model quantization
Model quantization is a technique in deep learning that involves reducing the precision (number of bits) used to represent the weights and activations of a neural network model. This process is beneficial for both training and deployment as it reduces memory storage requirements and computational complexity. By quantizing the model, we aim to strike a balance between model size and accuracy, enabling more efficient inference on resource-constrained devices without significant degradation in performance.
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Related Concepts (20)
- activation quantization
- deep learning
- efficient inference with quantized models
- floating-point to fixed-point conversion
- integer quantization
- low-precision models
- machine learning
- model compression
- model optimization
- neural networks
- pruning and quantization
- quantization error
- quantization methods
- quantization-aware fine-tuning
- quantization-aware inference
- quantization-aware training
- quantization-aware training methods
- quantized neural networks
- trade-offs in model quantization
- weight quantization
Similar Concepts
- canonical quantization
- deformation quantization
- mode quantization
- quantization noise
- quantum algorithms
- quantum data compression
- quantum imaging
- quantum measurement
- quantum measurement process
- quantum measurement theory
- quantum measurements
- quantum metrology
- quantum optimization
- quantum theory of measurement
- signal quantization