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Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> A finite-sample bound for identifying partially observed linear switched systems from a single trajectory </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Racz%2C+D">Daniel Racz</a>, <a href="/search/cs?searchtype=author&query=Petreczky%2C+M">Mihaly Petreczky</a>, <a href="/search/cs?searchtype=author&query=Daroczy%2C+B">Balint Daroczy</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="2503.13766v1-abstract-short" style="display: inline;"> We derive a finite-sample probabilistic bound on the parameter estimation error of a system identification algorithm for Linear Switched Systems. The algorithm estimates Markov parameters from a single trajectory and applies a variant of the Ho-Kalman algorithm to recover the system matrices. Our bound guarantees statistical consistency under the assumption that the true system exhibits quadratic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.13766v1-abstract-full').style.display = 'inline'; document.getElementById('2503.13766v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.13766v1-abstract-full" style="display: none;"> We derive a finite-sample probabilistic bound on the parameter estimation error of a system identification algorithm for Linear Switched Systems. The algorithm estimates Markov parameters from a single trajectory and applies a variant of the Ho-Kalman algorithm to recover the system matrices. Our bound guarantees statistical consistency under the assumption that the true system exhibits quadratic stability. The proof leverages the theory of weakly dependent processes. To the best of our knowledge, this is the first finite-sample bound for this algorithm in the single-trajectory setting. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.13766v1-abstract-full').style.display = 'none'; document.getElementById('2503.13766v1-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> 17 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.03364">arXiv:2502.03364</a> <span> [<a href="https://arxiv.org/pdf/2502.03364">pdf</a>, <a href="https://arxiv.org/format/2502.03364">other</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> </div> </div> <p class="title is-5 mathjax"> Scaling laws in wearable human activity recognition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hoddes%2C+T">Tom Hoddes</a>, <a href="/search/cs?searchtype=author&query=Bijamov%2C+A">Alex Bijamov</a>, <a href="/search/cs?searchtype=author&query=Joshi%2C+S">Saket Joshi</a>, <a href="/search/cs?searchtype=author&query=Roggen%2C+D">Daniel Roggen</a>, <a href="/search/cs?searchtype=author&query=Etemad%2C+A">Ali Etemad</a>, <a href="/search/cs?searchtype=author&query=Harle%2C+R">Robert Harle</a>, <a href="/search/cs?searchtype=author&query=Racz%2C+D">David Racz</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="2502.03364v1-abstract-short" style="display: inline;"> Many deep architectures and self-supervised pre-training techniques have been proposed for human activity recognition (HAR) from wearable multimodal sensors. Scaling laws have the potential to help move towards more principled design by linking model capacity with pre-training data volume. Yet, scaling laws have not been established for HAR to the same extent as in language and vision. By conducti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03364v1-abstract-full').style.display = 'inline'; document.getElementById('2502.03364v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.03364v1-abstract-full" style="display: none;"> Many deep architectures and self-supervised pre-training techniques have been proposed for human activity recognition (HAR) from wearable multimodal sensors. Scaling laws have the potential to help move towards more principled design by linking model capacity with pre-training data volume. Yet, scaling laws have not been established for HAR to the same extent as in language and vision. By conducting an exhaustive grid search on both amount of pre-training data and Transformer architectures, we establish the first known scaling laws for HAR. We show that pre-training loss scales with a power law relationship to amount of data and parameter count and that increasing the number of users in a dataset results in a steeper improvement in performance than increasing data per user, indicating that diversity of pre-training data is important, which contrasts to some previously reported findings in self-supervised HAR. We show that these scaling laws translate to downstream performance improvements on three HAR benchmark datasets of postures, modes of locomotion and activities of daily living: UCI HAR and WISDM Phone and WISDM Watch. Finally, we suggest some previously published works should be revisited in light of these scaling laws with more adequate model capacities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03364v1-abstract-full').style.display = 'none'; document.getElementById('2502.03364v1-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> 5 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.03156">arXiv:2410.03156</a> <span> [<a href="https://arxiv.org/pdf/2410.03156">pdf</a>, <a href="https://arxiv.org/format/2410.03156">other</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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> MELODI: Exploring Memory Compression for Long Contexts </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+Y">Yinpeng Chen</a>, <a href="/search/cs?searchtype=author&query=Hutchins%2C+D">DeLesley Hutchins</a>, <a href="/search/cs?searchtype=author&query=Jansen%2C+A">Aren Jansen</a>, <a href="/search/cs?searchtype=author&query=Zhmoginov%2C+A">Andrey Zhmoginov</a>, <a href="/search/cs?searchtype=author&query=Racz%2C+D">David Racz</a>, <a href="/search/cs?searchtype=author&query=Andersen%2C+J">Jesper Andersen</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="2410.03156v1-abstract-short" style="display: inline;"> We present MELODI, a novel memory architecture designed to efficiently process long documents using short context windows. The key principle behind MELODI is to represent short-term and long-term memory as a hierarchical compression scheme across both network layers and context windows. Specifically, the short-term memory is achieved through recurrent compression of context windows across multiple… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03156v1-abstract-full').style.display = 'inline'; document.getElementById('2410.03156v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.03156v1-abstract-full" style="display: none;"> We present MELODI, a novel memory architecture designed to efficiently process long documents using short context windows. The key principle behind MELODI is to represent short-term and long-term memory as a hierarchical compression scheme across both network layers and context windows. Specifically, the short-term memory is achieved through recurrent compression of context windows across multiple layers, ensuring smooth transitions between windows. In contrast, the long-term memory performs further compression within a single middle layer and aggregates information across context windows, effectively consolidating crucial information from the entire history. Compared to a strong baseline - the Memorizing Transformer employing dense attention over a large long-term memory (64K key-value pairs) - our method demonstrates superior performance on various long-context datasets while remarkably reducing the memory footprint by a factor of 8. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03156v1-abstract-full').style.display = 'none'; document.getElementById('2410.03156v1-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> 4 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.20278">arXiv:2405.20278</a> <span> [<a href="https://arxiv.org/pdf/2405.20278">pdf</a>, <a href="https://arxiv.org/ps/2405.20278">ps</a>, <a href="https://arxiv.org/format/2405.20278">other</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="Artificial Intelligence">cs.AI</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"> Length independent generalization bounds for deep SSM architectures </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=R%C3%A1cz%2C+D">D谩niel R谩cz</a>, <a href="/search/cs?searchtype=author&query=Petreczky%2C+M">Mih谩ly Petreczky</a>, <a href="/search/cs?searchtype=author&query=Dar%C3%B3czy%2C+B">B谩lint Dar贸czy</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="2405.20278v2-abstract-short" style="display: inline;"> Many state-of-the-art models trained on long-range sequences, for example S4, S5 or LRU, are made of sequential blocks combining State-Space Models (SSMs) with neural networks. In this paper we provide a PAC bound that holds for these kind of architectures with stable SSM blocks and does not depend on the length of the input sequence. Imposing stability of the SSM blocks is a standard practice in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.20278v2-abstract-full').style.display = 'inline'; document.getElementById('2405.20278v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.20278v2-abstract-full" style="display: none;"> Many state-of-the-art models trained on long-range sequences, for example S4, S5 or LRU, are made of sequential blocks combining State-Space Models (SSMs) with neural networks. In this paper we provide a PAC bound that holds for these kind of architectures with stable SSM blocks and does not depend on the length of the input sequence. Imposing stability of the SSM blocks is a standard practice in the literature, and it is known to help performance. Our results provide a theoretical justification for the use of stable SSM blocks as the proposed PAC bound decreases as the degree of stability of the SSM blocks increases. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.20278v2-abstract-full').style.display = 'none'; document.getElementById('2405.20278v2-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> 11 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 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">20 pages, no figures, accepted at ICML 2024 Next Generation of Sequence Modeling Architectures Workshop</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.10054">arXiv:2405.10054</a> <span> [<a href="https://arxiv.org/pdf/2405.10054">pdf</a>, <a href="https://arxiv.org/format/2405.10054">other</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="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> A finite-sample generalization bound for stable LPV systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Racz%2C+D">Daniel Racz</a>, <a href="/search/cs?searchtype=author&query=Gonzalez%2C+M">Martin Gonzalez</a>, <a href="/search/cs?searchtype=author&query=Petreczky%2C+M">Mihaly Petreczky</a>, <a href="/search/cs?searchtype=author&query=Benczur%2C+A">Andras Benczur</a>, <a href="/search/cs?searchtype=author&query=Daroczy%2C+B">Balint Daroczy</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="2405.10054v3-abstract-short" style="display: inline;"> One of the main theoretical challenges in learning dynamical systems from data is providing upper bounds on the generalization error, that is, the difference between the expected prediction error and the empirical prediction error measured on some finite sample. In machine learning, a popular class of such bounds are the so-called Probably Approximately Correct (PAC) bounds. In this paper, we deri… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.10054v3-abstract-full').style.display = 'inline'; document.getElementById('2405.10054v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.10054v3-abstract-full" style="display: none;"> One of the main theoretical challenges in learning dynamical systems from data is providing upper bounds on the generalization error, that is, the difference between the expected prediction error and the empirical prediction error measured on some finite sample. In machine learning, a popular class of such bounds are the so-called Probably Approximately Correct (PAC) bounds. In this paper, we derive a PAC bound for stable continuous-time linear parameter-varying (LPV) systems. Our bound depends on the H2 norm of the chosen class of the LPV systems, but does not depend on the time interval for which the signals are considered. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.10054v3-abstract-full').style.display = 'none'; document.getElementById('2405.10054v3-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> 21 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 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">8 pages, 1 figure, under review</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.0 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.17378">arXiv:2310.17378</a> <span> [<a href="https://arxiv.org/pdf/2310.17378">pdf</a>, <a href="https://arxiv.org/format/2310.17378">other</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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Optimization dependent generalization bound for ReLU networks based on sensitivity in the tangent bundle </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=R%C3%A1cz%2C+D">D谩niel R谩cz</a>, <a href="/search/cs?searchtype=author&query=Petreczky%2C+M">Mih谩ly Petreczky</a>, <a href="/search/cs?searchtype=author&query=Csert%C3%A1n%2C+A">Andr谩s Csert谩n</a>, <a href="/search/cs?searchtype=author&query=Dar%C3%B3czy%2C+B">B谩lint Dar贸czy</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="2310.17378v2-abstract-short" style="display: inline;"> Recent advances in deep learning have given us some very promising results on the generalization ability of deep neural networks, however literature still lacks a comprehensive theory explaining why heavily over-parametrized models are able to generalize well while fitting the training data. In this paper we propose a PAC type bound on the generalization error of feedforward ReLU networks via esti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.17378v2-abstract-full').style.display = 'inline'; document.getElementById('2310.17378v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.17378v2-abstract-full" style="display: none;"> Recent advances in deep learning have given us some very promising results on the generalization ability of deep neural networks, however literature still lacks a comprehensive theory explaining why heavily over-parametrized models are able to generalize well while fitting the training data. In this paper we propose a PAC type bound on the generalization error of feedforward ReLU networks via estimating the Rademacher complexity of the set of networks available from an initial parameter vector via gradient descent. The key idea is to bound the sensitivity of the network's gradient to perturbation of the input data along the optimization trajectory. The obtained bound does not explicitly depend on the depth of the network. Our results are experimentally verified on the MNIST and CIFAR-10 datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.17378v2-abstract-full').style.display = 'none'; document.getElementById('2310.17378v2-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> 4 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </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">17 pages, 5 figures, OPT2023: 15th Annual Workshop on Optimization for Machine Learning at the 37th NeurIPS 2023, New Orleans, LA, USA</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.03630">arXiv:2307.03630</a> <span> [<a href="https://arxiv.org/pdf/2307.03630">pdf</a>, <a href="https://arxiv.org/ps/2307.03630">ps</a>, <a href="https://arxiv.org/format/2307.03630">other</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> </div> </div> <p class="title is-5 mathjax"> PAC bounds of continuous Linear Parameter-Varying systems related to neural ODEs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=R%C3%A1cz%2C+D">D谩niel R谩cz</a>, <a href="/search/cs?searchtype=author&query=Petreczky%2C+M">Mih谩ly Petreczky</a>, <a href="/search/cs?searchtype=author&query=Dar%C3%B3czy%2C+B">B谩lint Dar贸czy</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="2307.03630v1-abstract-short" style="display: inline;"> We consider the problem of learning Neural Ordinary Differential Equations (neural ODEs) within the context of Linear Parameter-Varying (LPV) systems in continuous-time. LPV systems contain bilinear systems which are known to be universal approximators for non-linear systems. Moreover, a large class of neural ODEs can be embedded into LPV systems. As our main contribution we provide Probably Appro… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.03630v1-abstract-full').style.display = 'inline'; document.getElementById('2307.03630v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.03630v1-abstract-full" style="display: none;"> We consider the problem of learning Neural Ordinary Differential Equations (neural ODEs) within the context of Linear Parameter-Varying (LPV) systems in continuous-time. LPV systems contain bilinear systems which are known to be universal approximators for non-linear systems. Moreover, a large class of neural ODEs can be embedded into LPV systems. As our main contribution we provide Probably Approximately Correct (PAC) bounds under stability for LPV systems related to neural ODEs. The resulting bounds have the advantage that they do not depend on the integration interval. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.03630v1-abstract-full').style.display = 'none'; document.getElementById('2307.03630v1-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 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </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">12 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.0 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.01934">arXiv:2202.01934</a> <span> [<a href="https://arxiv.org/pdf/2202.01934">pdf</a>, <a href="https://arxiv.org/format/2202.01934">other</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> </div> </div> <p class="title is-5 mathjax"> Smartphone-based Hard-braking Event Detection at Scale for Road Safety Services </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Liu%2C+L">Luyang Liu</a>, <a href="/search/cs?searchtype=author&query=Racz%2C+D">David Racz</a>, <a href="/search/cs?searchtype=author&query=Vaillancourt%2C+K">Kara Vaillancourt</a>, <a href="/search/cs?searchtype=author&query=Michelman%2C+J">Julie Michelman</a>, <a href="/search/cs?searchtype=author&query=Barnes%2C+M">Matt Barnes</a>, <a href="/search/cs?searchtype=author&query=Mellem%2C+S">Stefan Mellem</a>, <a href="/search/cs?searchtype=author&query=Eastham%2C+P">Paul Eastham</a>, <a href="/search/cs?searchtype=author&query=Green%2C+B">Bradley Green</a>, <a href="/search/cs?searchtype=author&query=Armstrong%2C+C">Charles Armstrong</a>, <a href="/search/cs?searchtype=author&query=Bal%2C+R">Rishi Bal</a>, <a href="/search/cs?searchtype=author&query=O%27Banion%2C+S">Shawn O'Banion</a>, <a href="/search/cs?searchtype=author&query=Guo%2C+F">Feng Guo</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="2202.01934v1-abstract-short" style="display: inline;"> Road crashes are the sixth leading cause of lost disability-adjusted life-years (DALYs) worldwide. One major challenge in traffic safety research is the sparsity of crashes, which makes it difficult to achieve a fine-grain understanding of crash causations and predict future crash risk in a timely manner. Hard-braking events have been widely used as a safety surrogate due to their relatively high… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.01934v1-abstract-full').style.display = 'inline'; document.getElementById('2202.01934v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.01934v1-abstract-full" style="display: none;"> Road crashes are the sixth leading cause of lost disability-adjusted life-years (DALYs) worldwide. One major challenge in traffic safety research is the sparsity of crashes, which makes it difficult to achieve a fine-grain understanding of crash causations and predict future crash risk in a timely manner. Hard-braking events have been widely used as a safety surrogate due to their relatively high prevalence and ease of detection with embedded vehicle sensors. As an alternative to using sensors fixed in vehicles, this paper presents a scalable approach for detecting hard-braking events using the kinematics data collected from smartphone sensors. We train a Transformer-based machine learning model for hard-braking event detection using concurrent sensor readings from smartphones and vehicle sensors from drivers who connect their phone to the vehicle while navigating in Google Maps. The detection model shows superior performance with a $0.83$ Area under the Precision-Recall Curve (PR-AUC), which is $3.8\times$better than a GPS speed-based heuristic model, and $166.6\times$better than an accelerometer-based heuristic model. The detected hard-braking events are strongly correlated with crashes from publicly available datasets, supporting their use as a safety surrogate. In addition, we conduct model fairness and selection bias evaluation to ensure that the safety benefits are equally shared. The developed methodology can benefit many safety applications such as identifying safety hot spots at road network level, evaluating the safety of new user interfaces, as well as using routing to improve traffic safety. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.01934v1-abstract-full').style.display = 'none'; document.getElementById('2202.01934v1-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 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.13581">arXiv:2110.13581</a> <span> [<a href="https://arxiv.org/pdf/2110.13581">pdf</a>, <a href="https://arxiv.org/format/2110.13581">other</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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Gradient representations in ReLU networks as similarity functions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=R%C3%A1cz%2C+D">D谩niel R谩cz</a>, <a href="/search/cs?searchtype=author&query=Dar%C3%B3czy%2C+B">B谩lint Dar贸czy</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="2110.13581v1-abstract-short" style="display: inline;"> Feed-forward networks can be interpreted as mappings with linear decision surfaces at the level of the last layer. We investigate how the tangent space of the network can be exploited to refine the decision in case of ReLU (Rectified Linear Unit) activations. We show that a simple Riemannian metric parametrized on the parameters of the network forms a similarity function at least as good as the or… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.13581v1-abstract-full').style.display = 'inline'; document.getElementById('2110.13581v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.13581v1-abstract-full" style="display: none;"> Feed-forward networks can be interpreted as mappings with linear decision surfaces at the level of the last layer. We investigate how the tangent space of the network can be exploited to refine the decision in case of ReLU (Rectified Linear Unit) activations. We show that a simple Riemannian metric parametrized on the parameters of the network forms a similarity function at least as good as the original network and we suggest a sparse metric to increase the similarity gap. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.13581v1-abstract-full').style.display = 'none'; document.getElementById('2110.13581v1-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> 26 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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">Accepted at 29th ESANN 2021, 6-8 October 2021, Belgium, 7 pages, 1 figure</span> </p> </li> </ol> <div class="is-hidden-tablet"> <!-- feedback for mobile only --> <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> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary"> <!-- MetaColumn 1 --> <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 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