This chapter introduces the basic concepts and notation of unsupervised learning neural networks. Unsupervised networks are useful for analyzing data without having the desired outputs; in this case, the neural networks evolve to capture density characteristics of a data phase. We will describe in ...
1 Unsupervised learning of digit recognition using spike-timing-dependent plasticity 添加奖励机制 2 Combining stdp and reward-modulated stdp in deep convolutional spiking neural networks for digit recognition 把预训练好的 ANN 转化为 SNN 3 Spikingdeep convolutional neural networksfor energy-efficient object...
1) unsupervised learning neural networks 非监督学习神经网络2) Unsupervised learning ANN 无监督学习神经网络3) unsupervised neural network 无监督神经网络 1. The principle and algorithm of a unsupervised neural network─SOFM is be discussed,the unsupervised network is applied to pattern recognition of ...
[13] T. Hoefler, D. Alistarh, T. Ben-Nun, N. Dryden, and A. Peste, “Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks,” The Journal of Machine Learning Research, vol. 22, no. 1, pp. 10 882–11 005, 2021. [14] J. Hoffmann...
Keywords: unsupervised learning, reinforcement learning, spiking neural network, neural plasticity, clustered connectivity 1. INTRODUCTION 神经系统从过去的经验中学习,根据处理要求逐渐调整其属性。无论是在纯粹的感官驱动情况下还是在高级决策过程中,学习的一个关键组成部分是开发足够且可用的内部表征,使系统能够评估...
Supervised learning 监督学习 是有特征(feature)和标签(label)的,即便是没有标签的,机器也是可以通过特征和标签之间的关系,判断出标签。 Unsupervised learning 无监督学习 只有特征,没有标签。 Semi-Supervised learning 半监督学习 使用的数据,一部分是标记过的,而大部分是没有标记的。和监督学习相比较,半监督学习的...
unsupervised learning会对model起一定的限定作用,即相当于一个regularizer,这个regularizer使得encoder阶段提取得到的特征具有可解释性 四、Main contributions 本文实验表明了,high-capacity neural networks(采用了known switches)的 intermediate activations 可以保存input的大量信息,除了部分 ...
The “learning” capabilities of neural networks are by far their most fascinating property. The processing may be simulated in a computer program, but because of the sequential nature of conventional computer software, the parallel feature of the neural network will be lost and computation time ...
Strategies to associate memories by unsupervised learning in neural networks In this work we study the effects of three different strategies to associate memories in a neural network composed by both excitatory and inhibitory spiking neurons, which are randomly connected through recurrent excitatory and ...
Unsupervised Learning in LSTM Recurrent Neural Networks While much work has been done on unsupervised learning in feedforward neural network architectures, its potential with (theoretically more powerful) recurrent networks and time-varying inputs has rarely been explored. Here we train Long ... NN ...