Relational Structure Discovery for Deep Learning
Graph structure is ubiquitous: from physical relationships to biological interactions to social networks, and many more spread across the universe. Not only is the world around us rich in relational structure, but our mental model of the world is also structured: we think, reason, and communicate in terms of entities and their relations. Such a graph-structured real world calls for artificial intelligence methods that think like humans and hence employ this structure for decision making. Realizing such a framework requires known structure/graph and models that can ingest these non-linear graphical inputs. In cases of a latent unknown graph structure, state-of-the-art deep learning models either focus on task-agnostic statistical dependency learning or diverge from explicit feature dependencies during prediction. We bridge this gap and introduce methods for jointly learning and incorporating graph-based relational knowledge into state-of-the-art deep learning models to help improve (1) predictions, (2) interpretability, (3) post-hoc interpretations, and (4) test datasets selection. Specifically, we contribute methods that enable learning graphical relationships from data without such a ground truth graph. Furthermore, we introduce plug-and-play methods that bias deep learning models to include the learned graph explicitly for improving the aforementioned downstream tasks. We demonstrate our methods’ capabilities on simulated, tabular, NLP, and vision tasks.
- Yanjun Qi (Advisor)(CS/SEAS/UVA)
- Matthew Dwyer (CS/SEAS/UVA)
- Yangfeng Ji (CS/SEAS/UVA)
- Vicente Ordonez (CS/SEAS/UVA)
- Jianhui Zhou (Department of Statistics/UVA)