Project Grant FA95501817007

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...

This $150,000 EAGER: IMPRESS-U Project Grant awarded on January 1, 2024 by the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will support research at Pennsylvania State University (Penn State) to develop new Random Matrix Theory (RMT) techniques and apply them to improving the performance of Deep Neural Networks (DNNs). The key objectives are to: (1) further advance RMT methods, and (2) leverage these techniques to enhance accuracy, convergence, and...

This $300,000 Project Grant was awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the Texas A&M Engineering Experiment Station (Tees) for the period of July 1, 2024 to June 30, 2027. The grant supports the development of a new type of "Quantized-Analog Error-Correcting Code (QA-ECC)" to enable reliable in-memory computing for next-generation artificial intelligence (AI) systems. The research aims to...

This three-year Project Grant from the National Science Foundation's Division of Computing and Communication Foundations will fund the development of analysis and design techniques to provide formally guaranteed error bounds for deep neural networks used in autonomous cyber-physical systems. Totaling $999,996, the award will support research from June 2022 to May 2025. Specifically, the University of California, Los Angeles will investigate two approaches - "correctness-by-training"...

This four-year $800,000 Project Grant from the National Science Foundation's (NSF) Computer and Information Science and Engineering program (CFDA 47.070) will support research at the University of California, Los Angeles (UCLA) to develop theoretical tools for understanding deep neural networks (DNNs). The research aims to identify common themes in how artificial and biological systems like the human brain learn. It will investigate the hypothesis that DNNs succeed when learning tasks exhibit...

The National Science Foundation Office of Advanced Cyberinfrastructure awarded the University of Texas at Austin a $1.2 million Project Grant under the Computer and Information Science and Engineering program (CFDA 47.070) from September 1, 2022 to August 31, 2025. The grant funds research to develop a rigorous and reliable scientific deep learning framework for forward, inverse, and uncertainty quantification problems in computational science and engineering. Specific objectives include...

This $300,000 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CFDA 47.070) program will support research to develop new error-correcting codes for reliable in-memory computing in next-generation artificial intelligence (AI) systems. The project, titled "COLLABORATIVE RESEARCH: NSF-BSF: CIF: SMALL: ERROR-CORRECTING CODES FOR NEXT-GENERATION ARTIFICIAL INTELLIGENCE," will explore a new approach called...

The University of Texas at Austin was awarded a $450,000 Project Grant from the National Science Foundation (NSF) Division of Computing and Communication Foundations (CFDA 47.070 Computer and Information Science and Engineering) to conduct collaborative research on energy-efficient machine learning hardware for edge computing applications. The key research objectives are to: (1) develop novel weightless neural network architectures that combine the benefits of traditional deep neural networks...

The National Science Foundation Division of Mathematical Sciences awarded a $650,000 Project Grant to the University of Texas at Austin to support the CRCNS RESEARCH PROJECT: MULTIPLY AND CONQUER: REPLICA-MEAN-FIELD LIMIT FOR NEURAL NETWORKS from September 15, 2021 through August 31, 2024. This award will fund research under the NSF's Mathematical and Physical Sciences program (CFDA 47.049), which aims to promote progress in these fields and strengthen the nation's scientific enterprise....

This National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Project Grant award of $600,000 to the Massachusetts Institute of Technology (MIT) supports research into developing better algorithms for machine learning problems that involve sequential data with rich dependency structures. The project will explore learning methods for linear dynamical systems, graphical models, and hidden Markov models, with the goal of proving rigorous theoretical...