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pojęcia
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pojęcia

Weight Sharing

Method where multiple neural connections share the same parameters to significantly reduce the number of unique weights.

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Low-Rank Factorization

Decomposition of weight matrices into products of lower-rank matrices to compress dense network layers.

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Tensor Decomposition

Advanced technique factorizing convolutional weight tensors into simpler tensors to reduce computational complexity.

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Sparse Coding

Representation of activations with many zero coefficients, enabling efficient compression and accelerated computation.

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Huffman Coding

Lossless compression algorithm assigning variable-length binary codes to weights based on their frequency of occurrence.

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Model Splitting

Division of a model into segments distributed between clients and server to minimize communication while preserving confidentiality.

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Parameter Binarization

Conversion of weights into binary values (+1/-1) to drastically reduce memory and accelerate calculations on limited devices.

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Federated Averaging

Aggregation algorithm weighting local model updates according to client dataset sizes for global convergence.

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Model Pruning Ratio

Percentage of weights or neurons removed from the original model, determining the level of compression applied.

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Quantization-aware Training

Training that incorporates the effects of quantization to minimize performance degradation after compression.

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