no code implementations • 1 Nov 2023 • Connor Mclaughlin, Jason Matterer, Michael Yee
While deep learning models have seen widespread success in controlled environments, there are still barriers to their adoption in open-world settings.
no code implementations • 24 Feb 2022 • Ryan Soklaski, Michael Yee, Theodoros Tsiligkaridis
Diverse data augmentation strategies are a natural approach to improving robustness in computer vision models against unforeseen shifts in data distribution.
no code implementations • 14 Jan 2022 • Ryan Soklaski, Justin Goodwin, Olivia Brown, Michael Yee, Jason Matterer
Responsible Artificial Intelligence (AI) - the practice of developing, evaluating, and maintaining accurate AI systems that also exhibit essential properties such as robustness and explainability - represents a multifaceted challenge that often stretches standard machine learning tooling, frameworks, and testing methods beyond their limits.
no code implementations • 18 Dec 2021 • Naveen Raman, Michael Yee
We work to improve learning-to-defer algorithms when paired with specific individuals by incorporating two fine-tuning algorithms and testing their efficacy using both synthetic and image datasets.