Summary of Variational Quantization For State Space Models, by Etienne David (ip Paris et al.
Variational quantization for state space models
by Etienne David, Jean Bellot, Sylvain Le Corff
First submitted to arxiv on: 17 Apr 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 proposed forecasting model combines discrete state space hidden Markov models with neural network architectures and training procedures inspired by vector quantized variational autoencoders. The model introduces a variational discrete posterior distribution of the latent states given the observations and a two-stage training procedure to alternatively train the parameters of the latent states and of the emission distributions. This approach allows for exploring large datasets and leveraging available external signals, leading to sharp predictions with statistical guarantees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new forecasting model that combines different techniques to predict future outcomes from large datasets of time series data. The goal is to make accurate predictions by considering various factors and patterns in the data. The approach uses a combination of hidden Markov models and neural networks, which allows it to learn patterns and relationships in the data. The paper shows that this method performs well on several benchmark datasets compared to other state-of-the-art methods. |
Keywords
» Artificial intelligence » Neural network » Time series
