellink_keras

PixelLink: Detecting Scene Text via Instance Segmentation

https://github.com/open-edge-platform/geti

D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement

https://github.com/open-edge-platform/training_extensions

D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement

https://github.com/open-mmlab/mmpose

DeepPose: Human Pose Estimation via Deep Neural Networks

https://github.com/open-mmlab/mmtracking

Probabilistic Regression for Visual Tracking

https://github.com/opendilab/DI-engine/blob/main/ding/policy/iqn.py

Implicit Quantile Networks for Distributional Reinforcement Learning

https://github.com/opendilab/DI-engine/blob/main/ding/policy/qrdqn.py

Distributional Reinforcement Learning with Quantile Regression

https://github.com/opendrivelab/elm

SRFormer: Text Detection Transformer with Incorporated Segmentation and Regression

https://github.com/oscarknagg/few-shot

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

https://github.com/osman-haider/Decision-Tree-Stream

Decision Stream: Cultivating Deep Decision Trees

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