Summary of Recursive Learning Of Asymptotic Variational Objectives, by Alessandro Mastrototaro et al.
Recursive Learning of Asymptotic Variational Objectives
by Alessandro Mastrototaro, Mathias Müller, Jimmy Olsson
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Computation (stat.CO)
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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 proposes an innovative approach to enable online variational inference (VI) in general state-space models (SSMs), particularly suitable for sequential time-series data. The proposed method, called online sequential IWAE (OSIWAE), leverages stochastic approximation to maximize the asymptotic contrast function, allowing for real-time learning of model parameters and latent states. This is achieved by approximating filter state posteriors and their derivatives using sequential Monte Carlo (SMC) methods, creating a particle-based framework for online VI in SSMs. The paper provides theoretical results on the learning objective and demonstrates the efficiency of OSIWAE through numerical experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to use special models called general state-space models (SSMs) for real-time analysis of data that comes in a sequence over time. These models are very useful for tasks like predicting what might happen next or understanding patterns in data. The researchers created a new way to update these models as they receive more data, allowing them to learn and improve in real-time. This is important because it can be used for many applications such as monitoring weather patterns or tracking the spread of diseases. |
Keywords
» Artificial intelligence » Inference » Time series » Tracking
