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Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Nuria Oliver,Fernando Pérez-Cruz,Jose A. Lozano

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書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track
副標(biāo)題European Conference,
編輯Nuria Oliver,Fernando Pérez-Cruz,Jose A. Lozano
視頻videohttp://file.papertrans.cn/621/620543/620543.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Nuria Oliver,Fernando Pérez-Cruz,Jose A. Lozano
描述.The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic.?.The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions...The volumes are organized in topical sections as follows:..Research Track:..Part I:. Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications...Part II:. Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety...Part III: .Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics...Applied Data Science Track:..Part IV:. Anomaly detection and malware; spatio-temporal data; e-commerce and finance; health
出版日期Conference proceedings 2021
關(guān)鍵詞applied computing; computer vision; computing methodologies; correlation analysis; data mining; databases
版次1
doihttps://doi.org/10.1007/978-3-030-86523-8
isbn_softcover978-3-030-86522-1
isbn_ebook978-3-030-86523-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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Joslim: ,oint Widths and Weights ,ptimization for ,mable Neural Networks. From a practical standpoint, we propose Joslim, an algorithm that jointly optimizes both the widths and weights for slimmable nets, which outperforms existing methods for optimizing slimmable networks across various networks, datasets, and objectives. Quantitatively, improvements up?to 1.7% and 8%
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Variance Reduced Stochastic Proximal Algorithm for AUC Maximization Variance Reduced Stochastic Proximal algorithm for AUC Maximization (.) that combines the two areas of analyzing non-decomposable performance metrics with and optimization efforts to guarantee faster convergence. We perform an in-depth theoretical and empirical analysis to demonstrate that our algo
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More General and Effective Model Compression via an Additive Combination of Compressionsusing only 1 bit per weight without error degradation at the cost of adding a few floating point weights. However, VGG nets can be better compressed by combining low-rank with a few floating point weights.
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