AutoML
Minimum Description Length
Minimum Description Length provides a criterion for the selection of models, regardless of their complexity, without the restrictive assumption that the data form a sample from a 'true' distribution.
Extracted from scholarpedia
Source:
Paper: J. Rissanen (1978) Modeling by the shortest data description. Automatica 14, 465-471
Book: P. D. Grünwald (2007) The Minimum Description Length Principle, MIT Press, June 2007, 570 pages
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Multi-Task Learning | 5 | 5.49% |
| Multimodal Deep Learning | 4 | 4.40% |
| Active Learning | 3 | 3.30% |
| Image Classification | 3 | 3.30% |
| Reinforcement Learning (RL) | 3 | 3.30% |
| Subgroup Discovery | 3 | 3.30% |
| General Classification | 3 | 3.30% |
| Domain Adaptation | 3 | 3.30% |
| Recommendation Systems | 2 | 2.20% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
