May 6, 2022
PRECISE Team Wins Best Paper Award at ICCPS 2022
Yahan Yang, Ramneet Kaur, Souradeep Dutta, and Insup Lee won the Best Paper Award at the 13th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS 2022). Below is the title and abstract for their paper.
"Interpretable Detection of Distribution Shifts in Learning Enabled Cyber-Physical Systems"
Deep neural networks have allowed us to use high dimensional real world signals generated from sensors like camera and LiDAR. However, this comes with its potential perils. The pitfalls arise from possible over-fitting, and subsequent unsafe behavior when exposed to unknown environments. In this paper, our proposal is to build good representations for in-distribution data. We introduce the idea of a memory bank to store prototypical samples from the input space. We use these memories to compute probability density estimates using kernel density estimation techniques. We evaluate our technique on two challenging scenarios : a self-driving car setting implemented inside the simulator CARLA with image inputs, and an autonomous racing car navigation setting, with LiDAR inputs. An added benefit of using training samples as memories to detect out-of-distribution inputs is that the system is interpretable to a human operator. Explanation of this nature is generally hard to obtain from pure deep learning based alternatives.
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December 10, 2021
News: Philip Huebner, Elior Sulem, Cynthia Fisher and Dan Roth won Best Paper Award runner up for their paper titled "BabyBERTa: Learning More Grammar With Small-Scale Child-Directed Language" at the Conference on Natural Language Learning (CoNLL’21); this is the top venue for natural language learning that also involves cognitive and developmental considerations.