Artificial neural networks are notoriously power- and time-consuming when implemented on conventional von Neumann computing systems. Consequently, recent years have seen an emergence of research in machine learning hardware that strives to bring memory and computing closer together. A popular approach is to realise artificial neural networks in hardware by implementing their synaptic weights using memristive devices. However, various device- and system-level non-idealities usually prevent these physical imp

pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

Neural Hidden State From GM-RKB A Neural Hidden State is a output function of a hidden neuron . AKA: Hidden State , Hidden Neuron State Function . Context: It can be defined as [math]\displaystyle{ h =g(W, x, \Theta) }[/math], where [math]\displaystyle{ g }[/math] is a neuron activation function , [math]\displaystyle{ W }[/math] is a weight matrix , [math]\displaystyle{ x_i }[/math] are neural inputs , [math]\displaystyle{ \Theta }[/math] represents other state functions or variables. Example(s): a simple F

In short, a Gated Neural Network (GNN) allows for the layers of the network to learn in increments, rather than creating transformations from scratch. The gate in the neural network is used to decide whether the network can use the shortened identity connections, or if it will need to use the stacked layers

Neural components that weigh which parts of the input matter most for the current prediction

Forecasting with multivariate time series, which aims to predict future values given previous and current several univariate time series data, has been studied for decades, with one example being ARIMA. Because it is difficult to measure the extent to which noise is mixed with informative signals within rapidly fluctuating financial time series data, designing a good predictive model is not a simple task. Recently, many researchers have become interested in recurrent neural networks and attention-based neur

Lab note Companion post to the Activation Functions carousel. Previously: Softmax: The Probability Engine. The previous Softmax post ended with a strange contrast. Softmax was beautifully smooth, but its Jacobian was dense, global, and a li...

Long short-term memory (LSTM) networks are a specialized type of recurrent neural network (RNN) designed to handle seque

This paper explores Generative Teaching Networks (GTN), which are similar to GANs but instead of compete, two networks cooperate on a task. With applications in multiple domains, GTNs can aid supervised learning training times, learn Reinforcement Learning tasks like cart-pole, and perform Neural Architecture Search (NAS

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