This $1.6 million Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at the University of Pennsylvania from October 2022 through September 2026. The research focuses on developing theoretical tools to build an understanding of why deep neural networks (DNNs) work and when they can fail. Investigators will seek to identify common themes in how artificial and biological systems like the human brain learn. They will...
This $300,000 National Science Foundation project grant supports research into robust machine learning under sparse adversarial attacks through 2025. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), the University of California, Santa Barbara will develop theoretical frameworks and defense methods to make machine learning models resilient against perturbations affecting few data points. Specifically, the researchers aim to establish fundamental limits of...
This $400,000 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to develop a principled and unified mathematical framework for deep learning on low-dimensional data structures. The project aims to bridge the gap between theory and practice of deep learning by designing "white-box" deep neural networks using unrolled optimization schemes to maximize information gain in...
The National Science Foundation (NSF) Division of Information and Intelligent Systems awarded a $600,000 Project Grant to the University of California, San Diego (UCSD) under the Computer and Information Science and Engineering (CFDA 47.070) program. The 3-year grant, effective July 1, 2023, supports research to develop dynamic neural network architectures that can efficiently enable multimodal perception, including vision, audio, and language processing. The research aims to address...
The National Science Foundation (NSF) awarded a $240,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of Central Florida (UCF) Board of Trustees Office of Research. The grant supports a 3-year research project to develop a theoretical analysis that sheds light on the robustness of neural network-based methods and the properties of adversarial training. The research aims to contribute to the development of more robust neural network-based...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program provides $596,797 to the University of California, San Diego (UCSD) from September 1, 2024 to August 31, 2027. The project aims to develop automated frameworks for interpreting neural networks and designing robust, human-understandable neural network models. Key objectives include: (1) automating interpretations that describe the internal functioning of deep...
This $298,450 National Science Foundation project grant supports research to quantify the error landscape of deep neural networks. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), the awardee New York University will employ statistical mechanics methods to characterize the basins of attraction in high-dimensional parameter spaces of deep learning models. The university will measure basin volume distributions and flatness as a function of network parameters...
The University of California, San Diego (UCSD) was awarded a $455,058 project grant from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The 4-year project, which started on July 1, 2024, focuses on developing techniques for a mathematical understanding of deep learning and its application to a variety of neural network models and data sets. The key objectives are to: 1) compare different networks and understand...
The National Science Foundation Division of Computing and Communication Foundations awarded a $150,000 Project Grant to the Georgia TECH Research Corporation, doing business as the Office Of Sponsored Programs, for collaborative research titled "Foundations of Deep Learning: Theory, Robustness, and the Brain?" from December 1, 2021 through November 30, 2024. The research funded under this award will explore deep learning theory, robustness, and applications for understanding brain...
The National Science Foundation awarded The Johns Hopkins University a $900,000 Project Grant under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to conduct collaborative research focused on understanding robustness in machine learning via parsimonious structures from October 1, 2022 to September 30, 2025. Specifically, the University will research conditions under which one can detect adversarial attacks on networks or data poisoning and reconstruct...