1 code implementation • 21 Aug 2023 • Dongjin Lee, Juho Lee, Kijung Shin
Specifically, before the training procedure of a victim model, which is a TGNN for link prediction, we inject edge perturbations to the data that are unnoticeable in terms of the four constraints we propose, and yet effective enough to cause malfunction of the victim model.
1 code implementation • 17 Feb 2023 • Dahuin Jung, Dongjin Lee, Sunwon Hong, Hyemi Jang, Ho Bae, Sungroh Yoon
The aim of continual learning is to learn new tasks continuously (i. e., plasticity) without forgetting previously learned knowledge from old tasks (i. e., stability).
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 • 9 Jun 2022 • Dongjin Lee, Kijung Shin
Although machine learning on hypergraphs has attracted considerable attention, most of the works have focused on (semi-)supervised learning, which may cause heavy labeling costs and poor generalization.
no code implementations • 29 Mar 2022 • Mai Lan Tran, Dongjin Lee, Jae-Hun Jung
In \cite{TPJ}, the new concept of the {\it {\color{black}{Overlap}} matrix} has been proposed, which visualizes how those cycles are interconnected over the music flow, in a matrix form.
1 code implementation • 30 Jan 2022 • Byunggook Na, Jisoo Mok, Seongsik Park, Dongjin Lee, Hyeokjun Choe, Sungroh Yoon
We investigate the design choices used in the previous studies in terms of the accuracy and number of spikes and figure out that they are not best-suited for SNNs.
no code implementations • 14 Jun 2021 • Dongjin Lee, Seongsik Park, Jongwan Kim, Wuhyeong Doh, Sungroh Yoon
On MNIST dataset, our proposed student SNN achieves up to 0. 09% higher accuracy and produces 65% less spikes compared to the student SNN trained with conventional knowledge distillation method.
no code implementations • 22 Apr 2021 • Seongsik Park, Dongjin Lee, Sungroh Yoon
Spiking neural networks (SNNs) have emerged as energy-efficient neural networks with temporal information.
1 code implementation • 23 Feb 2021 • Taehyung Kwon, Inkyu Park, Dongjin Lee, Kijung Shin
SLICENSTITCH changes the starting point of each period adaptively, based on the current time, and updates factor matrices (i. e., outputs of CP decomposition) instantly as new data arrives.
1 code implementation • 16 Feb 2021 • Dongjin Lee, Kijung Shin
Consider multiple seasonal time series being collected in real-time, in the form of a tensor stream.