Summary of Automatic Gradient Estimation For Calibrating Crowd Models with Discrete Decision Making, by Philipp Andelfinger et al.
Automatic Gradient Estimation for Calibrating Crowd Models with Discrete Decision Making
by Philipp Andelfinger, Justin N. Kreikemeyer
First submitted to arxiv on: 6 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to estimating gradients in stochastic programs with discrete jumps, enabling gradient descent over complex optimization problems. This is particularly relevant to real-world applications, where gradient descent has been shown to be effective but limited by automatic differentiation (AD) alone. The proposed estimator is applied to the calibration of force-based crowd evacuation models based on the Social Force model, which involves both continuous and discrete decision-making processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new way to estimate gradients in complex optimization problems that involve both continuous and discrete jumps. The authors show how this can be used to calibrate real-world applications, like modeling how people move in crowds. They use an estimator to help gradient descent find the best solution faster and more accurately. |
Keywords
* Artificial intelligence * Gradient descent * Optimization
