arXiv:2607. 25529v1 Announce Type: new Abstract: As neural network models for image classification advance, neurons play critical roles in pruning, backdoor defense, and interpretability.
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The paper investigates how single‑hidden‑layer MLPs can fit training data yet fail to recover the underlying rule, focusing on higher‑order interactions and nuisance inputs. Using synthetic parity tasks, the authors benchmark different optimizers (SGD, Adam, Muon) and show that while all achieve perfect accuracy on second‑order interactions, performance drops sharply for higher orders, with Muon outperforming the others at fourth order. Experiments also reveal that freezing or removing nuisance‑related weights dramatically alters training outcomes, highlighting the role of nuisance learning in shaping the rules a shallow network can represent.
arXiv:2607. 11914v1 Announce Type: cross Abstract: A central goal of current Spiking Neural Network (SNN) research is to improve their accuracy toward becoming low-power alternatives to Artificial Neural Networks (ANNs).
arXiv:2609.26167v1 Announce Type: new Abstract: Activation-energy pruning -- removing weights whose product of magnitude and cumulative pre-synaptic spike count falls below a threshold -- was establi...
arXiv:2609.23561v1 Announce Type: new Abstract: Deploying efficient neural networks is essential in resource-constrained environments, yet compact models often sacrifice interpretability - a critical...
arXiv:2602.01718v2 Announce Type: replace Abstract: Predicting generalization from quantities available before target-test evaluation remains a central challenge in deep learning. The systematic benc...
arXiv:2606. 05740v1 Announce Type: new Abstract: Deep neural networks trained under severe class imbalance often exhibit degraded performance, typically attributed to statistical bias.