Recently Published by Autodesk Researchers
Autodesk Research teams regularly contribute to peer-reviewed scientific journals and present at conferences around the world. Check out some recent publications from Autodesk Researchers.
Interpreting the learned features of vision models has posed a longstanding challenge in the field of machine learning. To address this issue, the team introduced TExplain, a novel method that trains a neural network to establish a connection between the feature space of image classifiers and LLMs. TExplain projects learned visual representations of a frozen image classifier onto a space that an independently trained language model can interpret. Using a large number of generated sentence samples along with the visual representation, TExplain produces a word cloud for each visual representation.
As mechanical systems become more complex and technological advances accelerate, the traditional reliance on heritage designs for engineering endeavors is being diminished in its effectiveness. Considering the dynamic nature of the design industry where new challenges are continually emerging, alternative sources of knowledge need to be sought to guide future design efforts. One promising avenue lies in the analysis of design optimization data, which has the potential to offer valuable insights and overcome the limitations of heritage designs. This paper presents a step toward extracting knowledge from optimization data in multi-split fluid-based thermal management systems using different classification machine learning methods, so that designers can use it to guide decisions in future design efforts.
While visualizations have been used to support general architectural design exploration, existing computational solutions treat building codes as separate from, rather than part of, the design process, creating challenges for architects. Through a series of participatory design studies with professional architects, the team found that interactive visualizations have promising potential to aid design exploration and sensemaking at early stages of architectural design by providing feedback about potential allowances and consequences of design decisions. However, implementing a visualization system necessitates addressing the complexity and ambiguity inherent in building codes.
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