Model-Based Machine Learning

What is Model-Based Machine Learning?

Hundreds of learning algorithms have been developed in the field of machine learning. Scientists typically select from among these algorithms to answer specific issues. Their options are frequently restricted by their expertise with these systems. In this classical/traditional machine learning framework, scientists are forced to make some assumptions to employ an existing algorithm.

  • The model-based learning in machine learning is a technique that tries to generate a custom solution for each new challenge

MBML’s purpose is to offer a single development framework that facilitates the building of a diverse variety of custom models. This paradigm evolved as a result of a significant confluence of three main ideas:

  • Factor graphs
  • Bayesian perspective,
  • Probabilistic Programming 

The essential principle is that in the form of a model, all assumptions about the issue domain are made clear. Model-based deep learning is just a collection of assumptions stated in a graphical manner.

Factor Graphs

The usage of PGM- Probabilistic Graphical Models, particularly factor graphs, is the pillar of MBML. A PGM is a graph-based diagrammatic representation of the joint probability distribution across all random variables in a model.

They are a form of PGM with round nodes and square nodes representing variable probability distributions (factors), and vertices expressing conditional relationships between nodes. They offer a broad framework for simulating the combined dispersion of a set of random variables.

In factor graphs, we consider implicit parameters as random variables and discover their probability distributions throughout the network using Bayesian inference techniques. Inference/learning is just the product of factors across a subset of the graph’s variables. This makes it simple to develop local message forwarding algorithms.

Testing. CI/CD. Monitoring.

Because ML systems are more fragile than you think. All based on our open-source core.

Our GithubInstall Open SourceBook a Demo

Bayesian Methods

The first essential concept allowing this new machine learning architecture is Bayesian inference/learning. Latent/hidden parameters are represented in MBML as random variables with probability distributions. This provides for a consistent and rational approach to quantifying uncertainty in model parameters. Again when the observed variables in the model are locked to their values, the Bayes’ theorem is used to update the previously assumed probability distributions.

In contrast, the classical ML framework assigns model parameters to average values derived by maximizing an objective function. Bayesian inference on big models with millions of variables is accomplished similarly, but in a more complicated way, employing the Bayes’ theorem. This is because Bayes’ theory is an accurate inference approach that is intractable when applied to huge datasets. The rise in the processing capacity of computers over the last decade has enabled the research and innovation of algorithms that can scale to enormous data sets.

Probabilistic Programming 

Probabilistic programming (PP) is a breakthrough in computer science in which programming languages are now created to compute with uncertainty in addition to logic. Current programming languages can already handle random variables, variable restrictions, and inference packages. You may now express a model-based reinforcement learning of your problem concisely with a few lines of code using a PP language. So an inference engine is invoked to produce inference procedures to solve the problem automatically.

Model-Based ML Developmental Stages

It consists of three rules-based models in machine learning:

  • Describe the Model: Using factor graphs, describe the process that created the data.
  • Condition on Reported Data: Make the observed variables equal to their known values.
  • Backward reasoning is used to update the prior distribution across the latent constructs or parameters. Estimate the Bayesian probability distributions of latent constructs based on observable variables.