Summary of Towards Learning Stochastic Population Models by Gradient Descent, By Justin N. Kreikemeyer et al.
Towards Learning Stochastic Population Models by Gradient Descent
by Justin N. Kreikemeyer, Philipp Andelfinger, Adelinde M. Uhrmacher
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to learning mechanistic models from data is presented in this paper, focusing on both parameter estimation and model structure discovery. The authors explore simulation-based optimization methods that offer greater flexibility in objective formulation and weaker data requirements. They demonstrate the application of local stochastic gradient descent, a popular machine learning method, to estimate models accurately. However, they find that enforcing parsimonious and interpretable models significantly increases the difficulty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to learn mechanistic models from data by finding the right model structure and estimating its parameters. It uses special computer simulations to test different optimization methods. The authors show how one common machine learning method can be used to estimate models accurately, but they also find that making sure the models are simple and easy to understand makes it much harder. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Stochastic gradient descent
