However, only an upper limit has been provided for TTFS-coded SNNs, and the information-processing capability of SNNs at lower firing frequencies has not been fully investigated. In this paper, we propose two s
Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks PDF: https://arxiv.org/pdf/2201.03299.pdf PyTorch代码: https:///shanglianlm0525/CvPytorch PyTorch代码: https:///shanglianlm0525/PyTorch-Networks Regularizat...
Large and Deep Convolutional Neural Networks achieve good results in image classification tasks, but they need methods to prevent overfitting. In this paper we compare performance of different regularization techniques on ImageNet Large Scale Visual Recognition Challenge 2013. We show empirically that Drop...
Multilayer feedforward neural networks (FNNs) has been widely used in various fields [1], [2]. The training of FNNs can be reduced to solving nonlinear least square problems, to which numerous traditional numerical methods, such as the gradient descent method, Newton method [3], conjugate gr...
6.2 Comparison with the State-of-the-Art Methods(using the Standard Split) 并将GCN+P-reg和GAT+P-reg与现有方法进行了比较。其中,APPNP、GMNN和Graph U-Nets是新近提出的最先进的GNN模型,GraphAT、BVAT和GraphMix使用各种复杂的技术来提高GNN的性能。GraphAT和BVAT将对抗性扰动纳入输入数据。GraphMix采用联合...
Currently, Dropout (and related methods such as DropConnect) are the most effective means of regularizing large neural networks. These amount to efficiently visiting a large number of related models at training time, while aggregating them to a single predictor at test time. The proposed FaMe ...
For GMRF regularization, the quadratic l2 norm is employed for ϕ(⋅). These regularized methods smooth the restored image by penalizing the high-frequency component, and thus perform well in suppressing noise. However, they inevitably oversmooth the sharp edges and detailed information. 3.2.3.2...
First- and second-order methods for learning: Between steepest descent and Newton's method Neural Computation, 4 (2) (1992), pp. 144-166 Google Scholar [4] T.W.S. Chow, C.-T. Leung Neural network based short-term load forecasting using weather compensation IEEE Trans. Power Syst., 11...
log-likelihood on unseen samples, which provides well-calibrated predictive uncertainty. Our findings present a new direction to improve the predictive probability quality of deterministic neural networks, which can be an efficient and scalable alternative to Bayesian neural networks and ensemble methods. ...
Deep neural networks often work well when they are over-parameterized and trained with a massive amount of noise and regularization, such as weight decay and dropout. Although dropout is widely used as a regularization technique for fully connected layers, it is often less effective for convolutiona...