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/Leo-Simpson/c-lasso

공식 코드

c-lasso -- a Python package for constrained sparse and robust regression and classification

https://github.com/LeoYu/neural-tangent-kernel-UCI

공식 코드

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

https://github.com/Li-Wanhua/POEs

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Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression

https://github.com/LifangHe/NeurIPS18_SURF

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Tensor Regression

https://github.com/LifangHe/SURF

공식 코드

Boosted Sparse and Low-Rank Tensor Regression

https://github.com/LiliMeng/btrf

공식 코드

Backtracking Regression Forests for Accurate Camera Relocalization

https://github.com/LiuHaiTao01/GRBCM

공식 코드

Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression

https://github.com/LucasKookUZH/ontram-paper

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Deep and interpretable regression models for ordinal outcomes

https://github.com/MCG-NJU/FCOT

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Fully Convolutional Online Tracking

https://github.com/MCG-NJU/SADRNet

공식 코드

SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction

https://github.com/MCG-NJU/TREG

공식 코드

Target Transformed Regression for Accurate Tracking

https://github.com/MIC-DKFZ/DetectionAndRegression

공식 코드

Reg R-CNN: Lesion Detection and Grading under Noisy Labels

https://github.com/MIC-DKFZ/RegRCNN

공식 코드

Reg R-CNN: Lesion Detection and Grading under Noisy Labels

https://github.com/MIDA-group/Cell-Detection

공식 코드

A Comparison of Deep Learning Methods for Cell Detection in Digital Cytology

https://github.com/MIDA-group/OralScreen

공식 코드

A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images

https://github.com/MIT-SPARK/estimation-contracts

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Estimation Contracts for Outlier-Robust Geometric Perception

https://github.com/MKaptein/ofmlr

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Maximum likelihood estimation of a finite mixture of logistic regression models in a continuous data stream

https://github.com/ML-GSAI/Revisiting-Dis-vs-Gen-Classifiers

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Revisiting Discriminative vs. Generative Classifiers: Theory and Implications

https://github.com/MOhammedJAbi/SoftKMeans

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Deep clustering: On the link between discriminative models and K-means

https://github.com/MSNetrom/in-context-poly-playground

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Polynomial Regression as a Task for Understanding In-context Learning Through Finetuning and Alignment

https://github.com/MaLeLabTs/GPFormulasInterpretability

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Learning a Formula of Interpretability to Learn Interpretable Formulas

https://github.com/Machine-Learning-Dynamical-Systems/kooplearn

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Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces

https://github.com/Magica-Chen/gptp_multi_output

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Multivariate Gaussian and Student$-t$ Process Regression for Multi-output Prediction

https://github.com/Majeed7/L1LR

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An efficient projection neural network for $\ell_1$-regularized logistic regression

https://github.com/MansoorehMontazerin/LIES

공식 코드

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression

https://github.com/Mathux/Fall

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Fast local linear regression with anchor regularization

https://github.com/MattWet/stagewise

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Stagewise Boosting Distributional Regression

https://github.com/MedMNIST/MedMNIST

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MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis

https://github.com/Mehran-k/RelNN

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RelNN: A Deep Neural Model for Relational Learning

https://github.com/Menace-Dragon/VP-QOD

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HQOD: Harmonious Quantization for Object Detection

https://github.com/MiaHRT/CAWR

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CAWR: Corruption-Averse Advantage-Weighted Regression for Robust Policy Optimization

https://github.com/Mic-map/PIRACY

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An optimized pipeline for functional connectivity analysis in the rat brain

https://github.com/Microsoft/StateDecoding

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Provably efficient RL with Rich Observations via Latent State Decoding

https://github.com/MikeOMa/MV_Prediction

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Multivariate Probabilistic Regression with Natural Gradient Boosting

https://github.com/MilesCranmer/PySR

공식 코드

Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

https://github.com/Mingpan/generative_map

공식 코드

A Generative Map for Image-based Camera Localization

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