利用最后一层的累积膜电位来作为输出,并且在最后一层保留了膜电位衰减性质,最终输出: Backward propagation 采用输出脉冲频率与label之间的均方误差,误差反向传播如下: 这里要对SNN中的activation function过程做一个估计来近似反向梯度,并且这样的估计对于隐藏层和最后一层是不同的。对于最后一层,把output看成是a_{LIF...
蹚入Deep Learning的人越来越多了,直接上手写Image Classification、Speech Recognition甚至搭一个完整的Machine Translation System也不再是一个难事了,但也因为嵌套的非线性结构使得Neural Network框架更像是一个黑盒子,我们该如何解释究竟是什么因素使得有这样的预测结果。 为什么要使得AI System具备可解释性呢? 如在AI...
Further, a convolutional neural network (CNN) architecture was developed and shown to deliver performance that exceeds that of expert-based approaches. Here, we follow the framework of [1] and find deep neural network architectures that deliver higher accuracy than the state of the art. We ...
Deep neural network (DNN) architectures are considered to be robust to random perturbations. Nevertheless, it was shown that they could be severely vulnera... OF Tuna,FO Catak,MT Eskil - 《Multimedia Tools & Applications》 被引量: 0发表: 2022年 Multi-objective Search of Robust Neural Archit...
After the first successes of deep learning, designing neural network architectures with desirable performance criteria for a given task (for example, high accuracy or low latency) has been a challenging problem. Some call it alchemy and some intuition, but the task of discovering a novel...
deep neural network architectures for video classification. Finally, when finding the relevant area using the extracted action template, the proposed method successfully extracts proper keyframes from human action videos for video classification using deep neural networks. Although the proposed method has ...
Deep neural networks are a type of artificial neural network with multiple hidden layers, which makes them more complex and resource-intensive compared to conventional neural networks. They are used for various applications and work best with GPU-based architectures for faster training times. ...
An up-to-date overview is provided on four deep learning architectures, namely, autoencoder, convolutional neural network, deep belief network, and restricted Boltzmann machine. Different types of deep neural networks are surveyed and recent progresses are summarized. Applications of deep learning ...
I.PHI may be the first multitask dataset of machine-actionable epigraphical text, but its size is still several orders of magnitude smaller than modern typical language datasets. To avert the risk of overfitting, which is common in large-scale deep neural network architectures, we apply several...
There are mainly three architectures of Deep Learning Convolution Neural Network, Deep Neural Network and Recurrent Neural Network which provides the higher level of representation of data at each next layer. Deep Learning is required to classify high dimensional data like images, audio, video and ...