Papers with Code 아카이브

Feature Importance

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

6

보존된 평가 기록

12

코드

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

https://github.com/yu-group/imodels-experiments

공식 코드

Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability

https://github.com/zahraatashgahi/QuickSelection

공식 코드

Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders

https://github.com/zahraatashgahi/neuron-attribution

공식 코드

Unveiling the Power of Sparse Neural Networks for Feature Selection

https://github.com/zaparolicunha/stl_surrogate

공식 코드

On Machine Learning-Driven Surrogates for Sound Transmission Loss Simulations

https://github.com/zfying/visfis

공식 코드

VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives

https://github.com/zhanglab-aim/emgnn

공식 코드

Explainable Multilayer Graph Neural Network for Cancer Gene Prediction

https://github.com/zhangrh93/InvertibleCE

공식 코드

Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors

https://github.com/zhangtp1996/openfe

공식 코드

OpenFE: Automated Feature Generation with Expert-level Performance

https://github.com/zhenguonie/2021_AI4MPP

공식 코드

Artificial Intelligence Enhanced Rapid and Efficient Diagnosis of Mycoplasma Pneumoniae Pneumonia in Children Patients

https://github.com/zheyuaqazhang/unbiasedgbm

공식 코드

Unbiased Gradient Boosting Decision Tree with Unbiased Feature Importance

https://gitlab.com/TStepi/spyct

공식 코드

Oblique Predictive Clustering Trees

https://git.openi.org.cn/OpenModelZoo/FAT-deepFFM

FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine

https://github.com/2023-MindSpore-1/ms-code-220/tree/main/Fat-DeepFFM

FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine

https://github.com/2023-MindSpore-1/ms-code-220/tree/main/fibinet

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

https://github.com/AFIT-R/vip

Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations

https://github.com/Andy1314Chen/FAT-DeepFFM-Paddle

FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine

https://github.com/ArjunS07/pu-learning-for-frbs-2023

Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization

https://github.com/Blunde1/aGTBoost

agtboost: Adaptive and Automatic Gradient Tree Boosting Computations

https://github.com/DSXiangLi/CTR

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

https://github.com/DataCanvasIO/DeepTables

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

https://github.com/DiKorsch/l1_parts

Classification-Specific Parts for Improving Fine-Grained Visual Categorization

https://github.com/Eric-Wallace/trickme-interface

Interpreting Neural Networks With Nearest Neighbors

https://github.com/GISH123/Cathay-Holdings-CIP-Projects-for-Interpretable-Machine-Learning

A Unified Approach to Interpreting Model Predictions

https://github.com/GuanSuns/Simple-Human-in-the-Loop-ML-Interface

Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation

https://github.com/HaSai666/rec_pangu

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

https://github.com/Hirosora/LightCTR

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

https://github.com/ID-EDGe/XAI-PV

Neural network interpretability for forecasting of aggregated renewable generation

https://github.com/Jllamoza/bbva_attrition

Distributed and parallel time series feature extraction for industrial big data applications

https://github.com/Jllamoza/bbva_attrition_FRESH

Distributed and parallel time series feature extraction for industrial big data applications

https://github.com/LLNL/fastcam

A Benchmark for Interpretability Methods in Deep Neural Networks

https://github.com/LaurentLava/SHAP

A Unified Approach to Interpreting Model Predictions

https://github.com/LinJayan/FAT_DeepFFM_Paddle

FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine

https://github.com/LisaKouts/Implicit_bias_FCMs

Measuring Implicit Bias Using SHAP Feature Importance and Fuzzy Cognitive Maps

https://github.com/Luab/Explanation_by_Progressive_Exaggeration_Pytorch

Explanation by Progressive Exaggeration

https://github.com/MKowal2/ACE_TCAV_Pytorch

Towards Automatic Concept-based Explanations

https://github.com/MachineLearningJournalClub/LearningNLP

A Unified Approach to Interpreting Model Predictions

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