This chapter discusses artificial neural networks. We will present a mathematical model of a single neuron, various structures of artificial neural networks and their learning algorithms.doi:10.1007/978-3-540-76288-1_6Leszek RutkowskiSpringer Berlin HeidelbergRutkowski, L., Neural networks and their ...
Learning algorithms articles within Nature Communications Featured Article 02 May 2025 | Open Access Behavior engineering using quantitative reinforcement learning models Previous work has attempted to influence people’s decision-making processes based on qualitative psychological principles. Here, in a ...
meta-learning the learning algorithms themselves generating effective learning environments 当以上三个都能实现,我们就有希望构造极强的AGI了。环境,网络结构,算法都是学的。这个Meta Learning就是个上帝了。当然,现实情况是我们目前并不具备这样的计算资源及算法来实现,所以才会有Meta Learning的个个细分领域,我们...
limitations of training vast quantities of data huge compute resources Related work improving data efficiency knowledge transfer unsupervised learning Key insights learning-to-learn: replacing prior hand-designed learners with learned learning algorithms Key contribution Overview of the meta-learning landscape ...
3 Relationship and comparison to other reinforcement learning algorithms for spiking neural networks 可以看出,这里提出的算法与其他两种现有的脉冲强化学习算法具有共同的分析背景(Seung, 2003; Xie and Seung, 2004)。 Seung (Seung, 2003)通过考虑突触是智能体而不是我们所做的神经元来应用OLPOMDP。智能体的动作...
Neural networks in machine learningrefer to a set of algorithms designed to help machines recognize patterns without being explicitly programmed. They consist of a group of interconnected nodes. These nodes represent the neurons of the biological brain. ...
#Deep Neural Networks with GPU support This is a Java implementation of some of the algorithms for training deep neural networks. GPU support is provided via the OpenCL and Aparapi. The architecture is designed with modularity, extensibility and pluggability in mind. ###Git structure I'm using...
In principle, sequential, parallel and meta learning can be arbitrarily combined. Thus we can get algorithm networks with different topologies. A well known approach to combine a multitude of simplebase algorithms(threshold functions) into a complex structure areartificial neural networks(Chapter 11)....
Learning algorithms sound terrific. But how can we devise such algorithms for a neural network? Suppose we have a network of perceptrons that we'd like to use to learn to solve some problem. For example, the inputs to the network might be the raw pixel data from a scanned, handwritten ...
On the other hand, neural networks and boosting algorithms in neural decoding context had not shown its feasibility except for a few studies in the invasive decoding43,44. More specifically, Sussillo et. al. used a variant of RNN called Multiplicative RNN with big data collected throughout ...