Name
Abstract | ||
|---|---|---|
Deep learning excels at learning low-level task information from large amounts of data, but struggles with learning high-level domain knowledge, which can often be directly and succinctly expressed. In this work, we introduce Pylon, a neuro-symbolic training framework that builds on PyTorch to augment procedurally trained neural networks with declaratively specified knowledge. Pylon allows users to programmatically specify constraints as PyTorch functions, and compiles them into a differentiable loss, thus training predictive models that fit the data whilst satisfying the specified constraints. Pylon includes both exact as well as approximate compilers to efficiently compute the loss, employing fuzzy logic, sampling methods, and circuits, ensuring scalability even to complex models and constraints. A guiding principle in designing Pylon has been the ease with which any existing deep learning codebase can be extended to learn from constraints using only a few lines: a function expressing the constraint and a single line of code to compile it into a loss. We include case studies from natural language processing, computer vision, logical games, and knowledge graphs, that can be interactively trained, and highlights Pylon{’}s usage. |
Year | Venue | DocType |
|---|---|---|
2021 | Annual Conference on Neural Information Processing Systems | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kareem Ahmed | 1 | 0 | 0.68 |
| Tao Li | 2 | 11 | 6.27 |
| Thy Ton | 3 | 0 | 0.34 |
| Quan Guo | 4 | 0 | 0.68 |
| Kai-Wei Chang | 5 | 4735 | 276.81 |
| Parisa Kordjamshidi | 6 | 0 | 0.34 |
| Vivek Srikumar | 7 | 511 | 38.58 |
| Guy Van den Broeck | 8 | 0 | 0.34 |
| Sameer Singh | 9 | 1060 | 71.63 |