Neural networks in the brain are dominated by sometimes more than 60% feedback
connections, which most often have small synaptic weights. Different from this, little is known …

Neural networks are known to be effective function approximators. Recently, deep neural
networks have proven to be very effective in pattern recognition, classification tasks and …

Deep learning using multi-layer neural networks (NNs) architecture manifests superb power
in modern machine learning systems. The trained Deep Neural Networks (DNNs) are …

In many real-world deployments of machine learning systems, data arrive piecemeal. These
learning scenarios may be passive, where data arrive incrementally due to structural …

Abstract Recently proposed Gated Linear Networks (GLNs) present a tractable nonlinear
network architecture, and exhibit interesting capabilities such as learning with local error …

An overview of neural network architectures is presented. Some of these architectures have
been created in recent years, whereas others originate from many decades ago. Apart from …

Recent advances in deep learning motivate the use of deep neural networks in Internet-of-
Things (IoT) applications. These networks are modelled after signal processing in the …

Despite the widespread practical success of deep learning methods, our theoretical
understanding of the dynamics of learning in deep neural networks remains quite sparse …

Abstract We introduce Dynamic Deep Neural Networks (D2NN), a new type of feed-forward
deep neural network that allows selective execution. Given an input, only a subset of D2NN …

Continuous-time neural networks are a class of machine learning systems that can tackle
representation learning on spatiotemporal decision-making tasks. These models are …