content.raw: PyTorch implementation of the Self-Compression & Differentiable Quantization Algorithm introduced in “Self-Compressing Neural Networks” paper.
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content.value: PyTorch implementation of the Self-Compression & Differentiable Quantization Algorithm introduced in “Self-Compressing Neural Networks” paper.
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content.raw: The algorithm shows dynamic neural network compression during training - with reduced size of weight, activation tensors and bits required to represent weights. 
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content.value: The algorithm shows dynamic neural network compression during training - with reduced size of weight, activation tensors and bits required to represent weights. 
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content.raw: It’s basically shrinking the neural network size (weights and activations) as it’s being trained without compromising performance -  this helps reduce compute and inference cost.
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content.value: It’s basically shrinking the neural network size (weights and activations) as it’s being trained without compromising performance -  this helps reduce compute and inference cost.
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content.raw: Code: 
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content.value: Code: 
content.href: https://github.com/Jaykef/ai-algorithms
content.raw: https://github.com/Jaykef/ai-algorithms
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content.raw: Paper: 
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content.value: Paper: 
content.href: https://arxiv.org/pdf/2301.13142
content.raw: https://arxiv.org/pdf/2301.13142
content.type: link
rawContent: PyTorch implementation of the Self-Compression & Differentiable Quantization Algorithm introduced in “Self-Compressing Neural Networks” paper.
  
  The algorithm shows dynamic neural network compression during training - with reduced size of weight, activation tensors and bits required to represent weights. 
  
  It’s basically shrinking the neural network size (weights and activations) as it’s being trained without compromising performance -  this helps reduce compute and inference cost.
  
  Code: https://github.com/Jaykef/ai-algorithms
  Paper: https://arxiv.org/pdf/2301.13142
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