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YZ Sözlüğü

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162
kategoriler
2.032
alt kategoriler
23.060
terimler
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terimler

Structurally Constrained Quantization

Neural compression technique that applies specific quantization constraints while preserving the architecture and structural relationships of the neural network.

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Quantization Aware Training (QAT)

Training method that simulates the effects of quantization during the learning process to minimize post-quantization accuracy loss.

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Post-Training Quantization (PTQ)

Quantization process applied after the complete training of the model without requiring additional retraining.

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Quantization Granularity

Level of detail at which parameters are grouped for quantization, ranging from tensor level to channel or group level.

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Quantization Bias

Systematic error introduced when converting floating-point numbers to low-precision representation.

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Quantization Calibration

Process of determining optimal quantization parameters (scale and zero-point) by analyzing the distribution of activations.

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Weight Clustering

Technique grouping similar weights into clusters sharing a common representative value to reduce complexity.

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Quantized Precision

Number of bits used to represent each quantized value, typically 8, 4, 2 or 1 bit in extreme methods.

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Dynamic Range

Range of values that the quantized format can represent, crucial for preserving important information from the model.

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Low-Precision Quantization

Extreme compression using less than 8 bits per parameter, requiring advanced techniques to maintain performance.

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Weight Reconstruction

Optimization process aiming to minimize the error between original weights and their reconstructed quantized versions.

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Quantization Structure Optimization

Algorithms automatically determining the optimal quantization configuration for each part of the network.

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Quantization Sensitivity

Measure of the impact of quantization on the performance of a specific layer, guiding heterogeneous quantization strategies.

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Quantization with Topology Constraints

Approach preserving the topological properties of the network while applying specific quantization constraints.

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