Summary of Robust and Highly Scalable Estimation Of Directional Couplings From Time-shifted Signals, by Louis Rouillard et al.
Robust and highly scalable estimation of directional couplings from time-shifted signals
by Louis Rouillard, Luca Ambrogioni, Demian Wassermann
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 directed couplings between nodes in networks from indirect measurements, tackling the ill-posed problem caused by unknown delays. Using a variational Bayes framework, the authors marginalize uncertainty over delays to obtain conservative estimates. A hybrid-VI scheme is employed to overcome overconfidence issues, combining forward KL loss and gradient-based VI for estimating measurement parameters and couplings respectively. The approach outperforms regression DCM in ground-truth experiments, providing reliable and conservative coupling estimates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how networks work by figuring out how things are connected. Right now, scientists struggle to get accurate connections because they don’t know when some measurements were taken. The researchers came up with a new way to solve this problem using special math tools called variational Bayes. They also created a hybrid system that combines different methods to make their results more reliable. |
Keywords
» Artificial intelligence » Regression
