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Diffusion strategies for adaptation and learning over networks: an examination of distributed strategies and network behavior

Related publications (34)

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Distributed Meta-Learning with Networked Agents

Ali H. Sayed, Mert Kayaalp, Stefan Vlaski

Meta-learning aims to improve efficiency of learning new tasks by exploiting the inductive biases obtained from related tasks. Previous works consider centralized or federated architectures that rely on central processors, whereas, in this paper, we propos ...

EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP2021

Robust Distributed Learning

El Mahdi El Mhamdi

Whether it occurs in artificial or biological substrates, {\it learning} is a {distributed} phenomenon in at least two aspects. First, meaningful data and experiences are rarely found in one location, hence {\it learners} have a strong incentive to work t ...

EPFL2020

Learning Approach to Delineation of Curvilinear Structures in 2D and 3D Images

Agata Justyna Mosinska

Detection of curvilinear structures has long been of interest due to its wide range of applications. Large amounts of imaging data could be readily used in many fields, but it is practically not possible to analyze them manually. Hence, the need for automa ...

EPFL2019

Automated computer-based detection of encounter behaviours in groups of honeybees

Alessandro Crespi, Laurent Keller

Honeybees form societies in which thousands of members integrate their behaviours to act as a single functional unit. We have little knowledge on how the collaborative features are regulated by workers' activities because we lack methods that enable collec ...

Springer Science and Business Media LLC2017

Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning

Rachid Guerraoui, El Mahdi El Mhamdi, Alexandre David Olivier Maurer, Hadrien Hendrikx

In reinforcement learning, agents learn by performing actions and observing their outcomes. Sometimes, it is desirable for a human operator to \textit{interrupt} an agent in order to prevent dangerous situations from happening. Yet, as part of their learni ...

EPFL2017

On the Learning Behavior of Adaptive Networks - Part II: Performance Analysis

Ali H. Sayed

Part I of this paper examined the mean-square stability and convergence of the learning process of distributed strategies over graphs. The results identified conditions on the network topology, utilities, and data in order to ensure stability; the results ...

IEEE2015

On the Learning Behavior of Adaptive Networks - Part I: Transient Analysis

Ali H. Sayed

This paper carries out a detailed transient analysis of the learning behavior of multiagent networks, and reveals interesting results about the learning abilities of distributed strategies. Among other results, the analysis reveals how combination policies ...

2015

Mechanosensory interactions drive collective behaviour in Drosophila

Dario Floreano, Pavan P Ramdya, Pawel Lichocki, Lukas Frisch, Winnie Wing Yee Tse

Collective behaviour enhances environmental sensing and decision-making in groups of animals. Experimental and theoretical investigations of schooling fish, flocking birds and human crowds have demonstrated that simple interactions between individuals can ...

2015

Adaptation, Learning, and Optimization over Networks

Ali H. Sayed

This work deals with the topic of information processing over graphs. The presentation is largely self-contained and covers results that relate to the analysis and design of multi-agent networks for the distributed solution of optimization, adaptation, and ...

NOW Publishers2014

Diffusion adaptation over networks

Ali H. Sayed

Adaptive networks are well-suited to perform decentralized information processing and optimization tasks and to model various types of self-organized and complex behavior encountered in nature. Adaptive networks consist of a collection of agents with proce ...

Elsevier2014