Search Results for author: Sungyong Seo

Found 9 papers, 4 papers with code

When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning

no code implementations31 Mar 2022 Chuizheng Meng, Sungyong Seo, Defu Cao, Sam Griesemer, Yan Liu

Physics-informed machine learning (PIML), referring to the combination of prior knowledge of physics, which is the high level abstraction of natural phenomenons and human behaviours in the long history, with data-driven machine learning models, has emerged as an effective way to mitigate the shortage of training data, to increase models' generalizability and to ensure the physical plausibility of results.

BIG-bench Machine Learning Physics-informed machine learning

Controlling Neural Networks with Rule Representations

1 code implementation NeurIPS 2021 Sungyong Seo, Sercan O. Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister

The key aspect of DeepCTRL is that it does not require retraining to adapt the rule strength -- at inference, the user can adjust it based on the desired operation point on accuracy vs. rule verification ratio.

Decision Making

Network Inference from a Mixture of Diffusion Models for Fake News Mitigation

no code implementations8 Aug 2020 Karishma Sharma, Xinran He, Sungyong Seo, Yan Liu

Users influential in the propagation of true and fake contents are identified using the inferred diffusion dynamics.

Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning

no code implementations15 Jun 2020 Sungyong Seo, Chuizheng Meng, Sirisha Rambhatla, Yan Liu

Although the knowledge of governing partial differential equations (PDE) of data can be helpful for the fast adaptation to few observations, it is mostly infeasible to exactly find the equation for observations in real-world physical systems.


COVID-19 on Social Media: Analyzing Misinformation in Twitter Conversations

3 code implementations26 Mar 2020 Karishma Sharma, Sungyong Seo, Chuizheng Meng, Sirisha Rambhatla, Yan Liu

The analysis is presented and updated on a publically accessible dashboard (https://usc-melady. github. io/COVID-19-Tweet-Analysis) to track the nature of online discourse and misinformation about COVID-19 on Twitter from March 1 - June 5, 2020.

Fact Checking Misinformation

A Deep Structural Model for Analyzing Correlated Multivariate Time Series

no code implementations2 Jan 2020 Changwei Hu, Yifan Hu, Sungyong Seo

The seasonality component is approximated via a non-liner function of a set of Fourier terms, and the event components are learned by a simple linear function of regressor encoding the event dates.

Time Series Time Series Analysis

Differentiable Physics-informed Graph Networks

1 code implementation8 Feb 2019 Sungyong Seo, Yan Liu

While physics conveys knowledge of nature built from an interplay between observations and theory, it has been considered less importantly in deep neural networks.

Automatically Inferring Data Quality for Spatiotemporal Forecasting

no code implementations ICLR 2018 Sungyong Seo, Arash Mohegh, George Ban-Weiss, Yan Liu

Spatiotemporal forecasting has become an increasingly important prediction task in machine learning and statistics due to its vast applications, such as climate modeling, traffic prediction, video caching predictions, and so on.

Traffic Prediction

CSI: A Hybrid Deep Model for Fake News Detection

2 code implementations20 Mar 2017 Natali Ruchansky, Sungyong Seo, Yan Liu

Specifically, we incorporate the behavior of both parties, users and articles, and the group behavior of users who propagate fake news.

Fake News Detection Misinformation

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