Now showing items 1-6 of 6
Towards tighter integration of machine learning and discrete optimization
(Georgia Institute of Technology, 2019-03-28)
Discrete Optimization algorithms underlie intelligent decision-making in a wide variety of domains. From airline fleet scheduling to data center resource management and matching in ride-sharing services, decisions are often ...
Learning neural algorithms with graph structures
(Georgia Institute of Technology, 2020-01-13)
Graph structures, like syntax trees, social networks, and programs, are ubiquitous in many real world applications including knowledge graph inference, chemistry and social network analysis. Over the past several decades, ...
Doctor AI: Interpretable deep learning for modeling electronic health records
(Georgia Institute of Technology, 2018-05-23)
Deep learning recently has been showing superior performance in complex domains such as computer vision, audio processing and natural language processing compared to traditional statistical methods. Naturally, deep learning ...
Algorithms and analysis for non-convex optimization problems in machine learning
(Georgia Institute of Technology, 2017-05-10)
In this thesis, we propose efficient algorithms and provide theoretical analysis through the angle of spectral methods for some important non-convex optimization problems in machine learning. Specifically, we focus on two ...
Large scale machine learning for geospatial problems in computational sustainability
(Georgia Institute of Technology, 2020-05-14)
The UN laid out 17 Sustainable Development Goals as part of the “The 2030 Agenda for Sustainable Development”. Each goal consists of broad targets - such as increasing the percentage of forested land (indicator 15.1.1) - ...
Deep representation learning on hypersphere
(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, ...