no code implementations • 1 Aug 2023 • Seongsik Park, Jeonghee Jo, Jongkil Park, YeonJoo Jeong, Jaewook Kim, Suyoun Lee, Joon Young Kwak, Inho Kim, Jong-Keuk Park, Kyeong Seok Lee, Gye Weon Hwang, Hyun Jae Jang
Deep spiking neural networks (SNNs) are promising neural networks for their model capacity from deep neural network architecture and energy efficiency from SNNs' operations.
no code implementations • 28 Jun 2023 • Jiwon Park, Jeonghee Jo, Sungroh Yoon
Mass spectra, which are agglomerations of ionized fragments from targeted molecules, play a crucial role across various fields for the identification of molecular structures.
no code implementations • CVPR 2022 • Jongwan Kim, Dongjin Lee, Byunggook Na, Seongsik Park, Jeonghee Jo, Sungroh Yoon
In terms of image quality, the LPIPS score improves by up to 12% and the reconstruction speed is 87% higher than that of ET-Net.
1 code implementation • 29 Jun 2021 • Bumju Kwak, Jiwon Park, Taewon Kang, Jeonghee Jo, Byunghan Lee, Sungroh Yoon
In recent years, molecular representation learning has emerged as a key area of focus in various chemical tasks.
no code implementations • 14 Jun 2021 • Jeonghee Jo, Bumju Kwak, Byunghan Lee, Sungroh Yoon
Message passing neural network provides an effective framework for capturing molecular geometric features with the perspective of a molecule as a graph.