Self-normalizing neural networks (SNN)2022 Elsevier LtdThe need for robust, secure and private machine learning is an important goal for realizing the full potential of the Internet of Things (IoT). Federated Learning has proven to help protect against privacy violations and information leakage. ...
The activation function of SNNs are "scaled exponential linear units" (SELUs), which induce self-normalizing properties. Using the Banach fixed-point theorem, we prove that activations close to zero mean and unit variance that are propagated through many network layers will converge towards zero ...
In this article we propose a hybrid architecture for end-to-end training of deep neural networks for event-based pattern recognition and object detection, combining a spiking neural network (SNN) backbone for efficient event-based feature extraction, and a subsequent analog neural network (ANN) ...
To address this issue, this paper implements a self-normalizing neural network (SNN) in order to extract high-level abstract representations without losing information due to the data initialization. The selected activation function (scaled exponential linear units or SELU) normalizes the data ...
Figure 4. The training architecture of AutoEmbedder using a Siamese neural network (SNN). The subnetwork of SNN is weight-sharable, and the activation function is Relu, which is described in Equation (4). The architecture calculates pairwise distance output based on the generated embeddings pair...
and standing, the 2-bit quantized model yields the highest accuracy, which could be due to the regularization effect of quantization. In the case of SNN, we notice a more visible correlation between the compression level and the inference accuracy (as we slim the network, the accuracy drops)...