Papers with Code 아카이브

regression

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하위 태스크

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6

보존된 평가 기록

15

코드

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

https://github.com/ZilongTan/sigp

공식 코드

Learning Integral Representations of Gaussian Processes

https://github.com/ZizhouJia/PDANet

공식 코드

PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression

https://github.com/ZoneTsuyoshi/grid-based-lipschitz-penalty

공식 코드

Aortic root landmark localization with optimal transport loss for heatmap regression

https://github.com/Zongliang-Wu/LADE-DUN

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Latent Diffusion Prior Enhanced Deep Unfolding for Snapshot Spectral Compressive Imaging

https://github.com/Zzh-tju/CIoU

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Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation

https://github.com/Zzh-tju/DIoU

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Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

https://github.com/a-jd/npsn

공식 코드

Deep Surrogate Models for Multi-dimensional Regression of Reactor Power

https://github.com/aaamini/reg_lattice_comp

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The neighborhood lattice for encoding partial correlations in a Hilbert space

https://github.com/aagarwal1996/additive_trees

공식 코드

A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds

https://github.com/aahrens1/pystacked

공식 코드

pystacked: Stacking generalization and machine learning in Stata

https://github.com/aamini/evidential-deep-learning

공식 코드

Deep Evidential Regression

https://github.com/aangelopoulos/im2im-uq

공식 코드

Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging

https://github.com/aangelopoulos/ppi_py

공식 코드

Prediction-Powered Inference

https://github.com/aangelopoulos/prediction-powered-inference

공식 코드

Prediction-Powered Inference

https://github.com/aaronbtb/mnerv

공식 코드

MNeRV: A Multilayer Neural Representation for Videos

https://github.com/aaronge-2020/deepimpute

공식 코드

FastImpute: A Baseline for Open-source, Reference-Free Genotype Imputation Methods -- A Case Study in PRS313

https://github.com/aaronhan223/slcp

공식 코드

Split Localized Conformal Prediction

https://github.com/ab9mamun/movesense

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Multimodal Physical Activity Forecasting in Free-Living Clinical Settings: Hunting Opportunities for Just-in-Time Interventions

https://github.com/abdo-eldesokey/nconv

공식 코드

Confidence Propagation through CNNs for Guided Sparse Depth Regression

https://github.com/abhi1kumar/seabird

공식 코드

SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects

https://github.com/abhin02/fair-selective-regression

공식 코드

Selective Regression Under Fairness Criteria

https://github.com/abir-de/SELCON

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Training Data Subset Selection for Regression with Controlled Generalization Error

https://github.com/abogatskiy/pelican

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PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics

https://github.com/acabassi/logistic-regression-for-multi-omic-data

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Two-step penalised logistic regression for multi-omic data with an application to cardiometabolic syndrome

https://github.com/acbull/HiCE

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Few-Shot Representation Learning for Out-Of-Vocabulary Words

https://github.com/aclyde11/regression_enrichment_surface

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Regression Enrichment Surfaces: a Simple Analysis Technique for Virtual Drug Screening Models

https://github.com/acstutts/ceqr-dqn

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Echoes of Socratic Doubt: Embracing Uncertainty in Calibrated Evidential Reinforcement Learning

https://github.com/activevisionlab/dfnet

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DFNet: Enhance Absolute Pose Regression with Direct Feature Matching

https://github.com/adaa-polsl/GuideR

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GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settings

https://github.com/adaa-polsl/RuleKit

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RuleKit: A Comprehensive Suite for Rule-Based Learning

https://github.com/adalca/voxelorb

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Predictive Modeling of Anatomy with Genetic and Clinical Data

https://github.com/adamkhakhar/autoregreg

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Neural Regression For Scale-Varying Targets

https://github.com/adamlauretig/bwe

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Identification, Interpretability, and Bayesian Word Embeddings

https://github.com/adamlauretig/bwe_application_naacl_2019

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Identification, Interpretability, and Bayesian Word Embeddings

https://github.com/adapt-python/adapt

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Adversarial Weighting for Domain Adaptation in Regression

https://github.com/adgress/ICDM2016

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Probabilistic Formulations of Regression with Mixed Guidance

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