226 citations · 272 across the 15 of their papers we have counts for
6 papers · 1 filter
Drag-guided diffusion models for vehicle image generation
Nikos Arechiga, Frank Permenter, Binyang Song +1
Denoising diffusion models trained at web-scale have revolutionized image generation. The application of these tools to engineering design is an intriguing possibility, but is curr…
Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderings
Binyang Song, Chenyang Yuan, Frank Permenter +2
Generative AI models have made significant progress in automating the creation of 3D shapes, which has the potential to transform car design. In engineering design and optimization…
Accelerating Understanding of Scientific Experiments with End to End Symbolic Regression
Nikos Arechiga, Francine Chen, Yan-Ying Chen +4
We consider the problem of learning free-form symbolic expressions from raw data, such as that produced by an experiment in any scientific domain. Accurate and interpretable models…
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization
Kaidi Cao, Yining Chen, Junwei Lu +3
Real-world large-scale datasets are heteroskedastic and imbalanced -- labels have varying levels of uncertainty and label distributions are long-tailed. Heteroskedasticity and imba…
Better AI through Logical Scaffolding
Nikos Arechiga, Jonathan DeCastro, Soonho Kong +1
We describe the concept of logical scaffolds, which can be used to improve the quality of software that relies on AI components. We explain how some of the existing ideas on runtim…
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
Kaidi Cao, Colin Wei, Adrien Gaidon +2
Deep learning algorithms can fare poorly when the training dataset suffers from heavy class-imbalance but the testing criterion requires good generalization on less frequent classe…