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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/tuatruog/astrocompress

공식 코드

AstroCompress: A benchmark dataset for multi-purpose compression of astronomical data

https://github.com/ustc-starteam/zip

공식 코드

Entropy Law: The Story Behind Data Compression and LLM Performance

https://github.com/v0lta/wavelet-network-compression

공식 코드

Neural network compression via learnable wavelet transforms

https://github.com/waltonfuture/Diff-eRank

공식 코드

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

https://github.com/xand-stapleton/fisher_pruning

공식 코드

Bayesian Renormalization

https://github.com/xinjie-q/ldmic

공식 코드

LDMIC: Learning-based Distributed Multi-view Image Coding

https://github.com/xinyandai/product-quantization

공식 코드

Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search

https://github.com/yokoxue/HRP

공식 코드

Efficient Sparse Coding using Hierarchical Riemannian Pursuit

https://github.com/yoshitomo-matsubara/bottlefit-split_computing

공식 코드

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

https://github.com/yoshitomo-matsubara/sc2-benchmark

공식 코드

SC2 Benchmark: Supervised Compression for Split Computing

https://github.com/yoshitomo-matsubara/supervised-compression

공식 코드

Supervised Compression for Resource-Constrained Edge Computing Systems

https://github.com/zhangmozhe/microshift_compression

공식 코드

Microshift: An Efficient Image Compression Algorithm for Hardware

https://github.com/zhihan1996/dnabert_2

공식 코드

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

https://github.com/1082-datascience/finalproject-finalproject-1082ds_group3

XGBoost: A Scalable Tree Boosting System

https://github.com/360jinrong/GBST

XGBoost: A Scalable Tree Boosting System

https://github.com/Alexei-WLU/gpt-ocr-entropy-analysis

Quantum Data Compression and Quantum Cross Entropy

https://github.com/Automunge/AutoMunge

XGBoost: A Scalable Tree Boosting System

https://github.com/CODARcode/MGARD

Scalable Hybrid Learning Techniques for Scientific Data Compression

https://github.com/Fickle519/stockforecastingmodelbasedon_XGboost

XGBoost: A Scalable Tree Boosting System

https://github.com/Guang000/Awesome-Dataset-Distillation/blob/main/README.md

DC-BENCH: Dataset Condensation Benchmark

https://github.com/Hem7513/Decision-Trees-and-XGBoost-Algorithm-Documentation

XGBoost: A Scalable Tree Boosting System

https://github.com/Javihaus/Auto-ML-app-with-dash

XGBoost: A Scalable Tree Boosting System

https://github.com/KPIxLILU/Machine-Learning-Workshop

XGBoost: A Scalable Tree Boosting System

https://github.com/MindCode-4/code-11/tree/main/entropy-law

Entropy Law: The Story Behind Data Compression and LLM Performance

https://github.com/MindCode-4/code-6/tree/main/entropy-law

Entropy Law: The Story Behind Data Compression and LLM Performance

https://github.com/MrCat9/Sklearn_Note

XGBoost: A Scalable Tree Boosting System

https://github.com/WallaceLiu/product-quantization

Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search

https://github.com/Wangkkklll/GausPcc

UniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approach

https://github.com/YuvalShalev/NJEE

Neural Joint Entropy Estimation

https://github.com/agnesdeng/mixgb

XGBoost: A Scalable Tree Boosting System

https://github.com/bamler-lab/constriction

Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective

https://github.com/bayesflow-org/bayesflow

BayesFlow: Amortized Bayesian Workflows With Neural Networks

https://github.com/bw2color/bw2color

XGBoost: A Scalable Tree Boosting System

https://github.com/david-dunson/GeodesicDistance

Efficient Manifold and Subspace Approximations with Spherelets

https://github.com/facebookresearch/uco3d

Compact 3D Scene Representation via Self-Organizing Gaussian Grids

https://github.com/frederikkemarin/bend

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

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