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Low-rank compression
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
ReCalKV: Low-Rank KV Cache Compression via Head Reordering and Offline Calibration
Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks
Cognitive Memory in Large Language Models
Penrose Tiled Low-Rank Compression and Section-Wise Q&A Fine-Tuning: A General Framework for Domain-Specific Large Language Model Adaptation
LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation
Low-Rank Compression for IMC Arrays
Theoretical Guarantees for Low-Rank Compression of Deep Neural Networks
Adaptive Pruning of Pretrained Transformer via Differential Inclusions
CALoR: Towards Comprehensive Model Inversion Defense
LoRC: Low-Rank Compression for LLMs KV Cache with a Progressive Compression Strategy
Unified Low-rank Compression Framework for Click-through Rate Prediction
Basis Selection: Low-Rank Decomposition of Pretrained Large Language Models for Target Applications
Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization
ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks
Efficient Wireless Federated Learning via Low-Rank Gradient Factorization
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks
TT-NF: Tensor Train Neural Fields
Compact Model Training by Low-Rank Projection with Energy Transfer
A Highly Effective Low-Rank Compression of Deep Neural Networks with Modified Beam-Search and Modified Stable Rank
FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization
Semi-tensor Product-based TensorDecomposition for Neural Network Compression
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition
Model compression as constrained optimization, with application to neural nets. Part V: combining compressions
Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression
A flexible, extensible software framework for model compression based on the LC algorithm
Decomposable-Net: Scalable Low-Rank Compression for Neural Networks
AntMan: Sparse Low-Rank Compression to Accelerate RNN inference
Approximate FPGA-based LSTMs under Computation Time Constraints
Domain-adaptive deep network compression
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.