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</div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.17569">arXiv:2402.17569</a> <span> [<a href="https://arxiv.org/pdf/2402.17569">pdf</a>, <a href="https://arxiv.org/format/2402.17569">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Backpropagation-Based Analytical Derivatives of EKF Covariance for Active Sensing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Benhamou%2C+J">Jonas Benhamou</a>, <a href="/search/cs?searchtype=author&query=Bonnabel%2C+S">Silv猫re Bonnabel</a>, <a href="/search/cs?searchtype=author&query=Chapdelaine%2C+C">Camille Chapdelaine</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.17569v3-abstract-short" style="display: inline;"> To enhance accuracy of robot state estimation, active sensing (or perception-aware) methods seek trajectories that maximize the information gathered by the sensors. To this aim, one possibility is to seek trajectories that minimize the (estimation error) covariance matrix output by an extended Kalman filter (EKF), w.r.t. its control inputs over a given horizon. However, this is computationally dem… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.17569v3-abstract-full').style.display = 'inline'; document.getElementById('2402.17569v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.17569v3-abstract-full" style="display: none;"> To enhance accuracy of robot state estimation, active sensing (or perception-aware) methods seek trajectories that maximize the information gathered by the sensors. To this aim, one possibility is to seek trajectories that minimize the (estimation error) covariance matrix output by an extended Kalman filter (EKF), w.r.t. its control inputs over a given horizon. However, this is computationally demanding. In this article, we derive novel backpropagation analytical formulas for the derivatives of the covariance matrices of an EKF w.r.t. all its inputs. We then leverage the obtained analytical gradients as an enabling technology to derive perception-aware optimal motion plans. Simulations validate the approach, showcasing improvements in execution time, notably over PyTorch's automatic differentiation. Experimental results on a real vehicle also support the method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.17569v3-abstract-full').style.display = 'none'; document.getElementById('2402.17569v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted at IORS 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.10529">arXiv:2103.10529</a> <span> [<a href="https://arxiv.org/pdf/2103.10529">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> White Paper Machine Learning in Certified Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Delseny%2C+H">Herv茅 Delseny</a>, <a href="/search/cs?searchtype=author&query=Gabreau%2C+C">Christophe Gabreau</a>, <a href="/search/cs?searchtype=author&query=Gauffriau%2C+A">Adrien Gauffriau</a>, <a href="/search/cs?searchtype=author&query=Beaudouin%2C+B">Bernard Beaudouin</a>, <a href="/search/cs?searchtype=author&query=Ponsolle%2C+L">Ludovic Ponsolle</a>, <a href="/search/cs?searchtype=author&query=Alecu%2C+L">Lucian Alecu</a>, <a href="/search/cs?searchtype=author&query=Bonnin%2C+H">Hugues Bonnin</a>, <a href="/search/cs?searchtype=author&query=Beltran%2C+B">Brice Beltran</a>, <a href="/search/cs?searchtype=author&query=Duchel%2C+D">Didier Duchel</a>, <a href="/search/cs?searchtype=author&query=Ginestet%2C+J">Jean-Brice Ginestet</a>, <a href="/search/cs?searchtype=author&query=Hervieu%2C+A">Alexandre Hervieu</a>, <a href="/search/cs?searchtype=author&query=Martinez%2C+G">Ghilaine Martinez</a>, <a href="/search/cs?searchtype=author&query=Pasquet%2C+S">Sylvain Pasquet</a>, <a href="/search/cs?searchtype=author&query=Delmas%2C+K">Kevin Delmas</a>, <a href="/search/cs?searchtype=author&query=Pagetti%2C+C">Claire Pagetti</a>, <a href="/search/cs?searchtype=author&query=Gabriel%2C+J">Jean-Marc Gabriel</a>, <a href="/search/cs?searchtype=author&query=Chapdelaine%2C+C">Camille Chapdelaine</a>, <a href="/search/cs?searchtype=author&query=Picard%2C+S">Sylvaine Picard</a>, <a href="/search/cs?searchtype=author&query=Damour%2C+M">Mathieu Damour</a>, <a href="/search/cs?searchtype=author&query=Cappi%2C+C">Cyril Cappi</a>, <a href="/search/cs?searchtype=author&query=Gard%C3%A8s%2C+L">Laurent Gard猫s</a>, <a href="/search/cs?searchtype=author&query=De+Grancey%2C+F">Florence De Grancey</a>, <a href="/search/cs?searchtype=author&query=Jenn%2C+E">Eric Jenn</a>, <a href="/search/cs?searchtype=author&query=Lefevre%2C+B">Baptiste Lefevre</a>, <a href="/search/cs?searchtype=author&query=Flandin%2C+G">Gregory Flandin</a> , et al. (3 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2103.10529v1-abstract-short" style="display: inline;"> Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc. It is also an opportunity to implement and embed new capabilities out of the reach of classical implementation techniques. However, ML techniques introduce new potential risks. Therefore, t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.10529v1-abstract-full').style.display = 'inline'; document.getElementById('2103.10529v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.10529v1-abstract-full" style="display: none;"> Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc. It is also an opportunity to implement and embed new capabilities out of the reach of classical implementation techniques. However, ML techniques introduce new potential risks. Therefore, they have only been applied in systems where their benefits are considered worth the increase of risk. In practice, ML techniques raise multiple challenges that could prevent their use in systems submitted to certification constraints. But what are the actual challenges? Can they be overcome by selecting appropriate ML techniques, or by adopting new engineering or certification practices? These are some of the questions addressed by the ML Certification 3 Workgroup (WG) set-up by the Institut de Recherche Technologique Saint Exup茅ry de Toulouse (IRT), as part of the DEEL Project. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.10529v1-abstract-full').style.display = 'none'; document.getElementById('2103.10529v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">113 pages, White paper</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> S079L03T00-005 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2; K.7.3 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.03020">arXiv:2101.03020</a> <span> [<a href="https://arxiv.org/pdf/2101.03020">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Dataset Definition Standard (DDS) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cappi%2C+C">Cyril Cappi</a>, <a href="/search/cs?searchtype=author&query=Chapdelaine%2C+C">Camille Chapdelaine</a>, <a href="/search/cs?searchtype=author&query=Gardes%2C+L">Laurent Gardes</a>, <a href="/search/cs?searchtype=author&query=Jenn%2C+E">Eric Jenn</a>, <a href="/search/cs?searchtype=author&query=Lefevre%2C+B">Baptiste Lefevre</a>, <a href="/search/cs?searchtype=author&query=Picard%2C+S">Sylvaine Picard</a>, <a href="/search/cs?searchtype=author&query=Soumarmon%2C+T">Thomas Soumarmon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2101.03020v1-abstract-short" style="display: inline;"> This document gives a set of recommendations to build and manipulate the datasets used to develop and/or validate machine learning models such as deep neural networks. This document is one of the 3 documents defined in [1] to ensure the quality of datasets. This is a work in progress as good practices evolve along with our understanding of machine learning. The document is divided into three main… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.03020v1-abstract-full').style.display = 'inline'; document.getElementById('2101.03020v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.03020v1-abstract-full" style="display: none;"> This document gives a set of recommendations to build and manipulate the datasets used to develop and/or validate machine learning models such as deep neural networks. This document is one of the 3 documents defined in [1] to ensure the quality of datasets. This is a work in progress as good practices evolve along with our understanding of machine learning. The document is divided into three main parts. Section 2 addresses the data collection activity. Section 3 gives recommendations about the annotation process. Finally, Section 4 gives recommendations concerning the breakdown between train, validation, and test datasets. In each part, we first define the desired properties at stake, then we explain the objectives targeted to meet the properties, finally we state the recommendations to reach these objectives. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.03020v1-abstract-full').style.display = 'none'; document.getElementById('2101.03020v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.01799">arXiv:2011.01799</a> <span> [<a href="https://arxiv.org/pdf/2011.01799">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation">stat.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Ensuring Dataset Quality for Machine Learning Certification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Picard%2C+S">Sylvaine Picard</a>, <a href="/search/cs?searchtype=author&query=Chapdelaine%2C+C">Camille Chapdelaine</a>, <a href="/search/cs?searchtype=author&query=Cappi%2C+C">Cyril Cappi</a>, <a href="/search/cs?searchtype=author&query=Gardes%2C+L">Laurent Gardes</a>, <a href="/search/cs?searchtype=author&query=Jenn%2C+E">Eric Jenn</a>, <a href="/search/cs?searchtype=author&query=Lef%C3%A8vre%2C+B">Baptiste Lef猫vre</a>, <a href="/search/cs?searchtype=author&query=Soumarmon%2C+T">Thomas Soumarmon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2011.01799v1-abstract-short" style="display: inline;"> In this paper, we address the problem of dataset quality in the context of Machine Learning (ML)-based critical systems. We briefly analyse the applicability of some existing standards dealing with data and show that the specificities of the ML context are neither properly captured nor taken into ac-count. As a first answer to this concerning situation, we propose a dataset specification and verif… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.01799v1-abstract-full').style.display = 'inline'; document.getElementById('2011.01799v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.01799v1-abstract-full" style="display: none;"> In this paper, we address the problem of dataset quality in the context of Machine Learning (ML)-based critical systems. We briefly analyse the applicability of some existing standards dealing with data and show that the specificities of the ML context are neither properly captured nor taken into ac-count. As a first answer to this concerning situation, we propose a dataset specification and verification process, and apply it on a signal recognition system from the railway domain. In addi-tion, we also give a list of recommendations for the collection and management of datasets. This work is one step towards the dataset engineering process that will be required for ML to be used on safety critical systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.01799v1-abstract-full').style.display = 'none'; document.getElementById('2011.01799v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> The 10th IEEE International Workshop on Software Certification (WoSoCer 2020) </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> 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