1 code implementation • 24 Jun 2021 • Xianlong Zeng, Fanghao Song, Zhongen Li, Krerkkiat Chusap, Chang Liu
Our method can be divided into three stages: 1) a neighborhood generation stage, which generates instances based on the given sample; 2) a classification stage, which yields classifications on the generated instances to carve out the local decision boundary and delineate the model behavior; and 3) a human-in-the-loop stage, which involves human to refine and explore the neighborhood of interest.
BIG-bench Machine Learning Explainable artificial intelligence +1
no code implementations • 6 Mar 2021 • Jeremy Beauchamp, Razvan Bunescu, Cindy Marling, Zhongen Li, Chang Liu
In this work, we invert the "what-if" scenario and introduce a similar architecture based on chaining two LSTMs that can be trained to make either insulin or carbohydrate recommendations aimed at reaching a desired BG level in the future.