9 citations · 34 across the 14 of their papers we have counts for
17 papers
Exploring Temporal Information Dynamics in Spiking Neural Networks
Youngeun Kim, Yuhang Li, Hyoungseob Park +3
Most existing Spiking Neural Network (SNN) works state that SNNs may utilize temporal information dynamics of spikes. However, an explicit analysis of temporal information dynamics…
Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural Networks
Yuhang Li, Ruokai Yin, Hyoungseob Park +2
We study the Human Activity Recognition (HAR) task, which predicts user daily activity based on time series data from wearable sensors. Recently, researchers use end-to-end Artific…
Loss-based Sequential Learning for Active Domain Adaptation
Kyeongtak Han, Youngeun Kim, Dongyoon Han +1
Active domain adaptation (ADA) studies have mainly addressed query selection while following existing domain adaptation strategies. However, we argue that it is critical to conside…
Addressing Client Drift in Federated Continual Learning with Adaptive Optimization
Yeshwanth Venkatesha, Youngeun Kim, Hyoungseob Park +2
Federated learning has been extensively studied and is the prevalent method for privacy-preserving distributed learning in edge devices. Correspondingly, continual learning is an e…
Adversarial Detection without Model Information
Abhishek Moitra, Youngeun Kim, Priyadarshini Panda
Prior state-of-the-art adversarial detection works are classifier model dependent, i.e., they require classifier model outputs and parameters for training the detector or during ad…
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?
Youngeun Kim, Hyoungseob Park, Abhishek Moitra +3
Recent Spiking Neural Networks (SNNs) works focus on an image classification task, therefore various coding techniques have been proposed to convert an image into temporal binary s…