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Low-rank compression

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Showing 1–34 of 34 papers

TitleStatusHype
LiteASR: Efficient Automatic Speech Recognition with Low-Rank ApproximationCode2
Unified Low-rank Compression Framework for Click-through Rate PredictionCode1
TT-NF: Tensor Train Neural FieldsCode1
Compressing Neural Networks: Towards Determining the Optimal Layer-wise DecompositionCode1
MLorc: Momentum Low-rank Compression for Large Language Model Adaptation—0
ReCalKV: Low-Rank KV Cache Compression via Head Reordering and Offline CalibrationCode0
Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks—0
Cognitive Memory in Large Language Models—0
Penrose Tiled Low-Rank Compression and Section-Wise Q&A Fine-Tuning: A General Framework for Domain-Specific Large Language Model Adaptation—0
Low-Rank Compression for IMC Arrays—0
Theoretical Guarantees for Low-Rank Compression of Deep Neural Networks—0
Adaptive Pruning of Pretrained Transformer via Differential Inclusions—0
CALoR: Towards Comprehensive Model Inversion DefenseCode0
LoRC: Low-Rank Compression for LLMs KV Cache with a Progressive Compression Strategy—0
Basis Selection: Low-Rank Decomposition of Pretrained Large Language Models for Target Applications—0
Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization—0
ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks—0
Efficient Wireless Federated Learning via Low-Rank Gradient FactorizationCode0
Maestro: Uncovering Low-Rank Structures via Trainable DecompositionCode0
HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks—0
Compact Model Training by Low-Rank Projection with Energy TransferCode0
DRONE: Data-aware Low-rank Compression for Large NLP Models—0
A Highly Effective Low-Rank Compression of Deep Neural Networks with Modified Beam-Search and Modified Stable Rank—0
FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization—0
Semi-tensor Product-based TensorDecomposition for Neural Network Compression—0
Model compression as constrained optimization, with application to neural nets. Part V: combining compressionsCode0
Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression—0
Data-aware Low-Rank Compression for Large NLP Models—0
Low-Rank Compression of Neural Nets: Learning the Rank of Each LayerCode0
A flexible, extensible software framework for model compression based on the LC algorithmCode0
Decomposable-Net: Scalable Low-Rank Compression for Neural NetworksCode0
AntMan: Sparse Low-Rank Compression to Accelerate RNN inference—0
Approximate FPGA-based LSTMs under Computation Time Constraints—0
Domain-adaptive deep network compressionCode0
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