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Artificial Intelligence in Clinical Health Care Applications: Viewpoint

Jun 26, 2019
Michael van Hartskamp, Sergio Consoli, Wim Verhaegh, Milan Petković, Anja van de Stolpe

The idea of Artificial Intelligence (AI) has a long history. It turned out, however, that reaching intelligence at human levels is more complicated than originally anticipated. Currently we are experiencing a renewed interest in AI, fueled by an enormous increase in computing power and an even larger increase in data, in combination with improved AI technologies like deep learning. Healthcare is considered the next domain to be revolutionized by Artificial Intelligence. While AI approaches are excellently suited to develop certain algorithms, for biomedical applications there are specific challenges. We propose recommendations to improve AI projects in the biomedical space and especially clinical healthcare.

* Journal of Medical Internet Research (2019), 21(4):e12100 

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On Evaluation of Embodied Navigation Agents

Jul 18, 2018
Peter Anderson, Angel Chang, Devendra Singh Chaplot, Alexey Dosovitskiy, Saurabh Gupta, Vladlen Koltun, Jana Kosecka, Jitendra Malik, Roozbeh Mottaghi, Manolis Savva, Amir R. Zamir

Skillful mobile operation in three-dimensional environments is a primary topic of study in Artificial Intelligence. The past two years have seen a surge of creative work on navigation. This creative output has produced a plethora of sometimes incompatible task definitions and evaluation protocols. To coordinate ongoing and future research in this area, we have convened a working group to study empirical methodology in navigation research. The present document summarizes the consensus recommendations of this working group. We discuss different problem statements and the role of generalization, present evaluation measures, and provide standard scenarios that can be used for benchmarking.

* Report of a working group on empirical methodology in navigation research. Authors are listed in alphabetical order 

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Regularization for Deep Learning: A Taxonomy

Oct 29, 2017
Jan Kukačka, Vladimir Golkov, Daniel Cremers

Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affect data, network architectures, error terms, regularization terms, and optimization procedures. We do not provide all details about the listed methods; instead, we present an overview of how the methods can be sorted into meaningful categories and sub-categories. This helps revealing links and fundamental similarities between them. Finally, we include practical recommendations both for users and for developers of new regularization methods.


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Sequential ranking under random semi-bandit feedback

May 26, 2016
Hossein Vahabi, Paul Lagrée, Claire Vernade, Olivier Cappé

In many web applications, a recommendation is not a single item suggested to a user but a list of possibly interesting contents that may be ranked in some contexts. The combinatorial bandit problem has been studied quite extensively these last two years and many theoretical results now exist : lower bounds on the regret or asymptotically optimal algorithms. However, because of the variety of situations that can be considered, results are designed to solve the problem for a specific reward structure such as the Cascade Model. The present work focuses on the problem of ranking items when the user is allowed to click on several items while scanning the list from top to bottom.

* This submission has been withdrawn by arXiv administrators due to irreconcilable authorship dispute 

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Budget-Constrained Reinforcement of Ranked Objects

Mar 27, 2022
Amir Ban, Moshe Tennenholtz

Commercial entries, such as hotels, are ranked according to score by a search engine or recommendation system, and the score of each can be improved upon by making a targeted investment, e.g., advertising. We study the problem of how a principal, who owns or supports a set of entries, can optimally allocate a budget to maximize their ranking. Representing the set of ranked scores as a probability distribution over scores, we treat this question as a game between distributions. We show that, in the general case, the best ranking is achieved by equalizing the scores of several disjoint score ranges. We show that there is a unique optimal reinforcement strategy, and provide an efficient algorithm implementing it.


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Compliance checking in reified IO logic via SHACL

Oct 13, 2021
Livio Robaldo, Kolawole J. Adebayo

Reified Input/Output (I/O) logic[21] has been recently proposed to model real-world norms in terms of the logic in [11]. This is massively grounded on the notion of reification, and it has specifically designed to model meaning of natural language sentences, such as the ones occurring in existing legislation. This paper presents a methodology to carry out compliance checking on reified I/O logic formulae. These are translated in SHACL (Shapes Constraint Language) shapes, a recent W3C recommendation to validate and reason with RDF triplestores. Compliance checking is then enforced by validating RDF graphs describing states of affairs with respect to these SHACL shapes.


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"Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple Shops

Jul 08, 2021
Patrick John Chia, Bingqing Yu, Jacopo Tagliabue

Large eCommerce players introduced comparison tables as a new type of recommendations. However, building comparisons at scale without pre-existing training/taxonomy data remains an open challenge, especially within the operational constraints of shops in the long tail. We present preliminary results from building a comparison pipeline designed to scale in a multi-shop scenario: we describe our design choices and run extensive benchmarks on multiple shops to stress-test it. Finally, we run a small user study on property selection and conclude by discussing potential improvements and highlighting the questions that remain to be addressed.

* Accepted for publication at SIGIR eCom 2021 

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Analysis of draft EU ADS Performance Requirements

Jun 03, 2021
Maria Soledad Elli, Jack Weast

Recently, the European Commission published draft regulation for uniform procedures and technical specification for the type-approval of motor vehicles with an automated driving system (ADS). While the draft regulation is welcome progress for an industry ready to deploy life saving automated vehicle technology, we believe that the requirements can be further improved to enhance the safety and societal acceptance of automated vehicles (AVs). In this paper, we evaluate the draft regulation's performance requirements that would impact the Dynamic Driving Task (DDT). We highlight potential problems that can arise from the current proposed requirements and propose practical recommendations to improve the regulation.


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Pitfalls in Machine Learning Research: Reexamining the Development Cycle

Nov 04, 2020
Stella Biderman, Walter J. Scheirer

Machine learning has the potential to fuel further advances in data science, but it is greatly hindered by an ad hoc design process, poor data hygiene, and a lack of statistical rigor in model evaluation. Recently, these issues have begun to attract more attention as they have caused public and embarrassing issues in research and development. Drawing from our experience as machine learning researchers, we follow the machine learning process from algorithm design to data collection to model evaluation, drawing attention to common pitfalls and providing practical recommendations for improvements. At each step, case studies are introduced to highlight how these pitfalls occur in practice, and where things could be improved.

* NeurIPS "I Can't Believe It's Not Better!" Workshop 

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