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Neural Network Compression

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Showing 26–50 of 193 papers

TitleStatusHype
EPSD: Early Pruning with Self-Distillation for Efficient Model Compression—0
Convolutional Neural Network Compression via Dynamic Parameter Rank Pruning—0
Balanced and Deterministic Weight-sharing Helps Network Performance—0
ABKD: Graph Neural Network Compression with Attention-Based Knowledge Distillation—0
Grokking as Compression: A Nonlinear Complexity Perspective—0
Causal-DFQ: Causality Guided Data-free Network QuantizationCode0
A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and RecommendationsCode2
Quantization Aware Factorization for Deep Neural Network Compression—0
Survey on Computer Vision Techniques for Internet-of-Things Devices—0
Model Compression Methods for YOLOv5: A Review—0
Lightweight Attribute Localizing Models for Pedestrian Attribute Recognition—0
Neural Network Compression using Binarization and Few Full-Precision Weights—0
Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGDCode0
Understanding the Effect of the Long Tail on Neural Network Compression—0
End-to-End Neural Network Compression via _1_2 Regularized Latency Surrogates—0
Modular Transformers: Compressing Transformers into Modularized Layers for Flexible Efficient Inference—0
Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network CompressionCode0
Evaluation Metrics for DNNs Compression—0
How Informative is the Approximation Error from Tensor Decomposition for Neural Network Compression?—0
Guaranteed Quantization Error Computation for Neural Network Model Compression—0
SwiftTron: An Efficient Hardware Accelerator for Quantized TransformersCode1
WHC: Weighted Hybrid Criterion for Filter Pruning on Convolutional Neural NetworksCode0
DepGraph: Towards Any Structural PruningCode4
Magnitude and Similarity based Variable Rate Filter Pruning for Efficient Convolution Neural NetworksCode0
PD-Quant: Post-Training Quantization based on Prediction Difference MetricCode1
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