90 citations · 136 across the 5 of their papers we have counts for
5 papers
Automating Style Analysis and Visualization With Explainable AI -- Case Studies on Brand Recognition
Yu-hsuan Chen, Levent Burak Kara, Jonathan Cagan
Incorporating style-related objectives into shape design has been centrally important to maximize product appeal. However, stylistic features such as aesthetics and semantic attrib…
Learning to design without prior data: Discovering generalizable design strategies using deep learning and tree search
Ayush Raina, Jonathan Cagan, Christopher McComb
Building an AI agent that can design on its own has been a goal since the 1980s. Recently, deep learning has shown the ability to learn from large-scale data, enabling significant…
Design Strategy Network: A deep hierarchical framework to represent generative design strategies in complex action spaces
Ayush Raina, Jonathan Cagan, Christopher McComb
Generative design problems often encompass complex action spaces that may be divergent over time, contain state-dependent constraints, or involve hybrid (discrete and continuous) d…
Goal-Directed Design Agents: Integrating Visual Imitation with One-Step Lookahead Optimization for Generative Design
Ayush Raina, Lucas Puentes, Jonathan Cagan +1
Engineering design problems often involve large state and action spaces along with highly sparse rewards. Since an exhaustive search of those spaces is not feasible, humans utilize…
Learning to design from humans: Imitating human designers through deep learning
Ayush Raina, Christopher McComb, Jonathan Cagan
Humans as designers have quite versatile problem-solving strategies. Computer agents on the other hand can access large scale computational resources to solve certain design proble…