Summary of Alternators For Sequence Modeling, by Mohammad Reza Rezaei and Adji Bousso Dieng
Alternators For Sequence Modeling
by Mohammad Reza Rezaei, Adji Bousso Dieng
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Atmospheric and Oceanic Physics (physics.ao-ph); Neurons and Cognition (q-bio.NC)
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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 a new family of non-Markovian dynamical models for sequences called alternators. An alternator consists of two neural networks: the observation trajectory network (OTN) and the feature trajectory network (FTN). These networks work together, alternating between outputting samples in the observation space and some feature space over a cycle. The parameters of these networks are not time-dependent and are learned via a minimum cross-entropy criterion over the trajectories. Alternators can be used as dynamical latent-variable generative models or sequence-to-sequence predictors. They can uncover the underlying dynamics, forecast and impute missing data, and sample new trajectories. The paper demonstrates the capabilities of alternators in three applications: modeling chaotic behavior using the Lorenz equations, mapping brain activity to physical activity in Neuroscience, and forecasting sea-surface temperature in Climate Science. Alternators are found to be stable, fast, and high-quality generators, often outperforming strong baselines such as Mambas, neural ODEs, and diffusion models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new type of model called alternators that can help us understand complex sequences of data. It’s like having a special tool to uncover the underlying patterns in things like weather forecasts or brain activity. The model uses two types of networks that work together to generate new samples and make predictions about what might happen next. The authors tested this model on three different tasks, including predicting chaotic behavior and forecasting sea-surface temperature. They found that their model was good at making accurate predictions and generating high-quality results. |
Keywords
» Artificial intelligence » Cross entropy » Temperature
