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#supervised — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #supervised, aggregated by home.social.

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  1. RE: newsie.social/@LALegault/11698

    Sounds like these digital contracts aren’t worth the paper they’re written on. Hahaha! Sounds like Groucho and Chico Marx tearing up the contract in Night At The Opera (“You can’t fool me, there ain’t no sanity clause.”)
    #tesla #self-driving #AutonomousVehicles #Supervised #DisappearingDigitalContracts

  2. #Teilautonomes #Fahren:

    #Tesla bringt #FSD erstmals nach #Europa

    Tesla darf sein fortgeschrittenes Fahrassistenzsystem "Full Self-Driving ( #Supervised )" erstmals in Europa einsetzen, und zwar in den Niederlanden. Die dortige #Zulassungsbehörde #RDW hat dem System laut Reuters eine Typgenehmigung erteilt und gleichzeitig ein ungewöhnlich klares Statement zur Sicherheit abgegeben.

    golem.de/news/teilautonomes-fa

  3. #Teilautonomes #Fahren:

    #Tesla bringt #FSD erstmals nach #Europa

    Tesla darf sein fortgeschrittenes Fahrassistenzsystem "Full Self-Driving ( #Supervised )" erstmals in Europa einsetzen, und zwar in den Niederlanden. Die dortige #Zulassungsbehörde #RDW hat dem System laut Reuters eine Typgenehmigung erteilt und gleichzeitig ein ungewöhnlich klares Statement zur Sicherheit abgegeben.

    golem.de/news/teilautonomes-fa

  4. 'A Comparative Evaluation of Quantification Methods', by Tobias Schumacher, Markus Strohmaier, Florian Lemmerich.

    jmlr.org/papers/v26/21-0241.ht

    #classifiers #supervised #quantification

  5. 'A Comparative Evaluation of Quantification Methods', by Tobias Schumacher, Markus Strohmaier, Florian Lemmerich.

    jmlr.org/papers/v26/21-0241.ht

    #classifiers #supervised #quantification

  6. 'Efficient and Robust Semi-supervised Estimation of Average Treatment Effect with Partially Annotated Treatment and Response', by Jue Hou, Rajarshi Mukherjee, Tianxi Cai.

    jmlr.org/papers/v26/23-1587.ht

    #supervised #annotated #annotate

  7. 'Efficient and Robust Semi-supervised Estimation of Average Treatment Effect with Partially Annotated Treatment and Response', by Jue Hou, Rajarshi Mukherjee, Tianxi Cai.

    jmlr.org/papers/v26/23-1587.ht

    #supervised #annotated #annotate

  8. 'Optimizing Data Collection for Machine Learning', by Rafid Mahmood, James Lucas, Jose M. Alvarez, Sanja Fidler, Marc T. Law.

    jmlr.org/papers/v26/23-0292.ht

    #supervised #deep #collecting

  9. 'Optimizing Data Collection for Machine Learning', by Rafid Mahmood, James Lucas, Jose M. Alvarez, Sanja Fidler, Marc T. Law.

    jmlr.org/papers/v26/23-0292.ht

    #supervised #deep #collecting

  10. 'Supervised Learning with Evolving Tasks and Performance Guarantees', by Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano.

    jmlr.org/papers/v26/24-0343.ht

    #supervised #tasks #classification

  11. 'Supervised Learning with Evolving Tasks and Performance Guarantees', by Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano.

    jmlr.org/papers/v26/24-0343.ht

    #supervised #tasks #classification

  12. How long until investors and fans stop believing in bullshit? #Supervised #full #selfdriving my ass ... 😆
    'Musk said in 2022 that Tesla would mass-produce robotaxis by 2024. Before that, in 2019, he said that Tesla would have a million robotaxis on roads by 2020. Musk has promised that Tesla would solve full self-driving “next year” since at least 2016.'
    techcrunch.com/2024/12/31/elon

  13. 'Recursive Estimation of Conditional Kernel Mean Embeddings', by Ambrus Tamás, Balázs Csanád Csáji.

    jmlr.org/papers/v25/23-0168.ht

    #embeddings #supervised #estimation

  14. 'Recursive Estimation of Conditional Kernel Mean Embeddings', by Ambrus Tamás, Balázs Csanád Csáji.

    jmlr.org/papers/v25/23-0168.ht

    #embeddings #supervised #estimation

  15. 'On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Models', by Xuetong Wu, Mingming Gong, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu.

    jmlr.org/papers/v25/22-1024.ht

    #causal #causality #supervised

  16. 'On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Models', by Xuetong Wu, Mingming Gong, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu.

    jmlr.org/papers/v25/22-1024.ht

    #causal #causality #supervised

  17. Dear Friends #off AI (Artificial #Independence),
    As we know
    #AI is being #supervised or #managed by Actual Income (AI) self-appointed geniuses. Or the IG-Noble usual suspects: ​:ablobcatgoogly:​
    #Bezos the #Merciless, Elon Musk rat, tik-tok politicos, Lazy #influencers, prostituted #journalists etc.

    #Fortunately we all have our off #switch of #choice available. ⏹️

  18. 'Semi-supervised Inference for Block-wise Missing Data without Imputation', by Shanshan Song, Yuanyuan Lin, Yong Zhou.

    jmlr.org/papers/v25/21-1504.ht

    #imputation #supervised #neuroimaging

  19. 'Semi-supervised Inference for Block-wise Missing Data without Imputation', by Shanshan Song, Yuanyuan Lin, Yong Zhou.

    jmlr.org/papers/v25/21-1504.ht

    #imputation #supervised #neuroimaging

  20. 'Distributed Estimation on Semi-Supervised Generalized Linear Model', by Jiyuan Tu, Weidong Liu, Xiaojun Mao.

    jmlr.org/papers/v25/22-0670.ht

    #supervised #distributed #estimation

  21. 'Distributed Estimation on Semi-Supervised Generalized Linear Model', by Jiyuan Tu, Weidong Liu, Xiaojun Mao.

    jmlr.org/papers/v25/22-0670.ht

    #supervised #distributed #estimation

  22. 'Sample-efficient Adversarial Imitation Learning', by Dahuin Jung, Hyungyu Lee, Sungroh Yoon.

    jmlr.org/papers/v25/23-0314.ht

    #imitation #adversarial #supervised

  23. 'Dimensionality Reduction and Wasserstein Stability for Kernel Regression', by Stephan Eckstein, Armin Iske, Mathias Trabs.

    jmlr.org/papers/v24/22-0303.ht

    #regression #pca #supervised

  24. 'Weisfeiler and Leman go Machine Learning: The Story so far', by Christopher Morris et al.

    jmlr.org/papers/v24/22-0240.ht

    #graphs #graph #supervised

  25. 'Fair Data Representation for Machine Learning at the Pareto Frontier', by Shizhou Xu, Thomas Strohmer.

    jmlr.org/papers/v24/22-0005.ht

    #wasserstein #supervised #fairness