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Found 8 papers, 0 papers with code
Minimally Supervised Topological Projections of Self-Organizing Maps for Phase of Flight Identification
General aviation flight data for phase of flight identification is usually per-second data, comes on a large scale, and is class imbalanced.
Minimally Supervised Learning using Topological Projections in Self-Organizing Maps
In this work, we introduce a semi-supervised learning approach based on topological projections in self-organizing maps (SOMs), which significantly reduces the required number of labeled data points to perform parameter prediction, effectively exploiting information contained in large unlabeled datasets.
Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting
Results demonstrate that ONE-NAS outperforms traditional statistical time series forecasting methods, including online linear regression, fixed long short-term memory (LSTM) and gated recurrent unit (GRU) models trained online, as well as state-of-the-art, online ARIMA strategies.
ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecasting
Time series forecasting (TSF) is one of the most important tasks in data science, as accurate time series (TS) predictions can drive and advance a wide variety of domains including finance, transportation, health care, and power systems.
Continuous Ant-Based Neural Topology Search
This work introduces a novel, nature-inspired neural architecture search (NAS) algorithm based on ant colony optimization, Continuous Ant-based Neural Topology Search (CANTS), which utilizes synthetic ants that move over a continuous search space based on the density and distribution of pheromones, is strongly inspired by how ants move in the real world.
An Experimental Study of Weight Initialization and Weight Inheritance Effects on Neuroevolution
Weight initialization is critical in being able to successfully train artificial neural networks (ANNs), and even more so for recurrent neural networks (RNNs) which can easily suffer from vanishing and exploding gradients.
Neuroevolutionary Transfer Learning of Deep Recurrent Neural Networks through Network-Aware Adaptation
Transfer learning entails taking an artificial neural network (ANN) that is trained on a source dataset and adapting it to a new target dataset.
Improving Neuroevolution Using Island Extinction and Repopulation
This island based strategy is additionally compared to NEAT's (NeuroEvolution of Augmenting Topologies) speciation strategy.
