--> Login Home A technique for determining relevance scores of process activities using graph-based neural networks Stierle M, Weinzierl S, Harl M, Matzner M (2021) Publication Language: English Publication Type: Journal article, Original article Publication year: 2021 Journal Decision Support Systems Elsevier Book Volume: 144 Article Number: 113511 URI: http://www.sciencedirect.com/science/article/pii/S016792362100021X DOI: 10.1016/j.dss.2021.113511 Open Access Link: https://doi.org/10.1016/j.dss.2021.1135

Recurrent spiking neural networks (RSNNs) are notoriously difficult to train because of the vanishing gradient problem that is enhanced by the binary nature of the spikes. In this paper, we review the ability of the current state-of-the-art RSNNs to solve long-term memory tasks, and show that they have strong constraints both in performance, and for their implementation on hardware analog neuromorphic processors. We present a novel spiking neural network that circumvents these limitations. Our biologically

Support Vector Machines (SVMs) are powerful supervised learning algorithms commonly used for classification and regression tasks. Known for their effectiveness in handling both linear and nonlinear data, SVMs provide a versatile toolkit for machine learning practitioners. In this post, we'll explore SVM fundamentals, how margins influence their generalization capabilities, and how kernels enable SVMs to

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機械学習の世界において、画像といえばConvolutional Neural Network(以下CNN)というのは、うどんといえば香川くらい当たり前のこととして認識されています。しかし、そのCNNとは何なのか、という解説は意外と少なかったりします。 そこで、本記事ではCN

The text discusses various topics including communication clarity, neural network training methods, forex trading strategies, and technical implementations. It touches on the importance of accurate data interpretation, activation functions in neural networks, and the challenges of developing profitable trading systems. The author also mentions their work with ResNet for forex and the use of genetic optimizers, while emphasizing the need for proper validation techniques like cross-validation. Additionally, t

Skip to content Machine Learning (Theory) Machine learning and learning theory research Posted on 7/11/2011 by RichardSocher Interesting Neural Network Papers at ICML 2011 Maybe it’s too early to call, but with four separate Neural Network sessions at this year’s ICML , it looks like Neural Networks are making a comeback. Here are my highlights of these sessions. In general, my feeling is that these papers both demystify deep learning and show its broader applicability. The first observation I made is

Deep neural networks have demonstrated a high potential on image classification tasks while presenting new computational challenges to the machine learning community. Due to the complexity and vanishing gradient problem, it normally takes longer time and more

NeurIPS Proceedings Search Average gradient outer product as a mechanism for deep neural collapse Daniel Beaglehole, Peter Súkeník, Marco Mondelli, Mikhail Belkin Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Main Conference Track Abstract Deep Neural Collapse (DNC) refers to the surprisingly rigid structure of the data representations in the final layers of Deep Neural Networks (DNNs). Though the phenomenon has been measured in a variety of settings, its emergence is typically

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