1 code implementation • 4 Apr 2024 • Tyler Chang, Andrew Gillette, Romit Maulik
In this work, we present a best-of-both-worlds approach to verifiable scientific machine learning by demonstrating that (1) multiple standard interpolation techniques have informative error bounds that can be computed or estimated efficiently; (2) comparative performance among distinct interpolants can aid in validation goals; (3) deploying interpolation methods on latent spaces generated by deep learning techniques enables some interpretability for black-box models.
no code implementations • 26 Sep 2023 • Romain Egele, Tyler Chang, Yixuan Sun, Venkatram Vishwanath, Prasanna Balaprakash
Machine learning (ML) methods offer a wide range of configurable hyperparameters that have a significant influence on their performance.
1 code implementation • 4 Sep 2022 • Sean Trott, Cameron Jones, Tyler Chang, James Michaelov, Benjamin Bergen
Humans can attribute beliefs to others.
9 code implementations • ICCV 2021 • Weijian Xu, Yifan Xu, Tyler Chang, Zhuowen Tu
In this paper, we present Co-scale conv-attentional image Transformers (CoaT), a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms.