Search Results for author: Sookyung Kim

Found 9 papers, 2 papers with code

Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery

no code implementations5 Jan 2019 Bhavya Kailkhura, Brian Gallagher, Sookyung Kim, Anna Hiszpanski, T. Yong-Jin Han

We also propose a transfer learning technique and show that the performance loss due to models' simplicity can be overcome by exploiting correlations among different material properties.

BIG-bench Machine Learning Transfer Learning

Deep-dust: Predicting concentrations of fine dust in Seoul using LSTM

no code implementations29 Jan 2019 Sookyung Kim, Jungmin M. Lee, Jiwoo Lee, Jihoon Seo

Polluting fine dusts in South Korea which are mainly consisted of biomass burning and fugitive dust blown from dust belt is significant problem these days.

Time Series Time Series Analysis

Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equation

1 code implementation16 Oct 2020 Sunghyun Park, Kangyeol Kim, Junsoo Lee, Jaegul Choo, Joonseok Lee, Sookyung Kim, Edward Choi

Video generation models often operate under the assumption of fixed frame rates, which leads to suboptimal performance when it comes to handling flexible frame rates (e. g., increasing the frame rate of the more dynamic portion of the video as well as handling missing video frames).

Video Generation

Accelerating exploration of Marine Cloud Brightening impacts on tipping points Using an AI Implementation of Fluctuation-Dissipation Theorem

no code implementations3 Feb 2023 Haruki Hirasawa, Sookyung Kim, Peetak Mitra, Subhashis Hazarika, Salva Ruhling-Cachay, Dipti Hingmire, Kalai Ramea, Hansi Singh, Philip J. Rasch

Here, we describe an AI model, named AiBEDO, that can be used to rapidly projects climate responses to forcings via a novel application of the Fluctuation-Dissipation Theorem (FDT).

A Domain-Independent Agent Architecture for Adaptive Operation in Evolving Open Worlds

no code implementations9 Jun 2023 Shiwali Mohan, Wiktor Piotrowski, Roni Stern, Sachin Grover, Sookyung Kim, Jacob Le, Johan de Kleer

Model-based reasoning agents are ill-equipped to act in novel situations in which their model of the environment no longer sufficiently represents the world.

Visual Reasoning

Are Generative AI systems Capable of Supporting Information Needs of Patients?

no code implementations31 Jan 2024 Shreya Rajagopal, Subhashis Hazarika, Sookyung Kim, Yan-ming Chiou, Jae Ho Sohn, Hari Subramonyam, Shiwali Mohan

Given the recent advancements in Generative AI models aimed at improving the healthcare system, our work investigates whether and how generative visual question answering systems can responsibly support patient information needs in the context of radiology imaging data.

Computed Tomography (CT) Generative Visual Question Answering +2

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