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Sequential Estimation of Gaussian Process-based Deep State-Space Models


Core Concepts
Proposing a method for sequential estimation of unknowns in state-space and deep state-space models using Gaussian processes.
Abstract
The content discusses the application of Gaussian processes in estimating functions and latent processes in state-space models. It introduces a method based on particle filtering for estimation, along with ensemble learning to reduce variances. Various experiments are conducted to validate the proposed approach. Introduction Machine learning advancements driven by deep neural networks. Importance of Gaussian processes in solving complex problems. Background Overview of Gaussian Processes and Random Feature-Based Gaussian Processes. Introduction to Bayesian Linear Regression. Particle Filtering Explanation of particle filtering theory for sequential estimation in nonlinear models. Gaussian Process State Space Model Description of GP-based state space model and its inference using particle filtering. Ensemble Learning Utilizing ensembles of GPs for improved accuracy and robustness in forecasting time series data. Experiments Testing the proposed method with different scenarios to evaluate performance and accuracy.
Stats
"We tested the performance of the proposed method with several experiments." "The generated data set contained 2,000 samples with σ2u = σ2v = 0.001." "For drawing random vectors needed in the construction of the random features ϕ(x) in (4), we used Jx = Jy = 50."
Quotes
"We consider the problem of sequential estimation of the unknowns of state-space and deep state-space models that include estimation of functions and latent processes." "The reason for this is that they provide a principled, practical, and probabilistic approach to learning."

Deeper Inquiries

What are some potential limitations or challenges when applying ensemble learning methods

Ensemble learning methods can face limitations or challenges such as: Overfitting: If the individual models in the ensemble are too complex or if there is high correlation between them, it may lead to overfitting. Computational Resources: Running multiple models simultaneously can be computationally expensive and time-consuming. Model Selection: Choosing the right combination of models for the ensemble and determining their weights can be a challenging task. Diversity: Ensuring diversity among the individual models in terms of algorithms, features, or hyperparameters is crucial for an effective ensemble.

How does the computational complexity vary between using Gaussian distributions versus Student's t-distributions

The computational complexity varies between using Gaussian distributions and Student's t-distributions due to different factors: For Gaussian distributions: The computation involves calculating mean and variance updates which are relatively straightforward but might require matrix operations that could increase complexity with larger datasets. For Student's t-distributions: These distributions account for uncertainty in variances and have closed-form solutions for updating parameters, making them more suitable when dealing with uncertain data points. However, they might involve heavier computations due to additional calculations involved in handling degrees of freedom.

How can deep structures enhance modeling capacity compared to traditional machine learning methods

Deep structures enhance modeling capacity compared to traditional machine learning methods by: Hierarchical Representation Learning: Deep structures allow for hierarchical representation learning where each layer captures different levels of abstraction from data. Increased Non-linearity: Deeper architectures enable capturing complex non-linear relationships within data compared to shallow models. Feature Abstraction: Deep networks automatically learn relevant features from raw data without manual feature engineering, leading to better performance on tasks like image recognition or natural language processing. Improved Generalization: Deep structures help in generalizing well on unseen data by extracting intricate patterns during training. These advantages make deep structures highly effective in handling large-scale datasets and complex problems across various domains like computer vision, speech recognition, and natural language understanding.
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