Search Results for author: Ross Maciejewski

Found 11 papers, 5 papers with code

GeoExplainer: A Visual Analytics Framework for Spatial Modeling Contextualization and Report Generation

1 code implementation25 Aug 2023 Fan Lei, Yuxin Ma, Stewart Fotheringham, Elizabeth Mack, ZiQi Li, Mehak Sachdeva, Sarah Bardin, Ross Maciejewski

As analysts create their spatial models, our framework flags potential issues with model parameter selections, utilizes template-based text generation to summarize model outputs, and links with external knowledge repositories to provide annotations that help to explain the model results.

regression Text Generation

Privacy-preserving Graph Analytics: Secure Generation and Federated Learning

no code implementations30 Jun 2022 Dongqi Fu, Jingrui He, Hanghang Tong, Ross Maciejewski

Directly motivated by security-related applications from the Homeland Security Enterprise, we focus on the privacy-preserving analysis of graph data, which provides the crucial capacity to represent rich attributes and relationships.

Federated Learning Graph Generation +2

DISCO: Comprehensive and Explainable Disinformation Detection

1 code implementation9 Mar 2022 Dongqi Fu, Yikun Ban, Hanghang Tong, Ross Maciejewski, Jingrui He

Disinformation refers to false information deliberately spread to influence the general public, and the negative impact of disinformation on society can be observed in numerous issues, such as political agendas and manipulating financial markets.

Fake News Detection

Towards Conditional Generation of Minimal Action Potential Pathways for Molecular Dynamics

1 code implementation28 Nov 2021 John Kevin Cava, John Vant, Nicholas Ho, Ankita Shukla, Pavan Turaga, Ross Maciejewski, Abhishek Singharoy

In this paper, we utilized generative models, and reformulate it for problems in molecular dynamics (MD) simulation, by introducing an MD potential energy component to our generative model.

Deeper-GXX: Deepening Arbitrary GNNs

no code implementations26 Oct 2021 Lecheng Zheng, Dongqi Fu, Ross Maciejewski, Jingrui He

However, two major problems hinder the deeper GNNs to obtain satisfactory performance, i. e., vanishing gradient and over-smoothing.

Contrastive Learning Link Prediction +2

Metric Learning on Temporal Graphs via Few-Shot Examples

no code implementations29 Sep 2021 Dongqi Fu, Liri Fang, Ross Maciejewski, Vetle I Torvik, Jingrui He

Graph metric learning methods aim to learn the distance metric over graphs such that similar graphs are closer and dissimilar graphs are farther apart.

Drug Discovery Graph Classification +2

InfoFair: Information-Theoretic Intersectional Fairness

no code implementations24 May 2021 Jian Kang, Tiankai Xie, Xintao Wu, Ross Maciejewski, Hanghang Tong

The vast majority of the existing works on group fairness, with a few exceptions, primarily focus on debiasing with respect to a single sensitive attribute, despite the fact that the co-existence of multiple sensitive attributes (e. g., gender, race, marital status, etc.)

BIG-bench Machine Learning Fairness

A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes

1 code implementation15 Sep 2020 Yuxin Ma, Arlen Fan, Jingrui He, Arun Reddy Nelakurthi, Ross Maciejewski

Transfer Learning is intended to relax this assumption by modeling relationships between domains, and is often applied in deep learning applications to reduce the demand for labeled data and training time.

Descriptive Image Classification +1

Diagnosing Concept Drift with Visual Analytics

no code implementations28 Jul 2020 Weikai Yang, Zhen Li, Mengchen Liu, Yafeng Lu, Kelei Cao, Ross Maciejewski, Shixia Liu

Concept drift is a phenomenon in which the distribution of a data stream changes over time in unforeseen ways, causing prediction models built on historical data to become inaccurate.

text-classification Text Classification

Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics

1 code implementation17 Jul 2019 Yuxin Ma, Tiankai Xie, Jundong Li, Ross Maciejewski

Machine learning models are currently being deployed in a variety of real-world applications where model predictions are used to make decisions about healthcare, bank loans, and numerous other critical tasks.

BIG-bench Machine Learning Data Poisoning

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