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.
citing papers explorer
-
Not All Timesteps Matter Equally: Selective Alignment Knowledge Distillation for Spiking Neural Networks
SeAl-KD improves SNN accuracy by equalizing competing logits at erroneous timesteps and reweighting temporal alignment using confidence and inter-timestep similarity.
-
Spike-NVPT: Learning Robust Visual Prompts via Bio-Inspired Temporal Filtering and Discretization
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: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching
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.
- Reducing Bias and Variance: Generative Semantic Guidance and Bi-Layer Ensemble for Image Clustering