https://github.com/UCMerced-ML/LC-model-compression
공식 코드A flexible, extensible software framework for model compression based on the LC algorithm
Papers with Code 아카이브
Low-rank compression
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/bzqlin/lrpet
공식 코드Compact Model Training by Low-Rank Projection with Energy Transfer
https://github.com/chrisqcwx/calor
공식 코드https://github.com/efeslab/liteasr
공식 코드LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation
https://github.com/lucaslie/torchprune
공식 코드Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition
https://github.com/mingzhaoguo/ota-lc
공식 코드Efficient Wireless Federated Learning via Low-Rank Gradient Factorization
https://github.com/mmasana/DALR
공식 코드https://github.com/samuelhorvath/maestro-lod
공식 코드Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
https://github.com/toshas/ttnf
공식 코드https://github.com/xianglongyan/recalkv
공식 코드ReCalKV: Low-Rank KV Cache Compression via Head Reordering and Offline Calibration
https://github.com/ygcats/Scalable-Low-Rank-Compression-for-Neural-Networks
공식 코드Decomposable-Net: Scalable Low-Rank Compression for Neural Networks
https://github.com/yuhao318/atomic_feature_mimicking
공식 코드Unified Low-rank Compression Framework for Click-through Rate Prediction
https://github.com/The0nix/DALR
https://github.com/lucaslie/provable_pruning
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.