AKA, how to use straight lines to capture curved geometry in neural networks
NeurIPS Proceedings Search Synthesizing the preferred inputs for neurons in neural networks via deep generator networks Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, Jeff Clune Advances in Neural Information Processing Systems 29 (NIPS 2016) Abstract Deep neural networks (DNNs) have demonstrated state-of-the-art results on many pattern recognition tasks, especially vision classification problems. Understanding the inner workings of such computational brains is both fascinating basic science t
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Is the capability of AI determined only by its parameter scale and interaction patterns? First observed in 2020, subtle performance differences across network structures inspired a five-year exploration that ultimately revealed how network motifs shape the performance of artificial neural networks
ICANN 2014 : The 24th International Conference on Artificial Neural Networks
This study investigates the realm of liquid neural networks (LNNs) and their deployment on neuromorphic hardware platforms. It provides an in-depth analysis of Liquid State Machines (LSMs) and explores the adaptation of LNN architectures to neuromorphic systems, highlighting the theoretical foundations and practical applications. We introduce a pioneering approach to image classification on the CIFAR-10 dataset by implementing Liquid Neural Networks (LNNs) on state-of-the-art neuromorphic hardware platforms
# temporal convolutional networks ## Multiple Time Series Forecasting with Temporal Convolutional Networks (TCN) in Python In this article you will learn an easy, fast, step-by-step way to use Convolutional Neural Networks for multiple time series forecasting in Python. We will use the NeuralForecast library which implements the Temporal Convolutional Network (TCN) architecture. Temporal Convolutional Network (TCN) This architecture is a variant of the Convolutional Neural Network (CNN) architecture that
Abstract page for arXiv paper 2207.02098: Neural Networks and the Chomsky Hierarchy
Deep Neural networks – also known as Multi Layer Perceptrons – are universal approximator, i.e. an ML approximator to any function in a bounded continuous domain. They’re the most important and relevant ML tool on the field of supervised learning as they’re included in models related to most supervised tasks. The structure of a basic NN is the following: one input layer of size \(D\), \(L\) hidden layers of size \(K\), and one output layer. It is a feedfoward network: the computation performed by
In this article, I will explain how to perform Training Neural Networks Using Backpropagation. Backpropagation is the primary algorithm used to train neural networks. It is a supervised learning algorithm that involves iteratively adjusting the weights of a neural network to minimize the difference between the predicted output and the target output. Here are the