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Neural Network Compression

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Showing 101–125 of 193 papers

TitleStatusHype
SPC-NeRF: Spatial Predictive Compression for Voxel Based Radiance Field—0
Stabilizing Quantization-Aware Training by Implicit-Regularization on Hessian Matrix—0
Survey on Computer Vision Techniques for Internet-of-Things Devices—0
Taxonomy and Evaluation of Structured Compression of Convolutional Neural Networks—0
The Impact of Quantization and Pruning on Deep Reinforcement Learning Models—0
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression—0
Tiled Bit Networks: Sub-Bit Neural Network Compression Through Reuse of Learnable Binary Vectors—0
TOCO: A Framework for Compressing Neural Network Models Based on Tolerance Analysis—0
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression—0
torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation—0
Toward Compact Parameter Representations for Architecture-Agnostic Neural Network Compression—0
Towards Explaining Deep Neural Network Compression Through a Probabilistic Latent Space—0
Transform Quantization for CNN (Convolutional Neural Network) Compression—0
Tropical Geometrical Zonotope Reduction as Applied to Neural Network Compression.—0
TropNNC: Structured Neural Network Compression Using Tropical Geometry—0
UMEC: Unified model and embedding compression for efficient recommendation systems—0
Understanding the Effect of the Long Tail on Neural Network Compression—0
Unified Framework for Neural Network Compression via Decomposition and Optimal Rank Selection—0
Universal Deep Neural Network Compression—0
VQN: Variable Quantization Noise for Neural Network Compression—0
Weight Normalization based Quantization for Deep Neural Network Compression—0
What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias—0
XNOR-Net++: Improved Binary Neural Networks—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
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