mlsanityData to 7 Oct 2026

A Multiagent Framework for the Asynchronous and Collaborative Extension of Multitask ML Systems

arXiv 2209.14745 ↗First posted 29 Sep 2022cs.LG cs.AI cs.CVcited by 2

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Abstract (arXiv)

The traditional ML development methodology does not enable a large number of contributors, each with distinct objectives, to work collectively on the creation and extension of a shared intelligent system. Enabling such a collaborative methodology can accelerate the rate of innovation, increase ML technologies accessibility and enable the emergence of novel capabilities. We believe that this novel methodology for ML development can be demonstrated through a modularized representation of ML models and the definition of novel abstractions allowing to implement and execute diverse methods for the asynchronous use and extension of modular intelligent systems. We present a multiagent framework for the collaborative and asynchronous extension of dynamic large-scale multitask systems.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 62 of its 111 references that are in the map. First posted on arXiv 29 Sep 2022.

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62 of 111 references.

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Citing papers per year: 2023: 2.

2 papers.

  1. 2023Modular Deep Learningcited by 82
  2. 2023Multipath agents for modular multitask ML systemscited by 1