Search Results for author:
Found 25 papers, 4 papers with code
Transfer Learning Methods for Domain Adaptation in Technical Logbook Datasets
Event identification in technical logbooks poses challenges given the limited logbook data available in specific technical domains, the large set of possible classes, and logbook entries typically being in short form and non-standard technical language.
Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-Organizing Maps
The major challenge that makes crafting such a system difficult is known as catastrophic forgetting - an agent, such as one based on artificial neural networks (ANNs), struggles to retain previously acquired knowledge when learning from new samples.
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.
Backpropagation-Free 4D Continuous Ant-Based Neural Topology Search
Continuous Ant-based Topology Search (CANTS) is a previously introduced novel nature-inspired neural architecture search (NAS) algorithm that is based on ant colony optimization (ACO).
Predicted Embedding Power Regression for Large-Scale Out-of-Distribution Detection
Out-of-distribution (OOD) inputs can compromise the performance and safety of real world machine learning systems.
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.
A Large-Scale Annotated Multivariate Time Series Aviation Maintenance Dataset from the NGAFID
To overcome this, we use the National General Aviation Flight Information Database (NGAFID), which contains flights recorded during regular operation of aircraft, and maintenance logs to construct a part failure dataset.
Addressing Tactic Volatility in Self-Adaptive Systems Using Evolved Recurrent Neural Networks and Uncertainty Reduction Tactics
Simulations using 52, 106 tactic records demonstrate that: I) eRNN is an effective prediction mechanism, II) TVA-E represents an improvement over existing state-of-the-art processes in accounting for tactic volatility, and III) Uncertainty reduction tactics are beneficial in accounting for tactic volatility.
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.
Robust Augmentation for Multivariate Time Series Classification
We also show that augmentation improves accuracy for recurrent and self attention based architectures.
Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay
A lifelong learning agent is able to continually learn from potentially infinite streams of pattern sensory data.
Predictive Maintenance for General Aviation Using Convolutional Transformers
However, the development of such systems has been limited due to a lack of publicly labeled multivariate time series (MTS) sensor data.
Handling Extreme Class Imbalance in Technical Logbook Datasets
In this paper we focus on the problem of technical issue classification by considering logbook datasets from the automotive, aviation, and facilities maintenance domains.
NLP Tools for Predictive Maintenance Records in MaintNet
Processing maintenance logbook records is an important step in the development of predictive maintenance 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.
MaintNet: A Collaborative Open-Source Library for Predictive Maintenance Language Resources
Furthermore, it provides a way to encourage discussion on and sharing of new datasets and tools for logbook data analysis.
Improving Neuroevolution Using Island Extinction and Repopulation
This island based strategy is additionally compared to NEAT's (NeuroEvolution of Augmenting Topologies) speciation strategy.
Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility
Unfortunately, current self-adaptive approaches do not account for tactic volatility in their decision-making processes, and merely assume that tactics do not experience volatility.
Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution
This paper presents a new algorithm, Evolutionary eXploration of Augmenting Memory Models (EXAMM), which is capable of evolving recurrent neural networks (RNNs) using a wide variety of memory structures, such as Delta-RNN, GRU, LSTM, MGU and UGRNN cells.
Accelerating the Evolution of Convolutional Neural Networks with Node-Level Mutations and Epigenetic Weight Initialization
This paper examines three generic strategies for improving the performance of neuro-evolution techniques aimed at evolving convolutional neural networks (CNNs).
Optimizing Long Short-Term Memory Recurrent Neural Networks Using Ant Colony Optimization to Predict Turbine Engine Vibration
This research improves the performance of the most effective LSTM network design proposed in the previous work by using a promising neuroevolution method based on ant colony optimization (ACO) to develop and enhance the LSTM cell structure of the network.
Large Scale Evolution of Convolutional Neural Networks Using Volunteer Computing
EXACT is in part modeled after the neuroevolution of augmenting topologies (NEAT) algorithm, with notable exceptions to allow it to scale to large scale distributed computing environments and evolve networks with convolutional filters.
