How deeply does Machine Learning Algorithms in Depth cover message passing?

Defines message passing as dependence capture between graph nodes but stops short of update equations, derivation, or implementation.

Message passing

As explained in Machine Learning Algorithms in Depth

Message passing is a graph-computation technique in which nodes exchange information with neighboring nodes to update their representations.

Message passing is a graph-computation technique in which neighboring nodes exchange information so graph neural networks can update node representations and capture dependence relationships. section 11.5 section 11.7

Message passing supports graph-based learning tasks involving relational structure:

Modeling social networks, where relationships between nodes carry predictive information. section 11.5
Predicting protein structure, where graph representations encode relationships among components. section 11.5
Building graph neural network variants such as graph convolutional, graph attention, and graph recurrent networks. section 11.5 section 11.7

How Machine Learning Algorithms in Depth covers it

Cover of Machine Learning Algorithms in Depth

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Also explained in

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Graph Neural Networks in ActionMessage passing is a graph neural network technique in which node representations are updated by aggregating information from neighboring nodes.
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Optimization Algorithms: AI techniques for design, planning, and control problemsMessage passing is a graph-learning technique in which nodes exchange and aggregate information with neighboring nodes to update representations.
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Learn Concurrent Programming with GoMessage passing is a communication approach in which concurrent executions exchange information by sending and receiving messages.
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Elixir in Action, Third EditionMessage passing is interprocess communication in which processes exchange data through runtime messages rather than shared memory.
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Grokking ConcurrencyMessage passing is an interprocess communication technique in which execution contexts exchange data through discrete messages rather than directly sharing memory.
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Knowledge Graphs and LLMs in ActionMessage passing is a graph-neural-network technique in which nodes iteratively exchange, aggregate, and use information from neighboring nodes to update their representations.
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C++ Concurrency in Action, Second EditionMessage passing is a communication technique in which concurrent components exchange information by sending and receiving messages.
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Sutskever's ListMessage passing is an iterative graph-computation technique in which nodes send information to neighboring nodes and update their representations.

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Related concepts

shared memoryalternative to · Shared memory is a memory region or storage area accessible to multiple processes, threads, or other execution components for accessing or exchanging data.
actorused by · An actor is an independent entity or participant that performs actions, maintains or pursues state or goals, and interacts with other entities or systems.
distributed systemused by · A distributed system is a computing system whose components run across multiple networked processes or machines and coordinate through communication.
graph neural networkused by · A graph neural network is a neural-network model that learns representations or predictions from graph-structured data by combining information from connected entities.
embeddingproduces · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
communicating sequential processused by · Communicating Sequential Processes is a formal concurrency model in which independent sequential processes communicate through channels by exchanging messages.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning Algorithms in Depth. The section links point to the parts of the book it draws on.