no code implementations • 19 Nov 2023 • Chanhui Lee, Juhyeon Kim, Yongjun Jeong, Juhyun Lyu, Junghee Kim, Sangmin Lee, Sangjun Han, Hyeokjun Choe, Soyeon Park, Woohyung Lim, Sungbin Lim, Sanghack Lee
Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning.
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.
1 code implementation • ICCV 2021 • Jisoo Mok, Byunggook Na, Hyeokjun Choe, Sungroh Yoon
Current efforts to improve the robustness of neural networks against adversarial examples are focused on developing robust training methods, which update the weights of a neural network in a more robust direction.
1 code implementation • 9 Jun 2021 • Byunggook Na, Jisoo Mok, Hyeokjun Choe, Sungroh Yoon
By analyzing proxy data constructed using various selection methods through data entropy, we propose a novel proxy data selection method tailored for NAS.
no code implementations • 10 Sep 2018 • Seongsik Park, Seijoon Kim, Hyeokjun Choe, Sungroh Yoon
The spiking neural networks (SNNs) are considered as one of the most promising artificial neural networks due to their energy efficient computing capability.
no code implementations • 6 Oct 2016 • Hyeokjun Choe, Seil Lee, Hyunha Nam, Seongsik Park, Seijoon Kim, Eui-Young Chung, Sungroh Yoon
The second is the popularity of NAND flash-based solid-state drives (SSDs) containing multicore processors that can accommodate extra computation for data processing.