mlsanityData to 7 Oct 2026

Team Deep Mixture of Experts for Distributed Power Control

arXiv 2007.14147 ↗First posted 28 Jul 2020cs.IT cs.LG math.ITcited by 0

On the map: Mixture-of-experts → Vision mixture-of-experts

Abstract (arXiv)

In the context of wireless networking, it was recently shown that multiple DNNs can be jointly trained to offer a desired collaborative behaviour capable of coping with a broad range of sensing uncertainties. In particular, it was established that DNNs can be used to derive policies that are robust with respect to the information noise statistic affecting the local information (e.g. CSI in a wireless network) used by each agent (e.g. transmitter) to make its decision. While promising, a major challenge in the implementation of such method is that information noise statistics may differ from agent to agent and, more importantly, that such statistics may not be available at the time of training or may evolve over time, making burdensome retraining necessary. This situation makes it desirable to devise a "universal" machine learning model, which can be trained once for all so as to allow for decentralized cooperation in any future feedback noise environment. With this goal in mind, we propose an architecture inspired from the well-known Mixture of Experts (MoE) model, which was previously used for non-linear regression and classification tasks in various contexts, such as computer vision and speech recognition. We consider the decentralized power control problem as an example to showcase the validity of the proposed model and to compare it against other power control algorithms. We show the ability of the so called Team-DMoE model to efficiently track time-varying statistical scenarios.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Vision mixture-of-experts, one of 12 directions in Mixture-of-experts. Builds on 6 of its 18 references that are in the map. First posted on arXiv 28 Jul 2020.

Builds on

6 of 18 references.

References · 6

  1. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  2. 2013Learning Factored Representations in a Deep Mixture of Expertscited by 275
  3. 2020From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systemscited by 18
  4. 2019Deep Learning for Distributed Optimization: Applications to Wireless Resource Managementcited by 14
  5. 2019Meta-Learning to Communicate: Fast End-to-End Training for Fading Channelscited by 13
  6. 2017Speech Enhancement using a Deep Mixture of Expertscited by 4

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  8. 2024HoME: Hierarchy of Multi-Gate Experts for Multi-Task Learning at Kuaishou