23 citations · 25 across the 8 of their papers we have counts for
8 papers
SketchOGD: Memory-Efficient Continual Learning
Youngjae Min, Benjamin Wright, Jeremy Bernstein +1
When machine learning models are trained continually on a sequence of tasks, they are often liable to forget what they learned on previous tasks--a phenomenon known as catastrophic…
Data-Driven Control with Inherent Lyapunov Stability
Youngjae Min, Spencer M. Richards, Navid Azizan
Recent advances in learning-based control leverage deep function approximators, such as neural networks, to model the evolution of controlled dynamical systems over time. However,…
DS-K3DOM: 3-D Dynamic Occupancy Mapping with Kernel Inference and Dempster-Shafer Evidential Theory
Juyeop Han, Youngjae Min, Hyeok-Joo Chae +2
Occupancy mapping has been widely utilized to represent the surroundings for autonomous robots to perform tasks such as navigation and manipulation. While occupancy mapping in 2-D…
ORFit: One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares
Youngjae Min, Namhoon Cho, Navid Azizan
While large machine learning models have shown remarkable performance in various domains, their training typically requires iterating for many passes over the training data. Howeve…
Informative Planning of Mobile Sensor Networks in GPS-Denied Environments
Youngjae Min, Soon-Seo Park, Han-Lim Choi
This paper considers the problem to plan mobile sensor networks for target localization task in GPS-denied environments. Most researches on mobile sensor networks assume that the s…
Shallow Neural Network can Perfectly Classify an Object following Separable Probability Distribution
Youngjae Min, Hye Won Chung
Guiding the design of neural networks is of great importance to save enormous resources consumed on empirical decisions of architectural parameters. This paper constructs shallow s…
Online Gaussian Process State-Space Model: Learning and Planning for Partially Observable Dynamical Systems
Soon-Seo Park, Young-Jin Park, Youngjae Min +1
This paper proposes an online learning method of Gaussian process state-space model (GP-SSM). GP-SSM is a probabilistic representation learning scheme that represents unknown state…
A Distributed ADMM Approach to Non-Myopic Path Planning for Multi-Target Tracking
Soon-Seo Park, Youngjae Min, Jung-Su Ha +2
This paper investigates non-myopic path planning of mobile sensors for multi-target tracking. Such problem has posed a high computational complexity issue and/or the necessity of h…