Project Grant 2145703
- 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 $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...
- Federal Project Grant Award Summary Carnegie Mellon University received a $600,000 Project Grant from the National Science Foundation (NSF) Division of Information and Intelligent Systems under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070), awarded June 1, 2025, with completion targeted for May 31, 2028. The award supports research investigating computational mechanisms underlying visual cortical recurrent circuits in the brain, with the...
- 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 National Science Foundation awarded a $449,998 project grant to Carnegie Mellon University under the Mathematical and Physical Sciences program (CFDA 47.049) to support research titled "COLLABORATIVE RESEARCH: NEW PERSPECTIVES ON DEEP LEARNING: BRIDGING APPROXIMATION, STATISTICAL, AND ALGORITHMIC THEORIES" from November 1, 2021 to October 31, 2024. The grant aims to promote progress in mathematical and physical sciences by increasing scientific knowledge and understanding of...
- The National Science Foundation (NSF) awarded a $225,000 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to The Leland Stanford Junior University. The 3-year grant, effective July 1, 2024, aims to gain a deeper theoretical understanding of the statistical properties of neural networks, which have revolutionized science and engineering. Key research directions include studying the distinguishing features of deep neural networks compared to classical statistical...
- 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...
- This Project Grant from the National Science Foundation's Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering federal grant program (CFDA 47.070), provides $1,127,925 to Carnegie Mellon University for research titled "Foundations of Self-Supervised Learning through the Lens of Probabilistic Generative Models." The research aims to develop scientific and mathematical foundations for self-supervised learning by analyzing aspects...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to advance artificial intelligence (AI) by investigating the mathematical foundations and practical applications of deep learning models. The $600,000 award, with a performance period from December 2024 to November 2027, will support research focused on understanding the properties of neural networks, the function spaces and data representations that emerge...
- This $400,000 federal Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to The Ohio State University. The grant supports collaborative research to develop a principled and unified mathematical framework for deep learning on low-dimensional data structures. The key objectives are to: 1) Design "white-box" deep neural networks optimized for information gain and representation...
The National Science Foundation awarded a $245,043 Project Grant to Carnegie Mellon University under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The five-year award will support research towards theoretical foundations of neural network-based representation learning. Specifically, the awardee will build a comprehensive theory for new neural network representation learning techniques. This includes characterizing statistical properties of representations and how they are encoded in neural networks. The research has three components: determining when training objectives lead to unique representations; studying representations efficiently learned by deep learning models and their encoding in network weights; and analyzing statistical properties making representations effective for downstream tasks to improve interpretability. Findings will inform practical advancement of deep learning and allow for better human interaction with models in applications such as self-driving vehicles.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $124.3k | 8/17/22 | ||
| Not listed | $120.7k | 1/27/22 |