116 citations · 146 across the 10 of their papers we have counts for
11 papers
Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems
Yeshwanth Venkatesha, Youngeun Kim, Hyoungseob Park +1
Federated Learning (FL) is a privacy-preserving distributed machine learning approach geared towards applications in edge devices. However, the problem of designing custom neural a…
Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient
Yuhang Li, Youngeun Kim, Hyoungseob Park +1
Spiking Neural Networks (SNNs) are recognized as the candidate for the next-generation neural networks due to their bio-plausibility and energy efficiency. Recently, researchers ha…
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…
Exploring Lottery Ticket Hypothesis in Spiking Neural Networks
Youngeun Kim, Yuhang Li, Hyoungseob Park +3
Spiking Neural Networks (SNNs) have recently emerged as a new generation of low-power deep neural networks, which is suitable to be implemented on low-power mobile/edge devices. As…
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…
Neuromorphic Data Augmentation for Training Spiking Neural Networks
Yuhang Li, Youngeun Kim, Hyoungseob Park +2
Developing neuromorphic intelligence on event-based datasets with Spiking Neural Networks (SNNs) has recently attracted much research attention. However, the limited size of event-…
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…
Neural Architecture Search for Spiking Neural Networks
Youngeun Kim, Yuhang Li, Hyoungseob Park +2
Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent high-s…
Robust Federated Learning with Noisy Labels
Seunghan Yang, Hyoungseob Park, Junyoung Byun +1
Federated learning is a paradigm that enables local devices to jointly train a server model while keeping the data decentralized and private. In federated learning, since local dat…
Meta Batch-Instance Normalization for Generalizable Person Re-Identification
Seokeon Choi, Taekyung Kim, Minki Jeong +2
Although supervised person re-identification (Re-ID) methods have shown impressive performance, they suffer from a poor generalization capability on unseen domains. Therefore, gene…