Abstract page for arXiv paper 2303.07310v1: Learning Reduced-Order Models for Cardiovascular Simulations with Graph Neural Networks

Toggle navigation White Rose eTheses Online Approximate Solutions to Abstract Argumentation Problems Using Graph Neural Networks --> Malmqvist, Lars (2022) Approximate Solutions to Abstract Argumentation Problems Using Graph Neural Networks. PhD thesis, University of York. Abstract This thesis explores a new approach to approximating decision problems in abstract argumentation using Graph Convolutional Networks (GCN). It demonstrates that such an approach can reach well-balanced accuracy levels above 90

Explore neural text to speech technology and its applications. Neural TTS allows AI to create realistic voices numerous use cases across business

Graph Neural Networks (GNNs) have emerged as a powerful tool to learn from graph-structured data. A paramount example of such data is the brain, which operates as a network, from the micro-scale of neurons, to the macro-scale of regions. This organization deemed GNNs a natural tool of choice to model brain activity, and have consequently attracted a lot of attention in the neuroimaging community. Yet, the advantage of adopting these models over conventional methods has not yet been assessed in a systematic

# Feedforward step of a basic feedforward neural network ¶ ## What is feedforward? ¶ The feedforward step for a neural network is when we pick a sample \(D_i\) from the dataset \(D\), and feed it into the network. The network’s weights in each layer tranform the sample from its initial representation into various other representations, which are fed into layer after layer. The output of the final hidden layer, \(Z_{L-1}\), is then transformed by the output layer to create the network output \(O\). ## D

Hironobu SUZUKI @ InterDB > Part 1: Neural Networks > 2. Neural Network 2. Neural Network This chapter introduces neural networks by presenting a simplified network designed to solve the XOR problem. Chapter Contents 2.1. Formulation of Neural Networks 2.2. Overview of the Neural Network Training 2.3. Computing gradients for Back Propagation 2.4. Implementing XOR-gate from Scratch Further details and advanced concepts will be covered in the subsequent chapters. The Engineer's Guide To Deep Learning Search H

Literal Labs co-founder Alex Yakovlev and CTO Leon Fedden join ipXchange host Elliott Lee-Hearn to discuss Tsetlin Machines.

Sampling Strategies for Recurrent Neural Networks Recurrent Neural Networks are currently one of the most powerful Machine Learning models. They are the method behind many advances in speech

When designing the architecture of neural networks, we need to decide the number of nodes to include in the output layer and choose the appropriate activation function. For regression tasks, the linear activation function is the most suitable option. On the other hand, for classification tasks, we have a choice between softmax and sigmoid activation functions

Differentiable logic programming in Python for elegant encoding and extending of GNNs towards more complex deep relational models