Summary of Foundation Inference Models For Markov Jump Processes, by David Berghaus et al.
Foundation Inference Models for Markov Jump Processes
by David Berghaus, Kostadin Cvejoski, Patrick Seifner, Cesar Ojeda, Ramses J. Sanchez
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 This paper introduces a methodology for zero-shot inference of Markov jump processes (MJPs) from noisy and sparse observations. The approach consists of two components: a broad probability distribution over families of MJPs, observation times, and noise mechanisms, which simulates synthetic data; and a neural network model that processes subsets of the simulated observations to output initial conditions and rate matrices. The model is trained in a supervised way to infer hidden MJPs evolving in state spaces of different dimensionalities. Experimental results demonstrate the effectiveness of this approach for inferring MJPs describing various systems, including Brownian motors, molecular simulations, ion channel data, and protein folding models. The model performs on par with state-of-the-art finetuned models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to figure out rules that govern changes in complex systems without seeing the whole system or knowing what it looks like beforehand. It uses a special type of math called Markov jump processes to study these systems. The researchers developed a way to learn about these processes using only some information we can observe, kind of like solving a puzzle. They tested this method on different types of systems, like how proteins fold and how ions move through channels in cells. Their results show that their approach works well and could be used to study many other complex systems. |
Keywords
» Artificial intelligence » Inference » Neural network » Probability » Supervised » Synthetic data » Zero shot
