Project Grant 2134133
Award Date
11/1/21
Completion Date
10/31/24
Dollars Obligated
$450K
Funding Federal Agency
National Science Foundation
Awarding Federal Agency
Division of Mathematical Sciences
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Pittsburgh, PA 15213, USA
Similar Awards
The National Science Foundation awarded a $225,000 Project Grant to Texas A&M University under the Mathematical and Physical Sciences program (CFDA 47.049) for the period of November 1, 2021 through October 31, 2024. The grant funds collaborative research on new perspectives for deep learning by bridging approximation, statistical, and algorithmic theories. The Mathematical and Physical Sciences program aims to advance scientific knowledge and understanding in core areas of mathematics and...
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...
The National Science Foundation Division of Computing and Communication Foundations awarded a $300,000 Project Grant to Columbia University for research titled "Collaborative Research: Foundations of Deep Learning: Theory, Robustness, and the Brain" under the Mathematical and Physical Sciences program (CFDA 47.049). The three-year award running from December 2021 through November 2024 will support research into the theoretical foundations of deep learning techniques with a focus on...
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...
The University of California, Santa Barbara was awarded a $299,966 project grant from the National Science Foundation Division of Computing and Communication Foundations. The grant is part of the NSF's Mathematical and Physical Sciences program (CFDA 47.049) and will support research titled "COLLABORATIVE RESEARCH: SCALE MODL: ADAPTIVITY OF DEEP NEURAL NETWORKS" from October 1, 2021 through September 30, 2024. The grant funding will enable collaborative research exploring the...
The University of California Santa Cruz will receive $458,486 under a two-year Project Grant from the National Science Foundation Division of Computing and Communication Foundations. The grant will fund research titled "Collaborative Research: Algebraic Framework of Compositional Functions for New Structure, Training, and Explainability of Deep Learning" from January 1, 2022 to December 31, 2024. The research aims to develop new techniques for deep learning model structure, training...
This Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) will support a collaborative research project between mathematicians and computer scientists at Carnegie Mellon University (CMU). The $418,035 award, effective October 1, 2024 through September 30, 2027, will explore new ways to solve complex mathematical problems by integrating advanced machine learning techniques with automated reasoning. The project will focus...
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 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...
The National Science Foundation (NSF) awarded a $599,410 Project Grant to Carnegie Mellon University (CMU) to develop a collaborative learning framework for dynamic and diverse computing environments, particularly focused on edge devices. The grant, under NSF's Computer and Information Science and Engineering program (CFDA 47.070), aims to innovate model-parallel collaborative learning by designing unique model architectures and efficient algorithms, facilitate practical on-device training and...