PyTorch implementation of the Self-Compression & Differentiable Quantization Algorithm introduced in “Self-Compressing Neural Networks” paper.
text
PyTorch implementation of the Self-Compression & Differentiable Quantization Algorithm introduced in “Self-Compressing Neural Networks” paper.
new_line
new_line
The algorithm shows dynamic neural network compression during training - with reduced size of weight, activation tensors and bits required to represent weights. 
text
The algorithm shows dynamic neural network compression during training - with reduced size of weight, activation tensors and bits required to represent weights. 
new_line
new_line
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.
text
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.
new_line
new_line
Code: 
text
Code: 
https://github.com/Jaykef/ai-algorithms
https://github.com/Jaykef/ai-algorithms
link
new_line
Paper: 
text
Paper: 
https://arxiv.org/pdf/2301.13142
https://arxiv.org/pdf/2301.13142
link
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
🔥
DmitryRyumin GPT007 osanseviero maywell HideFace
👀
John6666