Last year, we introduced LunchBoxML - a machine learning (ML) plugin for Grasshopper and Dynamo that uses the Accord framework. LunchBoxML introduces several generalized supervised and unsupervised learning tools to visual programming including regression analysis, neural networks, and mixture models. We have published a few new examples to the Bitbucket repository to demonstrate the application … Continue reading New Machine Learning Examples with LunchBoxML
In addition to presenting new peer-reviewed research at this year's conference, I am excited to announce that Proving Ground will be a bronze sponsor for the ACADIA 2017 conference hosted by MIT! It is no secret we believe in contributing to the research world and strive to provide rich research experiences through our internships and … Continue reading We’re a Bronze Sponsor of ACADIA 2017!
I am happy to announce that our paper "A Novel Mesh-based Workflow for Complex Geometry in BIM" has been accepted to ACADIA's 2017 conference: "Disciplines and Disruptions." hosted by MIT! The paper, co-authored by Dave Stasiuk and I, will be included in the peer-reviewed research proceedings. The research builds upon our recent work in utilizing … Continue reading We’re Presenting New Research at ACADIA!
Earlier this summer, I previewed some of our research on machine learning which focused on potential applications for the building industry. I am now pleased to announce that these explorations have been released as a new extension to our LunchBox tools for Grasshopper: LunchBoxML. LunchBoxML exposes new open source components built on a popular machine … Continue reading Machine Learning with LunchBoxML