2 papers
cs.LG2026
Beyond Discreteness: Sample Complexity Analysis of Straight-Through Estimator for 1-bit Quantization
Halyun Jeong, Jack Xin, Penghang Yin
Training quantized neural networks requires addressing the non-differentiable and discrete nature of the underlying optimization problem. To tackle this challenge, the straight-thr…
cs.LG2024
COMQ: A Backpropagation-Free Algorithm for Post-Training Quantization
Aozhong Zhang, Zi Yang, Naigang Wang +4
Post-training quantization (PTQ) has emerged as a practical approach to compress large neural networks, making them highly efficient for deployment. However, effectively reducing t…