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/johnhalloran321/cvxpy_classifiers

Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization

https://github.com/jonasrothfuss/promp

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

https://github.com/jpcreis/Hyper-Process-Model

Hyper-Process Model: A Zero-Shot Learning algorithm for Regression Problems based on Shape Analysis

https://github.com/jstriaukas/midasml

High-Dimensional Granger Causality Tests with an Application to VIX and News

https://github.com/julianbertini/MSNet

NiftyNet: a deep-learning platform for medical imaging

https://github.com/junjya/faiss

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

https://github.com/juntang-zhuang/explain_invertible

Invertible Network for Classification and Biomarker Selection for ASD

https://github.com/junwenbai/correlation-gnn

Residual Correlation in Graph Neural Network Regression

https://github.com/justinkay/repulsion-loss-detectron2

Repulsion Loss: Detecting Pedestrians in a Crowd

https://github.com/justusschock/shape-constrained-network

Super-realtime facial landmark detection and shape fitting by deep regression of shape model parameters

https://github.com/jwzhanggy/DifNet

Graph Neural Lasso for Dynamic Network Regression

https://github.com/kalubin-awym/GIoU-loss-for-RetinaNet

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

https://github.com/karunru/ConvexFactorizationMachines.jl

Convex Factorization Machine for Regression

https://github.com/katerakelly/pytorch-maml

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

https://github.com/katerynaCh/fastSDA

Speed-up and multi-view extensions to Subclass Discriminant Analysis

https://github.com/kawshik8/DL-project

Learning Modulated Loss for Rotated Object Detection

https://github.com/kclip/r_autoeval_plus

Prediction-Powered Inference

https://github.com/keleog/PidginUNMT

Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion

https://github.com/kenya-sk/mc_dropout_tensorflow

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

https://github.com/kernel-enthusiasts/KF_NN2

Deep regularization and direct training of the inner layers of Neural Networks with Kernel Flows

https://github.com/kernelmodedec/Kernel-Mode-Decomposition-1D

Kernel Mode Decomposition and programmable/interpretable regression networks

https://github.com/kevinmoonlab/rf-gap

Geometry- and Accuracy-Preserving Random Forest Proximities

https://github.com/kherud/neural-additive-models-pt

Neural Additive Models: Interpretable Machine Learning with Neural Nets

https://github.com/kibernetika-ai/re3-tensorflow

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

https://github.com/kimmeen/sl-gad

Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection

https://github.com/kingcong/tall

TALL: Temporal Activity Localization via Language Query

https://github.com/kirumang/Pix2Pose

Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation

https://github.com/kobybibas/pnml_linear_regression

A New Look at an Old Problem: A Universal Learning Approach to Linear Regression

https://github.com/kobybibas/pnml_linear_regression_isit_2019_latex

A New Look at an Old Problem: A Universal Learning Approach to Linear Regression

https://github.com/kobybibas/pnml_linear_regression_simulation

A New Look at an Old Problem: A Universal Learning Approach to Linear Regression

https://github.com/kosyoshida/simple-keras

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

https://github.com/krutikabapat/Crowd_Counting

Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking

https://github.com/kshmawj111/solar_energy_forecast

A Multi-Horizon Quantile Recurrent Forecaster

https://github.com/ku2482/fqf-iqn-qrdqn.pytorch

Distributional Reinforcement Learning with Quantile Regression

https://github.com/ku2482/rljax

Distributional Reinforcement Learning with Quantile Regression

https://github.com/kul-forbes/CIAOAlgorithms

Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives

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