SeAl-KD improves SNN accuracy by equalizing competing logits at erroneous timesteps and reweighting temporal alignment using confidence and inter-timestep similarity.
Learning multiple layers of features from tiny im- ages
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4representative citing papers
Spike-NVPT creates noise-robust binary visual prompts by using integrate-and-fire spiking neurons to filter signals and discretize them, yielding up to 11.2% better robustness than standard prompt tuning while keeping clean accuracy competitive.
FedIDM filters abnormal updates in federated learning by creating condensed data through distribution matching and rejecting updates that deviate or cause high loss on that data.