THEORINET: Transferable, Hierarchical, Expressive, Optimal, Robust, and Interpretable NETworks
Transferable, Hierarchical, Expressive, Optimal, Robust, and Interpretable NETworks (THEORINET) is an NSF-Simons Research Collaboration on the Mathematical and Scientific Foundations of Deep Learning (MoDL) whose goal is to develop a mathematical, statistical and computational framework that helps explain the success of current network architectures, understand its pitfalls, and guide the design of novel architectures with guaranteed confidence, robustness, interpretability, optimality, and transferability. THEORINET will also create new undergraduate and graduate programs in the foundations of data science and organize a series of collaborative research events, including semester research programs and summer schools on the foundations of deep learning.
