--> Login Home Artificial neural networks for intelligent cost estimation – a contribution to strategic cost management in the manufacturing supply chain Bodendorf F, Merkl P, Franke J (2021) Publication Type: Journal article, Original article Publication year: 2021 Journal International Journal of Production Research Taylor & Francis Original Authors: Frank Bodendorf, Philipp Merkl, Jörg Franke Pages Range: 1-22 DOI: 10.1080/00207543.2021.1998697 Abstract In today’s complex supply networks sharing
↓ Skip to main content Altmetric What is this page? Embed badge Share Artificial Neural Networks and Machine Learning – ICANN 2025 Overview of attention for book Table of Contents Altmetric Badge Book Overview Altmetric Badge Chapter 1 ACGCN: A Sequence-Attention-Based Graph Convolutional Model for Enhanced Recommendation Systems Altmetric Badge Chapter 2 Hyperparameter-Free Bi-level Knowledge Graph Optimization for Link Prediction Altmetric Badge Chapter 3 SWIFT: State-Space Wavelet Integrated
Inspired by the extensive success of deep learning, graph neural networks (GNNs) have been proposed to learn expressive node representations and demonstrated promising performance in various graph
Cold-start problem is a fundamental challenge for recommendation tasks. Despite the recent advances on Graph Neural Networks (GNNs) incorporate the high-order collaborative signal to alleviate the problem, the embeddings of the cold-start users and items aren't explicitly optimized, and the cold-start neighbors are not dealt with during the graph convolution in GNNs. This paper proposes to pre-train a GNN model before applying it for recommendation. Unlike the goal of recommendation, the pre-training GNN si
Recurrent neural circuits often face inherent complexities in learning and generating their desired outputs, especially when they initially exhibit chaotic spontaneous activity. While the celebrated FORCE learning rule can train chaotic recurrent networks to produce coherent patterns by suppressing chaos, it requires non-local plasticity rules and quick plasticity, raising the question of how synapses adapt on local, biologically plausible timescales to handle potential chaotic dynamics. We propose a novel
artbrain.org #1 Intro to Neuroaesthetic Theory (1997-99) #2 Cinema and the Brain (2000-02) #3 Buildings, Movies and Brains (2003-04) #4 The Phantom Limb (2005) #5 Conference of Neuroaesthetics (2005) #6 Shifter 16: Pluripotential (2007-11) #7 The Psychopathologies of Cognitive Capitalism: Part One (2013) #8 The Psychopathologies of Cognitive Capitalism: Part Two (2014) #9 The Psychopathologies of Cognitive Capitalism: Part Three (2017) #10 Activist Neuroaesthetics (2021) #11 AI and the Brain (2026) Chaoid G
Active learning for efficiently training parametric PINN
Posts about brain functional networks written by Jari Saramäki
The remarkable successes of contemporary large neural networks in generalizing to new data and tasks have been attributed to their ability to implicitly memorize complex training patterns. Boosting model size has proven an effective approach for enabling such memorization, but this can also dramatically increase training and serving costs. Might there be a way to
Skip to content The Asimov Institute Search for: Neural Network Zoo Prequel: Cells and Layers Posted on March 31, 2017April 1, 2017 by Fjodor van Veen Cells The Neural Network Zoo shows different types of cells and various layer connectivity styles, but it doesn’t really go into how each cell type works. A number of cell types I originally gave different colours to differentiate the networks more clearly, but I have since found out that these cells work more or less the same way, so you’ll find