3 papers
cs.LG2026
Layerwise Progressive Freezing: A Training Scaffold for Depth-Scalable Binary Networks
Evan Gibson Smith, Bashima Islam
Training binary neural networks (BNNs) from scratch is dominated by the straight-through estimator (STE), whose forward/backward mismatch produces severe accuracy degradation as ne…
cs.LG2026
Quantization of Spiking Neural Networks Beyond Accuracy
Evan Gibson Smith, Jacob Whitehill, Fatemeh Ganji
Quantization is a natural complement to the sparse, event-driven computation of Spiking Neural Networks, reducing memory bandwidth and arithmetic cost for deployment on resource-co…
cs.LG2026
Layerwise Progressive Freezing Enables STE-Free Training of Deep Binary Neural Networks
Evan Gibson Smith, Bashima Islam
We investigate progressive freezing as an alternative to straight-through estimators (STE) for training binary networks from scratch. Under controlled training conditions, we find…