Recent Submissions

  • Finding Dense Regions of Rapidly Changing Graphs 

    Gabert, Kasimir Georg (Georgia Institute of Technology, 2022-05-02)
    Many of today's massive and rapidly changing graphs contain internal structure---hierarchies of locally dense regions---and finding and tracking this structure is key to detecting emerging behavior, exposing internal activity, ...
  • NEWS DATA VISUALIZATION INTERFACE DEVELOPMENT USING NMF ALGORITHM 

    Ahn, Byeongsoo (Georgia Institute of Technology, 2022-05-03)
    News data is a super large-scale dataset. It covers a wide range of topics ranging from heavy topics such as politics and society to beauty and entertainment, relatively light topics. At the same time, it is also the most ...
  • Understanding, Fortifying and Democratizing AI Security 

    Das, Nilaksh (Georgia Institute of Technology, 2022-04-19)
    As we steadily move towards an AI-powered utopia that could only be imagined in lofty fiction in the recent past, a formidable threat is emerging that endangers the acute capitalization of AI in our everyday lives. A growing ...
  • Knowledge Reasoning with Graph Neural Networks 

    Zhang, Yuyu (Georgia Institute of Technology, 2021-12-15)
    Knowledge reasoning is the process of drawing conclusions from existing facts and rules, which requires a range of capabilities including but not limited to understanding concepts, applying logic, and calibrating or ...
  • Towards Performance Portable Graph Algorithms 

    Yasar, Abdurrahman (Georgia Institute of Technology, 2021-12-14)
    In today's data-driven world, our computational resources have become heterogeneous, making the processing of large-scale graphs in an architecture agnostic manner crucial. Traditionally, hand-optimized high-performance ...
  • Cooperation in Multi-Agent Reinforcement Learning 

    Yang, Jiachen (Georgia Institute of Technology, 2021-12-13)
    As progress in reinforcement learning (RL) gives rise to increasingly general and powerful artificial intelligence, society needs to anticipate a possible future in which multiple RL agents must learn and interact in a ...
  • Developing Robust Models, Algorithms, Databases and Tools With Applications to Cybersecurity and Healthcare 

    Freitas, Scott (Georgia Institute of Technology, 2021-12-13)
    As society and technology becomes increasingly interconnected, so does the threat landscape. Once isolated threats now pose serious concerns to highly interdependent systems, highlighting the fundamental need for robust ...
  • Parallel Algorithms and Generalized Frameworks for Learning Large-Scale Bayesian Networks 

    Srivastava, Ankit (Georgia Institute of Technology, 2021-08-13)
    Bayesian networks (BNs) are an important subclass of probabilistic graphical models that employ directed acyclic graphs to compactly represent exponential-sized joint probability distributions over a set of random variables. ...
  • Interactive Visual Text Analytics 

    Kim, Hannah (Georgia Institute of Technology, 2020-12-07)
    Human-in-the-Loop machine learning leverages both human and machine intelligence to build a smarter model. Even with the advances in machine learning techniques, results generated by automated models can be of poor quality ...
  • Learning from Multi-Source Weak Supervision for Neural Text Classification 

    Ren, Wendi (Georgia Institute of Technology, 2020-07-28)
    Text classification is a fundamental text mining task with numerous real-life applications. While deep neural nets have achieved superior performance for text classification, they rely on large-scale labeled data to achieve ...
  • On Computation and Application of Optimal Transport 

    Xie, Yujia (Georgia Institute of Technology, 2021-07-28)
    The Optimal Transport (OT) problem naturally arises in various machine learning problems, where one needs to align data from multiple sources. For example, the training data and application scenarios oftentimes have a ...
  • Optimizing resource allocation in computational sustainability: Models, algorithms and tools 

    Gupta, Amrita (Georgia Institute of Technology, 2021-01-21)
    The 17 Sustainable Development Goals laid out by the United Nations include numerous targets as well as indicators of progress towards sustainable development. Decision-makers tasked with meeting these targets must frequently ...
  • On Using Inductive Biases for Designing Deep Learning Architectures 

    Shrivastava, Harsh (Georgia Institute of Technology, 2020-12-15)
    Recent advancements in field of Artificial Intelligence, especially in the field of Deep Learning (DL), have paved way for new and improved solutions to complex problems occurring in almost all domains. Often we have some ...
  • Performance Primitives for Artificial Neural Networks 

    Dukhan, Marat (Georgia Institute of Technology, 2021-05-10)
    Optimized software implementations of artificial neural networks leverage primitives from performance libraries, such as the BLAS. However, these primitives were prototyped decades ago, and do not necessarily reflect the ...
  • Interval Deep Learning for Uncertainty Quantification in Engineering Problems 

    Betancourt, David (Georgia Institute of Technology, 2021-05-06)
    Deep neural networks are becoming more common in important real-world safety-critical applications where reliability in the predictions is paramount. Despite their exceptional prediction capabilities, current deep neural ...
  • Data-Driven Approach using Machine Learning for Real-Time Flight Path Optimization 

    Kim, Jung Hyun (Georgia Institute of Technology, 2021-04-27)
    Airlines typically gather all available weather information before departure to generate flight routes that avoid hazardous weather while minimizing operating expenditures. However, pilots potentially have to perform ...
  • ROBUST COUNTERFACTUAL LEARNING FOR CLINICAL DECISION-MAKING USING ELECTRONIC HEALTH RECORDS 

    Choudhary, Anirudh (Georgia Institute of Technology, 2020-12-07)
    Building clinical decision support systems, which includes diagnosing patient’s disease states and formulating a treatment plan, is an important step toward personalized medicine. The counterfactual nature of clinical ...
  • Prokaryotic Gene Start Prediction: Algorithms for Genomes and Metagenomes 

    Gemayel, Karl (Georgia Institute of Technology, 2020-12-01)
    Prokaryotic gene-prediction is the task of finding genes in archaeal or bacterial DNA sequences. These genomes consist of alternating gene-coding and non-coding regions, meaning the task is solved by determining the start ...
  • Interactive Scalable Interfaces for Machine Learning Interpretability 

    Hohman, Frederick (Georgia Institute of Technology, 2020-12-01)
    Data-driven paradigms now solve the world's hardest problems by automatically learning from data. Unfortunately, what is learned is often unknown to both the people who train the models and the people they impact. This has ...
  • Deep representation learning on hypersphere 

    Liu, Weiyang (Georgia Institute of Technology, 2020-07-27)
    How to efficiently learn discriminative deep features is arguably one of the core problems in deep learning, since it can benefit a lot of downstream tasks such as visual recognition, object detection, semantic segmentation, ...

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