no code implementations • CVPR 2023 • Andrey Zhmoginov, Mark Sandler, Nolan Miller, Gus Kristiansen, Max Vladymyrov
We study the effects of data and model architecture heterogeneity and the impact of the underlying communication graph topology on learning efficiency and show that our agents can significantly improve their performance compared to learning in isolation.
no code implementations • 8 Nov 2017 • Gus Kristiansen, Xavi Gonzalvo
We present E NERGY N ET , a new framework for analyzing and building artificial neural network architectures.
