AI-ordlista
Den kompletta ordlistan över AI
Variable-Level Quantization
Approach using a variable number of bits to represent different parts of the model according to their importance or distribution.
Quantile Quantization
Technique dividing the weight distribution into intervals containing an equal number of values for balanced representation.
K-means Clustering Quantization
Method using the K-means algorithm to identify optimal cluster centers as quantization levels.
Non-Uniform Quantization with Bias
Approach introducing controlled bias in the intervals to favor certain regions of the weight distribution.
Logarithmic Quantization
Technique using a logarithmic scale for intervals, particularly effective for long-tail distributions.
Probability Distribution Quantization
Method adapting quantization intervals according to the probability density of weights in each region.
Hybrid Quantization
Combination of uniform and non-uniform techniques applied to different layers or parts of the neural model.
Variable-Step Quantization
Approach where the quantization step size varies according to local weight density to minimize reconstruction error.
Minimum Entropy Quantization
Optimization of quantization levels to minimize the entropy of the overall quantization error.
KL Optimization Quantization
Method minimizing the Kullback-Leibler divergence between the original and quantized weight distributions.
Post-Training Non-Uniform Quantization
Application of non-uniform quantization techniques on an already trained model without additional retraining.
Distribution-Aware Quantization
Approach that analyzes and adapts quantization based on the specific shape of the model's weight distribution.
Adaptive Histogram Quantization
Technique using an adaptive histogram to determine optimal quantization intervals according to local density.
Backpropagation Quantization
Method optimizing quantization levels during training via gradient backpropagation.
Vector Quantization Quantization
Technique grouping weights into vectors and applying non-uniform quantization at the vector level rather than scalar level.