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 National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) award of $400,000 to the University of California, Berkeley provides funding for a 3-year collaborative research project titled "Principled Approaches to Deep Learning for Low-dimensional Structures". The project aims to develop a unified mathematical framework for deep learning by designing "white-box" deep networks that maximize information gain...
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 two-year, $250,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop techniques for improving the interpretability and robustness of deep neural networks. Specifically, the University of California, Santa Barbara will apply ideas from communication theory and neuroscience to actively shape the features extracted by individual layers of neural networks in addition to end-to-end training. By learning "matched...
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...
This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering program (CFDA 47.070) will provide $597,224 to the University of California, Los Angeles (UCLA) over a 3-year period from October 1, 2024 to September 30, 2027. The project, titled "SMALL: Adaptive Synaptic Dynamics Neural Networks: A Novel Hypothesis for Temporal Computations in Artificial Neural Networks," aims to develop a novel class of feedforward neural...
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 $600,000 project grant awarded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) aims to advance artificial intelligence (AI) capabilities by investigating the mathematical foundations and practical applications of deep learning models. The project, awarded to the University of Wisconsin System's University of Wisconsin-Madison campus, will focus on understanding the properties of neural networks trained on...
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 $299,889 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research at the University of California, San Diego (UCSD) to develop algorithms for compressing and improving the efficiency of large neural networks used in modern artificial intelligence applications. The key products and services to be delivered include: The research project focuses on developing quantization, pruning, and low-rank...