7 citations · 18 across the 21 of their papers we have counts for
16 papers · 1 filter
Uncertainty-Driven Replay Memory for Reinforcement Learning
Sheeraja Rajakrishnan, Alexander G. Ororbia, Travis Desell +1
Uncertainty estimation provides promising capabilities for reinforcement learning (RL) agents. Notably, estimating uncertainty can reduce the training time and enable agents to obt…
Unsupervised Continual Learning with Growing Self-Organizing Maps and Synthetic Replay
Pujan Thapa, Alexander Ororbia, Travis Desell
This work presents a generative continual learning framework based on growing self-organizing maps (GSOMs) that are augmented with learned distributional statistics as well as enco…
Domain Feature Collapse: Implications for Out-of-Distribution Detection and Solutions
Hong Yang, Devroop Kar, Qi Yu +2
Why do state-of-the-art OOD detection methods exhibit catastrophic failure when models are trained on single-domain datasets? We provide the first theoretical explanation for this…
Class Incremental Continual Learning with Self-Organizing Maps and Synthetic Replay
Pujan Thapa, Alexander Ororbia, Travis Desell
This work introduces a novel generative continual learning framework based on self-organizing maps (SOMs), a brain-inspired natural computing model, extended with learned distribut…
Directly Learning Stock Trading Strategies Through Profit Guided Loss Functions
Devroop Kar, Zimeng Lyu, Sheeraja Rajakrishnan +4
Stock trading has always been a challenging task due to the highly volatile nature of the stock market. Making sound trading decisions to generate profit is particularly difficult…
Can We Ignore Labels In Out of Distribution Detection?
Hong Yang, Qi Yu, Travis Desell
Out-of-distribution (OOD) detection methods have recently become more prominent, serving as a core element in safety-critical autonomous systems. One major purpose of OOD detection…
Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-Organizing Maps
Hitesh Vaidya, Travis Desell, Ankur Mali +1
An intelligent system capable of continual learning is one that can process and extract knowledge from potentially infinitely long streams of pattern vectors. The major challenge t…
Minimally Supervised Topological Projections of Self-Organizing Maps for Phase of Flight Identification
Zimeng Lyu, Pujan Thapa, Travis Desell
Identifying phases of flight is important in the field of general aviation, as knowing which phase of flight data is collected from aircraft flight data recorders can aid in the mo…
Minimally Supervised Learning using Topological Projections in Self-Organizing Maps
Zimeng Lyu, Alexander Ororbia, Rui Li +1
Parameter prediction is essential for many applications, facilitating insightful interpretation and decision-making. However, in many real life domains, such as power systems, medi…
Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting
Zimeng Lyu, Alexander Ororbia, Travis Desell
Time series forecasting (TSF) is one of the most important tasks in data science given the fact that accurate time series (TS) predictive models play a major role across a wide var…
A Large-Scale Annotated Multivariate Time Series Aviation Maintenance Dataset from the NGAFID
Hong Yang, Travis Desell
This paper presents the largest publicly available, non-simulated, fleet-wide aircraft flight recording and maintenance log data for use in predicting part failure and maintenance…
Addressing Tactic Volatility in Self-Adaptive Systems Using Evolved Recurrent Neural Networks and Uncertainty Reduction Tactics
Aizaz Ul Haq, Niranjana Deshpande, AbdElRahman ElSaid +2
Self-adaptive systems frequently use tactics to perform adaptations. Tactic examples include the implementation of additional security measures when an intrusion is detected, or ac…
ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecasting
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…
Robust Augmentation for Multivariate Time Series Classification
Hong Yang, Travis Desell
Neural networks are capable of learning powerful representations of data, but they are susceptible to overfitting due to the number of parameters. This is particularly challenging…
Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay
Hitesh Vaidya, Travis Desell, Alexander Ororbia
A lifelong learning agent is able to continually learn from potentially infinite streams of pattern sensory data. One major historic difficulty in building agents that adapt in thi…
Predictive Maintenance for General Aviation Using Convolutional Transformers
Hong Yang, Aidan LaBella, Travis Desell
Predictive maintenance systems have the potential to significantly reduce costs for maintaining aircraft fleets as well as provide improved safety by detecting maintenance issues b…