Search Results for author:
Found 5 papers, 2 papers with code
A Better Match for Drivers and Riders: Reinforcement Learning at Lyft
This change was the first documented implementation of a ridesharing matching algorithm that can learn and improve in real time.
A Post-Processing Tool and Feasibility Study for Three-Dimensional Imaging with Electrical Impedance Tomography During Deep Brain Stimulation Surgery
This paper investigates the application of EIT during deep brain stimulation (DBS) surgery as a means to identify targets during operations.
A Feasibility Study on Real-Time High Resolution Imaging of the Brain Using Electrical Impedance Tomography
Conclusion: High quality reconstruction from phantom data validates the efficiency of the novel reconstruction method.
The Price of Interpretability
When quantitative models are used to support decision-making on complex and important topics, understanding a model's ``reasoning'' can increase trust in its predictions, expose hidden biases, or reduce vulnerability to adversarial attacks.
Optimal Explanations of Linear Models
We propose a general optimization framework to create explanations for linear models.
