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Beyond Discreteness: Sample Complexity Analysis of Straight-Through Estimator for 1-bit Quantization
arXiv:2505.18113v2 Announce Type: replace Abstract: Training quantized neural networks requires addressing the non-differentiable and discrete nature of the underlying optimization problem. To tackle this challenge, the straight-through estimator (STE) has become the most widely adopted heuristic, allowing backpropagation through discrete operations by introducing biased yet valid surrogate gradients. However, its theoretical properties remain largely unexplored, with few existing analyses...
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DOT-MoE: Differentiable Optimal Transport for MoEfication
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