Towards Long-Term Memory for Social Robots: Proposing a New Challenge for the RoboCup@Home League

Pavez, Matías; Ruiz‐del‐Solar, Javier; Amo, Victoria; Driehausen, Felix Meyer zu

doi:10.48550/arxiv.1811.10758

Cited by 1 publication

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“…For example, the Retrospective Image Registration Experiment challenge revealed the utility of mutual information [53], and the NetFlix challenge boosted the field of recommender systems [17,25]. From the field of robotics it is also evident that competitions such as the RoboCup [29] can be a catalyst for innovation [23,40] as well as interdisciplinary teamwork.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Challenge Design: Insight and Deployment Challenges to Address Medical Image Analysis Problems

Mendrik¹,

Aylward²

2019

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In this paper we aim to refine the concept of grand challenges in medical image analysis, based on statistical principles from quantitative and qualitative experimental research. We identify two types of challenges based on their generalization objective: 1) a deployment challenge and 2) an insight challenge. A deployment challenge's generalization objective is to find algorithms that solve a medical image analysis problem, which thereby requires the use of a quantitative experimental design. An insight challenge's generalization objective is to gain a broad understanding of what class of algorithms might be effective for a class of medical image analysis problems, in which case a qualitative experimental design is sufficient. Both challenge types are valuable, but problems arise when a challenge's design and objective are inconsistent, as is often the case when a challenge does not carefully consider these concepts. Therefore, in this paper, we propose a theoretical framework, based on statistical principles, to guide researchers in challenge design, documentation, and assessment. Experimental results are given that explore the factors that effect the practical implementation of this theoretical framework.

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