Добірка наукової літератури з теми "Frugal AI"
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Статті в журналах з теми "Frugal AI":
Štukelj, Gašper. "On the simplicity of simple heuristics." Adaptive Behavior 28, no. 4 (August 6, 2019): 261–71. http://dx.doi.org/10.1177/1059712319861589.
Silva, Mariane Alves, Marcela Martins Soares, Poliana Cristina de Almeida Fonsêca, Sarah Aparecida Vieira, Juliana Farias De Novaes, and Sylvia do Carmo Castro Franceschini. "Fatores sociodemográficos associados ao consumo de cálcio em crianças de 6 a 12 meses de vida." JMPHC | Journal of Management & Primary Health Care | ISSN 2179-6750 7, no. 1 (January 5, 2017): 65. http://dx.doi.org/10.14295/jmphc.v7i1.391.
Fisberg, Mauro, Agatha Nogueira Previdelli, Ana Paula Wolf Tasca Del’Arco, Abykeyla Tosatti, and Carlos Alberto Nogueira-de-Almeid. "Hábito alimentar nos lanches intermediários de crianças escolares brasileiras de 7 a 11 anos: estudo em amostra nacional representativa." International Journal of Nutrology 09, no. 04 (September 2016): 225–36. http://dx.doi.org/10.1055/s-0040-1705637.
Corsetti, Renato. "A Mother Tongue Spoken Mainly by Fathers." Language Problems and Language Planning 20, no. 3 (January 1, 1996): 263–73. http://dx.doi.org/10.1075/lplp.20.3.05cor.
Peres, Bruna Carraco de Azeredo, Marianna Miranda Rodrigues Vidal, Larissa Paulino Gama, Érica Ribeiro Pires, Desirée Lopes Reis, Marcio Marques Silva, Mara De Lima De Cnop, Avany Fernandes Pereira, and Thadia Turon Costa da Silva. "Oficina culinária como estratégia de articulação entre os movimentos sociais e a comunidade acadêmica para a promoção da alimentação saudável e sustentável: Relato de experiência." Revista Brasileira de Extensão Universitária 12, no. 2 (May 5, 2021): 179–89. http://dx.doi.org/10.36661/2358-0399.2021v12i2.11611.
Brydegaard, Mikkel, Ronniel D. Pedales, Vivian Feng, Assoumou saint-doria Yamoa, Benoit Kouakou, Hampus Månefjord, Lorenz Wührl, Christian Pylatiuk, Dalton de Souza Amorim, and Rudolf Meier. "Towards global insect biomonitoring with frugal methods." Philosophical Transactions of the Royal Society B: Biological Sciences 379, no. 1904 (May 6, 2024). http://dx.doi.org/10.1098/rstb.2023.0103.
Mooventhan, P., and Mamta Choudhary. "Assessment of Frozen Semen Quality through Foldscope Microscopy- A Novel Application of Frugal Science to Reduce the Infertility Rate." Indian Journal of Animal Research, Of (February 22, 2022). http://dx.doi.org/10.18805/ijar.b-4699.
Martignon, Laura, Tim Erickson, and Riccardo Viale. "Transparent, simple and robust fast-and-frugal trees and their construction." Frontiers in Human Dynamics 4 (October 10, 2022). http://dx.doi.org/10.3389/fhumd.2022.790033.
Rajapakse, Visal, Ishan Karunanayake, and Nadeem Ahmed. "Intelligence at the Extreme Edge: A Survey on Reformable TinyML." ACM Computing Surveys, February 13, 2023. http://dx.doi.org/10.1145/3583683.
Trappetti, Claudia, Lauren J. McAllister, Austen Chen, Hui Wang, Adrienne W. Paton, Marco R. Oggioni, Christopher A. McDevitt, and James C. Paton. "Autoinducer 2 Signaling via the Phosphotransferase FruA Drives Galactose Utilization by Streptococcus pneumoniae , Resulting in Hypervirulence." mBio 8, no. 1 (January 24, 2017). http://dx.doi.org/10.1128/mbio.02269-16.
Дисертації з теми "Frugal AI":
Cherdo, Yann. "Détection d'anomalie non supervisée sur les séries temporelle à faible coût énergétique utilisant les SNNs." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4018.
In the context of the predictive maintenance of the car manufacturer Renault, this thesis aims at providing low-power solutions for unsupervised anomaly detection on time-series. With the recent evolution of cars, more and more data are produced and need to be processed by machine learning algorithms. This processing can be performed in the cloud or directly at the edge inside the car. In such a case, network bandwidth, cloud services costs, data privacy management and data loss can be saved. Embedding a machine learning model inside a car is challenging as it requires frugal models due to memory and processing constraints. To this aim, we study the usage of spiking neural networks (SNNs) for anomaly detection, prediction and classification on time-series. SNNs models' performance and energy costs are evaluated in an edge scenario using generic hardware models that consider all calculation and memory costs. To leverage as much as possible the sparsity of SNNs, we propose a model with trainable sparse connections that consumes half the energy compared to its non-sparse version. This model is evaluated on anomaly detection public benchmarks, a real use-case of anomaly detection from Renault Alpine cars, weather forecasts and the google speech command dataset. We also compare its performance with other existing SNN and non-spiking models. We conclude that, for some use-cases, spiking models can provide state-of-the-art performance while consuming 2 to 8 times less energy. Yet, further studies should be undertaken to evaluate these models once embedded in a car. Inspired by neuroscience, we argue that other bio-inspired properties such as attention, sparsity, hierarchy or neural assemblies dynamics could be exploited to even get better energy efficiency and performance with spiking models. Finally, we end this thesis with an essay dealing with cognitive neuroscience, philosophy and artificial intelligence. Diving into conceptual difficulties linked to consciousness and considering the deterministic mechanisms of memory, we argue that consciousness and the self could be constitutively independent from memory. The aim of this essay is to question the nature of humans by contrast with the ones of machines and AI
Тези доповідей конференцій з теми "Frugal AI":
Vianello, Elisa, Filippo Moro, Tifenn Hirtzlin, Emmanuel Hardy, Bruno Fain, Melika Payvand, and Damien Querlioz. "Resistive memories to enable frugal AI devices." In Materials, devices and systems for neuromorphic computing 2022. València: Fundació Scito, 2022. http://dx.doi.org/10.29363/nanoge.matnec.2022.017.
Gandhi, Himanshu, Misha Mehra, and Vinay Ribeiro. "BOND: Efficient and Frugal DL Model Co-design for Botnet detection on IoT Gateways." In AIMLSystems 2021: The First International Conference on AI-ML-Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3486001.3486237.
Martignon, Laura, Joachim Engel, and Tim Erickson. "A Transparent, Simple AI Tool for Constructing Efficient and Robust Fast and Frugal Trees for Classification Under Risk." In Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.icots11.t6g3.