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Neural Network Compression

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Showing 51–100 of 193 papers

TitleStatusHype
SPC-NeRF: Spatial Predictive Compression for Voxel Based Radiance Field—0
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
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
End-to-End Neural Network Compression via _1_2 Regularized Latency Surrogates—0
Understanding the Effect of the Long Tail on Neural Network Compression—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
WHC: Weighted Hybrid Criterion for Filter Pruning on Convolutional Neural NetworksCode0
Magnitude and Similarity based Variable Rate Filter Pruning for Efficient Convolution Neural NetworksCode0
AcceRL: Policy Acceleration Framework for Deep Reinforcement Learning—0
Partial Binarization of Neural Networks for Budget-Aware Efficient Learning—0
Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction—0
spred: Solving L_1 Penalty with SGDCode0
Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks—0
Quantized Sparse Weight Decomposition for Neural Network Compression—0
Quantum Neural Network Compression—0
A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation—0
Neural Network Compression via Effective Filter Analysis and Hierarchical Pruning—0
A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification—0
Revisiting Random Channel Pruning for Neural Network CompressionCode0
PPCD-GAN: Progressive Pruning and Class-Aware Distillation for Large-Scale Conditional GANs Compression—0
Approximate Bisimulation Relations for Neural Networks and Application to Assured Neural Network Compression—0
Neural Network Compression of ACAS Xu Early Prototype is Unsafe: Closed-Loop Verification through Quantized State BackreachabilityCode0
Toward Compact Parameter Representations for Architecture-Agnostic Neural Network Compression—0
VQN: Variable Quantization Noise for Neural Network Compression—0
Adaptive Distillation: Aggregating Knowledge from Multiple Paths for Efficient DistillationCode0
Semi-tensor Product-based TensorDecomposition for Neural Network Compression—0
Convolutional Neural Network Compression through Generalized Kronecker Product Decomposition—0
Tropical Geometrical Zonotope Reduction as Applied to Neural Network Compression.—0
Quantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study—0
DKM: Differentiable K-Means Clustering Layer for Neural Network Compression—0
Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data—0
Data-Driven Low-Rank Neural Network Compression—0
HEMP: High-order Entropy Minimization for neural network comPression—0
Joint Matrix Decomposition for Deep Convolutional Neural Networks CompressionCode0
Exact Backpropagation in Binary Weighted Networks with Group Weight TransformationsCode0
Minimally Invasive Surgery for Sparse Neural Networks in Contrastive Manner—0
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