no code implementations • 17 Feb 2024 • Zimeng Lyu, Pujan Thapa, Travis Desell
General aviation flight data for phase of flight identification is usually per-second data, comes on a large scale, and is class imbalanced.
no code implementations • 12 Jan 2024 • Zimeng Lyu, Alexander Ororbia, Rui Li, Travis Desell
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.
no code implementations • 20 Feb 2023 • Zimeng Lyu, Alexander Ororbia, Travis Desell
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.
no code implementations • 27 Feb 2022 • Zimeng Lyu, Travis Desell
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.
no code implementations • 21 Nov 2020 • AbdElRahman ElSaid, Joshua Karns, Zimeng Lyu, Alexander Ororbia, Travis Desell
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.
no code implementations • 21 Sep 2020 • Zimeng Lyu, AbdElRahman ElSaid, Joshua Karns, Mohamed Mkaouer, Travis Desell
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.
no code implementations • 4 Jun 2020 • AbdElRahman ElSaid, Joshua Karns, Alexander Ororbia II, Daniel Krutz, Zimeng Lyu, Travis Desell
Transfer learning entails taking an artificial neural network (ANN) that is trained on a source dataset and adapting it to a new target dataset.
no code implementations • 15 May 2020 • Zimeng Lyu, Joshua Karns, AbdElRahman ElSaid, Travis Desell
This island based strategy is additionally compared to NEAT's (NeuroEvolution of Augmenting Topologies) speciation strategy.