Embedding Symbolic Temporal Knowledge into Deep Sequential Models
We embed symbolic knowledge expressed as linear temporal logic (LTL) and use these embeddings to guide the training of deep sequential models.
Yaqi Xie
I love Machine Learning!
Enhancing Deep Learning with Symbolic Knowledge
Bridging the gap between symbolic and connectionist paradigms via Graph Neural Network embeddings
Yaqi wins the 2020 Research Achievement Award.
Yaqi Xie is awarded the Research Achievement Award for his NeurIPS 2019 paper on Embedding symbolic knowledge into deep networks.
Embedding Symbolic Knowledge into Deep Networks
Leveraging prior symbolic knowledge to improve the performance of deep models.
Robot Capability and Intention in Trust-based Decisions across Tasks
We present results from a human-subject study designed to explore two facets of human mental models of robots - inferred capability and intention - and their relationship to overall trust and eventual decisions.