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Ensemble Learning

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코드

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

https://github.com/yzhao062/combo

공식 코드

Combining Machine Learning Models using combo Library

https://github.com/zcrwind/prefer

공식 코드

PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refine

https://github.com/zhaochangming/boostforest

공식 코드

BoostTree and BoostForest for Ensemble Learning

https://github.com/zhiningliu1998/imbalanced-ensemble

공식 코드

IMBENS: Ensemble Class-imbalanced Learning in Python

https://github.com/zhouzihao501/nlpcc2023-shared-task3-chinesemwp

공식 코드

Solving Math Word Problem with Problem Type Classification

https://github.com/zhxiaokang/EFSIS

공식 코드

EFSIS: Ensemble Feature Selection Integrating Stability

https://github.com/zly555/mpewsvms

공식 코드

Linear Algorithms for Robust and Scalable Nonparametric Multiclass Probability Estimation

https://github.com/zuchniakk/mtkd

공식 코드

Multi-teacher knowledge distillation as an effective method for compressing ensembles of neural networks

https://github.com/zuchniakk/votingalgorithms

공식 코드

Multi-teacher knowledge distillation as an effective method for compressing ensembles of neural networks

https://gitlab.com/dbdmg/dac

공식 코드

Scaling associative classification for very large datasets

https://github.com/AceChuse/DCN

Decoder Choice Network for Meta-Learning

https://github.com/Adlik/model_optimizer

Ensemble Knowledge Distillation for Learning Improved and Efficient Networks

https://github.com/Alishaba/pykeen-snapshot-ensembles

Multiple Run Ensemble Learning with Low-Dimensional Knowledge Graph Embeddings

https://github.com/Anonnoname/G2GT_2

G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-Training

https://github.com/BeechburgPieStar/LabCode

Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning

https://github.com/Chao-Chen-Hao/Rethinking-EnD-SegUDA

Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaptation

https://github.com/Claydon-Wang/Anti-random-pruning

Prune and Tune Ensembles: Low-Cost Ensemble Learning With Sparse Independent Subnetworks

https://github.com/FaceOnLive/Face-Liveness-Detection-SDK-Linux

Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing

https://github.com/FilippoMB/TCK_AE

Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data

https://github.com/GJTNB/reading-memo

Multiple Expert Brainstorming for Domain Adaptive Person Re-identification

https://github.com/KaiyangZhou/mixstyle-release

Domain Generalization: A Survey

https://github.com/LARS-research/RelEns

Relation-aware Ensemble Learning for Knowledge Graph Embedding

https://github.com/MS-P3/code6/tree/main/peft

PEFT for Speech: Unveiling Optimal Placement, Merging Strategies, and Ensemble Techniques

https://github.com/MindCode-4/code-3/tree/main/funnel

Funnelling: A New Ensemble Method for Heterogeneous Transfer Learning and its Application to Cross-Lingual Text Classification

https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection

Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing

https://github.com/RobertOrmandi/Gossip-Learning-Framework

Gossip Learning with Linear Models on Fully Distributed Data

https://github.com/Siddaarth-Babu/Prediction-of-Stock-Market-using-RF

Predicting the direction of stock market prices using random forest

https://github.com/arrival-ltd/catalyst-rl-tutorial

Sample Efficient Ensemble Learning with Catalyst.RL

https://github.com/ashokkumar-kola/FYP

Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Models

https://github.com/benedekrozemberczki/BoostedFactorization

Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation

https://github.com/benedekrozemberczki/karateclub

Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation

https://github.com/chunhuililili/mt_dnn

Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding

https://github.com/cpapadimitriou/Click-Through-Rate-prediction

An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy

https://github.com/dslisleedh/EnsNet-tensorflow2

Ensemble learning in CNN augmented with fully connected subnetworks

https://github.com/eaguaida/TF_EnsNet-

Ensemble learning in CNN augmented with fully connected subnetworks

https://github.com/ensta-u2is/torch-uncertainty

Masksembles for Uncertainty Estimation

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