Publications
Tagged As
GraphChallenge.org sparse deep neural network performance [e-print]
Summary
Summary
The MIT/IEEE/Amazon GraphChallenge.org encourages community approaches to developing new solutions for analyzing graphs and sparse data. Sparse AI analytics present unique scalability difficulties. The Sparse Deep Neural Network (DNN) Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a challenge that is reflective...
AI data wrangling with associative arrays [e-print]
Summary
Summary
The AI revolution is data driven. AI "data wrangling" is the process by which unusable data is transformed to support AI algorithm development (training) and deployment (inference). Significant time is devoted to translating diverse data representations supporting the many query and analysis steps found in an AI pipeline. Rigorous mathematical...
Graph matching via multi-scale heat diffusion
Summary
Summary
We propose a novel graph matching algorithm that uses ideas from graph signal processing to match vertices of graphs using alternative graph representations. Specifically, we consider a multi-scale heat diffusion on the graphs to create multiple weighted graph representations that incorporate both direct adjacencies as well as local structures induced...
AI enabling technologies: a survey
Summary
Summary
Artificial Intelligence (AI) has the opportunity to revolutionize the way the United States Department of Defense (DoD) and Intelligence Community (IC) address the challenges of evolving threats, data deluge, and rapid courses of action. Developing an end-to-end artificial intelligence system involves parallel development of different pieces that must work together...
Artificial intelligence: short history, present developments, and future outlook, final report
- Artificial Intelligence Technology and Systems
- Homeland Sensors and Analytics
- Lincoln Laboratory Supercomputing Center
- Cyber System Assessments
- Cyber Operations and Analysis Technology
- Cyber-Physical Systems
- Artificial Intelligence Software Architectures and Algorithms
- Space Systems Analysis and Test
Summary
Summary
The Director's Office at MIT Lincoln Laboratory (MIT LL) requested a comprehensive study on artificial intelligence (AI) focusing on present applications and future science and technology (S&T) opportunities in the Cyber Security and Information Sciences Division (Division 5). This report elaborates on the main results from the study. Since the...
Detection and characterization of human trafficking networks using unsupervised scalable text template matching
Summary
Summary
Human trafficking is a form of modern-day slavery affecting an estimated 40 million victims worldwide, primarily through the commercial sexual exploitation of women and children. In the last decade, the advertising of victims has moved from the streets to websites on the Internet, providing greater efficiency and anonymity for sex...
Neural network topologies for sparse training
Summary
Summary
The sizes of deep neural networks (DNNs) are rapidly outgrowing the capacity of hardware to store and train them. Research over the past few decades has explored the prospect of sparsifying DNNs before, during, and after training by pruning edges from the underlying topology. The resulting neural network is known...
Colorization of H&E stained tissue using deep learning
Summary
Summary
Histopathology is a critical tool in the diagnosis and stratification of cancer. Digital Pathology involves the scanning of stained and fixed tissue samples to produce high-resolution images that can be used for computer-aided diagnosis and research. A common challenge in digital pathology related to the quality and characteristics of staining...
Twitter language identification of similar languages and dialects without ground truth
Summary
Summary
We present a new method to bootstrap filter Twitter language ID labels in our dataset for automatic language identification (LID). Our method combines geolocation, original Twitter LID labels, and Amazon Mechanical Turk to resolve missing and unreliable labels. We are the first to compare LID classification performance using the MIRA...
Predicting and analyzing factors in patent litigation
Summary
Summary
Patent litigation is an expensive and time-consuming process. To minimize its impact on the participants in the patent lifecycle, automatic determination of litigation potential is a compelling machine learning application. In this paper, we consider preliminary methods for the prediction of a patent being involved in litigation using metadata, content...