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Neural Network Compression

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Showing 101–150 of 193 papers

TitleStatusHype
Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization—0
SCANN: Synthesis of Compact and Accurate Neural Networks—0
Semi-tensor Product-based TensorDecomposition for Neural Network Compression—0
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations—0
Sparse matrix products for neural network compression—0
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
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
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
Hybrid Tensor Decomposition in Neural Network Compression—0
Is Quantum Optimization Ready? An Effort Towards Neural Network Compression using Adiabatic Quantum Computing—0
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration—0
Lightweight Attribute Localizing Models for Pedestrian Attribute Recognition—0
Linearity-based neural network compression—0
Compressing 3DCNNs Based on Tensor Train Decomposition—0
Low-Rank Matrix Approximation for Neural Network Compression—0
Minimally Invasive Surgery for Sparse Neural Networks in Contrastive Manner—0
MINT: Deep Network Compression via Mutual Information-based Neuron Trimming—0
Partial Binarization of Neural Networks for Budget-Aware Efficient Learning—0
MLPrune: Multi-Layer Pruning for Automated Neural Network Compression—0
Exact Backpropagation in Binary Weighted Networks with Group Weight TransformationsCode0
WHC: Weighted Hybrid Criterion for Filter Pruning on Convolutional Neural NetworksCode0
COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level PruningCode0
MUSCO: Multi-Stage Compression of neural networksCode0
A Programmable Approach to Neural Network CompressionCode0
Parallel Blockwise Knowledge Distillation for Deep Neural Network CompressionCode0
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