Project Grant 2413864

Award Date 7/1/24
Completion Date 6/30/27
Dollars Obligated $225K
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Stanford, CA 94305, USA
Similar Awards
The National Science Foundation Division of Computing and Communication Foundations awarded a $500,000 Project Grant to Princeton University from January 1, 2022 to December 31, 2024 to support collaborative research on probabilistic, geometric, and topological analysis of neural networks. This award falls under the Mathematical and Physical Sciences program (CFDA 47.049), which aims to strengthen the Nation's scientific enterprise through advancing knowledge and understanding of major...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $331,902 Project Grant to the Trustees of Boston University on August 15, 2023 under the Mathematical and Physical Sciences program (CFDA 47.049). The purpose of this 3-year grant is to develop rigorous mathematical analysis and theory for the training algorithms used in neural network models across various machine learning applications. The research will leverage stochastic analysis and weak convergence theory...
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...
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 Massachusetts Institute of Technology (MIT) received a $108,000 Project Grant award from the National Science Foundation Division of Computing and Communication Foundations to support collaboration research on probabilistic, geometric, and topological analysis of neural networks from theory to applications. The two-year award, issued on January 1, 2022 and set to conclude on December 31, 2024, will fund research under the Mathematical and Physical Sciences program (CFDA #47.049). This...
This three-year, $500,000 project grant from the National Science Foundation's Division of Computing and Communication Foundations, under the Computer and Information Science and Engineering program (CFDA 47.070), will support research to advance neural network verification techniques. The grantee, Stanford University, will partner with the Hebrew University of Jerusalem to pursue three goals: developing more scalable verification methods using abstraction and compositional reasoning;...
The National Science Foundation awarded a $400,000 Project Grant to Stanford University under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The three-year award will support research and education activities to develop conceptual and mathematical understanding of large language models. Specifically, the university will conduct research analyzing simplified generative text models and language models trained on such data to gain insights into their...
The National Science Foundation awarded a $218,748 Project Grant to the University of California, Los Angeles under the Mathematical and Physical Sciences program (CFDA 47.049). The grant will support research from June 2022 to May 2027 titled "CAREER: NEURAL NETWORKS IN THE PRACTICAL REGIME." As the award description indicates, the NSF Mathematical and Physical Sciences program aims to strengthen the nation's scientific enterprise by increasing knowledge and understanding of major...
The Trustees of the University of Pennsylvania received a $275,000 project grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) to conduct research on geometrization approaches toward understanding deep learning. Specifically, the three-year award funds research projects analyzing symmetries in trained deep neural networks, examining dynamics of deep learning training, and investigating how deep learning separates data across neural network...
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...

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 models, as well as investigating the potential for neural networks to be seen as algorithm approximators. This work may shed light on recent empirical phenomena observed in neural networks, including the behavior of transformers and large language models. The award includes support and mentoring for graduate students to advance this fundamental research in neural network theory, which has the potential for significant impacts across a vast range of applications.

Generated 4/8/25, 2:56 AM