# Post-synaptic potential regularization has potential

arXiv: https://arxiv.org/abs/1907.08544 · HTML: https://mlsanity.com/paper/1907.08544/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [SeReNe: Sensitivity based Regularization of Neurons for Structured Sparsity in Neural Networks](https://mlsanity.com/paper/2102.03773/) (2021)

## Abstract (arXiv)

Improving generalization is one of the main challenges for training deep neural networks on classification tasks. In particular, a number of techniques have been proposed, aiming to boost the performance on unseen data: from standard data augmentation techniques to the ℓ2 regularization, dropout, batch normalization, entropy-driven SGD and many more. In this work we propose an elegant, simple and principled approach: post-synaptic potential regularization (PSP). We tested this regularization on a number of different state-of-the-art scenarios. Empirical results show that PSP achieves a classification error comparable to more sophisticated learning strategies in the MNIST scenario, while improves the generalization compared to ℓ2 regularization in deep architectures trained on CIFAR-10.