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Neural Network Compression

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Showing 101–150 of 193 papers

TitleStatusHype
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
To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression—0
2PFPCE: Two-Phase Filter Pruning Based on Conditional Entropy—0
A Bayesian Optimization Framework for Neural Network Compression—0
ABKD: Graph Neural Network Compression with Attention-Based Knowledge Distillation—0
AcceRL: Policy Acceleration Framework for Deep Reinforcement Learning—0
A Comparative Study of Neural Network Compression—0
A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification—0
Adversarial Network Compression—0
An Efficient Real-Time Object Detection Framework on Resource-Constricted Hardware Devices via Software and Hardware Co-design—0
A novel channel pruning method for deep neural network compression—0
A Novel Structure-Agnostic Multi-Objective Approach for Weight-Sharing Compression in Deep Neural Networks—0
An Overview of Neural Network Compression—0
Approximate Bisimulation Relations for Neural Networks and Application to Assured Neural Network Compression—0
A Survey on Deep Neural Network Compression: Challenges, Overview, and Solutions—0
A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation—0
Automated Pruning for Deep Neural Network Compression—0
Automatic Parameter Tying in Neural Networks—0
Balanced and Deterministic Weight-sharing Helps Network Performance—0
Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data—0
Build a Compact Binary Neural Network through Bit-level Sensitivity and Data Pruning—0
Cascaded Projection: End-to-End Network Compression and Acceleration—0
Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks—0
Compression of Site-Specific Deep Neural Networks for Massive MIMO Precoding—0
Condensed Sample-Guided Model Inversion for Knowledge Distillation—0
Constraint-Aware Deep Neural Network Compression—0
Convolutional Neural Network Compression through Generalized Kronecker Product Decomposition—0
Convolutional Neural Network Compression Based on Low-Rank Decomposition—0
Convolutional neural network compression for natural language processing—0
Convolutional Neural Network Compression via Dynamic Parameter Rank Pruning—0
Convolutional neural networks compression with low rank and sparse tensor decompositions—0
Coreset-Based Neural Network Compression—0
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
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