Neural Network Compression
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Benchmarks
CIFAR-10
Most implemented
NeRV: Neural Representations for Videos
ZeroQ: A Novel Zero Shot Quantization Framework
Learning Filter Basis for Convolutional Neural Network Compression
Data-Free Learning of Student Networks
MUSCO: Multi-Stage Compression of neural networks
Papers
M-Fibration Theory with Applications to Neural Network Compression
The purpose of this paper is to provide a general, comprehensive, theoretical framework that allows one to deal with fibrations on graphs labelled on a commutative monoid. This is a genuine extension of the theory of gra…
Neural Network CompressionOn the Applicability of Safety Nets: A Safety-By-Design Solution for Certifying Neural Networks
The integration of Artificial Intelligence (AI) in safety-critical aviation systems presents significant challenges for certification and deployment. Aviation, often regarded as the safest form of transportation, relies …
Neural Network CompressionEvoLP: Self-Evolving Latency Predictor for Model Compression in Real-Time Edge Systems
Edge devices are increasingly utilized for deploying deep learning applications on embedded systems. The real-time nature of many applications and the limited resources of edge devices necessitate latency-targeted neural…
Neural Network CompressionModel CompressionHierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization
We present HiReLC, a hierarchical ensemble-reinforcement learning framework for automated joint quantization and structured pruning of deep neural networks. The framework decomposes the compression search across two leve…
Hierarchical Reinforcement LearningNeural Network CompressionActive LearningHybrid Compression: Integrating Pruning and Quantization for Optimized Neural Networks
Deep neural networks have witnessed remarkable advancements in recent years and have become integral to various applications. However, alongside these developments, training and deployment of neural network models on emb…
Neural Network CompressionModel CompressionNeural Network Compression by Approximate Differential Equivalence
Neural network compression is commonly achieved by pruning parameters based on local importance scores, e.g., magnitude-based pruning. We propose a complementary approach that compresses models by aggregating neurons wit…
Neural Network Compression