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Neural Network Compression

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Showing 151–175 of 193 papers

TitleStatusHype
Cramnet: Layer-wise Deep Neural Network Compression with Knowledge Transfer from a Teacher Network—0
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds—0
Data-Driven Low-Rank Neural Network Compression—0
Data-Free Knowledge Distillation with Soft Targeted Transfer Set Synthesis—0
Deep Neural Network Compression for Aircraft Collision Avoidance Systems—0
Differentiable Joint Pruning and Quantization for Hardware Efficiency—0
Distilling Critical Paths in Convolutional Neural Networks—0
Distilling Pixel-Wise Feature Similarities for Semantic Segmentation—0
DKM: Differentiable K-Means Clustering Layer for Neural Network Compression—0
DP-Net: Dynamic Programming Guided Deep Neural Network Compression—0
Adaptive Error-Bounded Hierarchical Matrices for Efficient Neural Network Compression—0
Efficient and Robust Knowledge Distillation from A Stronger Teacher Based on Correlation Matching—0
Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression—0
Efficient Neural Network Compression via Transfer Learning for Industrial Optical Inspection—0
End-to-End Neural Network Compression via _1_2 Regularized Latency Surrogates—0
Entropy-Constrained Training of Deep Neural Networks—0
EPSD: Early Pruning with Self-Distillation for Efficient Model Compression—0
Evaluation Metrics for DNNs Compression—0
Generalized Ternary Connect: End-to-End Learning and Compression of Multiplication-Free Deep Neural Networks—0
GranQ: Granular Zero-Shot Quantization with Channel-Wise Activation Scaling in QAT—0
Grokking as Compression: A Nonlinear Complexity Perspective—0
Guaranteed Quantization Error Computation for Neural Network Model Compression—0
Hardware-Guided Symbiotic Training for Compact, Accurate, yet Execution-Efficient LSTM—0
HEMP: High-order Entropy Minimization for neural network comPression—0
How Informative is the Approximation Error from Tensor Decomposition for Neural Network Compression?—0
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