no code implementations • 22 Jan 2021 • Jin Young Shin, Cheolhyeong Kim, Hyung Ju Hwang
In this paper, we claim that active inference can be interpreted using reinforcement learning (RL) algorithms and find a theoretical connection between them.
no code implementations • 1 Jan 2021 • Jin Young Shin, Cheolhyeong Kim, Hyung Ju Hwang
In this paper, we claim that active inference can be interpreted using reinforcement learning (RL) algorithms and find a theoretical connection between them.
no code implementations • 6 Sep 2019 • Cheolhyeong Kim, Haeseong Moon, Hyung Ju Hwang
However, past GNN algorithms based on 1-hop neighborhood neural message passing are exposed to a risk of loss of information on local structures and relationships.
no code implementations • ICLR 2019 • Cheolhyeong Kim, Seungtae Park, Hyung Ju Hwang
Wasserstein GAN(WGAN) is a model that minimizes the Wasserstein distance between a data distribution and sample distribution.
no code implementations • 5 Oct 2018 • Cheolhyeong Kim, Seungtae Park, Hyung Ju Hwang
Wasserstein GAN(WGAN) is a model that minimizes the Wasserstein distance between a data distribution and sample distribution.