Modeling interactions with Deep Learning
Interacting systems are highly prevalent in many real-world settings, including genomics, proteomics, and images. The dynamics of complex systems are often explained as a composition of entities and their interaction graphs. In this dissertation, we design state-of-the-art deep neural networks for interaction-oriented representation learning. Learning such structure representations from data can provide semantic clarity, ease of reasoning for generating new knowledge, and potentially causal interpretation. We consider three different types of interactions: 1) interactions within a particular input sample, 2) interactions between multiple input samples, and 3) interactions between output labels. For each type of interaction, we design novel models to tackle a real-world problem and validate our results both quantitatively and visually.
- Vicente Ordóñez Román, Committee Chair, (CS/SEAS/UVA)
- Yanjun Qi, advisor, (CS/SEAS/UVA)
- Yangfeng Ji (CS/SEAS/UVA)
- Clint Miller (Public Health Sciences/SOM/UVA)
- Casey Greene (Biochemistry & Molecular Genetics/SOM/University of Colorado)