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Data Compression

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

https://github.com/google/codex

An Introduction to Neural Data Compression

https://github.com/heartyguy/AI-AngryBird-Eagle-Wing

XGBoost: A Scalable Tree Boosting System

https://github.com/hrluo/sphericalrotationdimensionreduction

Efficient Manifold and Subspace Approximations with Spherelets

https://github.com/jerryji1993/dnabert

DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome

https://github.com/jiangzhongkai/ifly-algorithm_challenge

XGBoost: A Scalable Tree Boosting System

https://github.com/jimmylihui/OpenGenome

DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome

https://github.com/jimmylihui/genbench

DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome

https://github.com/jlanday/Language-Detection

XGBoost: A Scalable Tree Boosting System

https://github.com/jlanday/Model-Selection-for-Pion-Photoproduction

XGBoost: A Scalable Tree Boosting System

https://github.com/jlanday/Towards-the-Minimal-Spectrum-of-Excited-Baryons

XGBoost: A Scalable Tree Boosting System

https://github.com/jlanday/X-ray-Object-Classification

XGBoost: A Scalable Tree Boosting System

https://github.com/joezengcbs/mini_xgboost_from_scratch

XGBoost: A Scalable Tree Boosting System

https://github.com/koheiobata/dmm

Fast and Multi-aspect Mining of Complex Time-stamped Event Streams

https://github.com/kwantommy/breast-cancer-diagnosis

XGBoost: A Scalable Tree Boosting System

https://github.com/liuzhening111/bisic

LDMIC: Learning-based Distributed Multi-view Image Coding

https://github.com/lluckyyh/xtrust

Diff-eRank: A Novel Rank-Based Metric for Evaluating Large Language Models

https://github.com/lucasmfaria/Machine-Learning-Capstone

XGBoost: A Scalable Tree Boosting System

https://github.com/ma-lab-berkeley/crate

White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is?

https://github.com/mandt-lab/shallow-ntc

An Introduction to Neural Data Compression

https://github.com/matibilkis/qvans

A semi-agnostic ansatz with variable structure for quantum machine learning

https://github.com/mistic-lab/IPSW-RFI

SAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable Features

https://github.com/mmukhopadhyay/-Efficient-Manifold-Learning-Using-Spherelets

Efficient Manifold and Subspace Approximations with Spherelets

https://github.com/mtorabirad/PricePrediction

XGBoost: A Scalable Tree Boosting System

https://github.com/nsubbaian/FrequentistML

XGBoost: A Scalable Tree Boosting System

https://github.com/omglu93/energy_consumption_eda_prediction

XGBoost: A Scalable Tree Boosting System

https://github.com/osofr/xgboost

XGBoost: A Scalable Tree Boosting System

https://github.com/pierobeat/Hoax-News-Classification

XGBoost: A Scalable Tree Boosting System

https://github.com/poyushen/classifaction

XGBoost: A Scalable Tree Boosting System

https://github.com/py-econometrics/duckreg

You Only Compress Once: Optimal Data Compression for Estimating Linear Models

https://github.com/tensorflow/compression

An Introduction to Neural Data Compression

https://github.com/tqchen/xgboost

XGBoost: A Scalable Tree Boosting System

https://github.com/xiadanqing/Binary

XGBoost: A Scalable Tree Boosting System

https://github.com/yingyichen-cyy/Nested-Co-teaching

Boosting Co-teaching with Compression Regularization for Label Noise

https://github.com/yoshitomo-matsubara/head-network-distillation

BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing

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