59 citations · 102 across the 6 of their papers we have counts for
6 papers
Stochastic Resetting Mitigates Latent Gradient Bias of SGD from Label Noise
Youngkyoung Bae, Yeongwoo Song, Hawoong Jeong
Giving up and starting over may seem wasteful in many situations such as searching for a target or training deep neural networks (DNNs). Our study, though, demonstrates that resett…
Inferring the Langevin Equation with Uncertainty via Bayesian Neural Networks
Youngkyoung Bae, Seungwoong Ha, Hawoong Jeong
Pervasive across diverse domains, stochastic systems exhibit fluctuations in processes ranging from molecular dynamics to climate phenomena. The Langevin equation has served as a c…
Unexpected advantages of exploitation for target searches in complex networks
Youngkyoung Bae, Gangmin Son, Hawoong Jeong
Exploitation universally emerges in various decision-making contexts, e.g., animals foraging, web surfing, the evolution of scientists' research topics, and our daily lives. Despit…
Attaining entropy production and dissipation maps from Brownian movies via neural networks
Youngkyoung Bae, Dong-Kyum Kim, Hawoong Jeong
Quantifying entropy production (EP) is essential to understand stochastic systems at mesoscopic scales, such as living organisms or biological assemblies. However, without tracking…
Inertial effects on the Brownian gyrator
Youngkyoung Bae, Sangyun Lee, Juin Kim +1
The recent interest into the Brownian gyrator has been confined chiefly to the analysis of Brownian dynamics both in theory and experiment despite the applicability of general case…
Learning entropy production via neural networks
Dong-Kyum Kim, Youngkyoung Bae, Sangyun Lee +1
This Letter presents a neural estimator for entropy production, or NEEP, that estimates entropy production (EP) from trajectories of relevant variables without detailed information…