Impacted
发表于 2025-3-21 17:23:04
书目名称Machine Learning and Knowledge Discovery in Databases. Research Track影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0620542<br><br> <br><br>书目名称Machine Learning and Knowledge Discovery in Databases. Research Track影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0620542<br><br> <br><br>书目名称Machine Learning and Knowledge Discovery in Databases. Research Track网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0620542<br><br> <br><br>书目名称Machine Learning and Knowledge Discovery in Databases. Research Track网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0620542<br><br> <br><br>书目名称Machine Learning and Knowledge Discovery in Databases. Research Track被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0620542<br><br> <br><br>书目名称Machine Learning and Knowledge Discovery in Databases. Research Track被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0620542<br><br> <br><br>书目名称Machine Learning and Knowledge Discovery in Databases. Research Track年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0620542<br><br> <br><br>书目名称Machine Learning and Knowledge Discovery in Databases. Research Track年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0620542<br><br> <br><br>书目名称Machine Learning and Knowledge Discovery in Databases. Research Track读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0620542<br><br> <br><br>书目名称Machine Learning and Knowledge Discovery in Databases. Research Track读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0620542<br><br> <br><br>
heckle
发表于 2025-3-21 23:39:57
Reinventing Node-centric Traffic Forecasting for Improved Accuracy and Efficiencyg methods, we identify two primary research approaches: node-centric and graph-centric. Node-centric methods focus on constructing spatial features through preprocessing and modeling spatial correlations in the input space. In contrast, graph-centric methods mainly rely on graph neural networks to c
生命层
发表于 2025-3-22 03:56:31
Direct-Effect Risk Minimization for Domain Generalizationhis is known as the problem of correlation shift and has posed concerns on the reliability of machine learning. In this work, we introduce the framework of direct and indirect effects from causal inference to the domain generalization problem. Models that learn direct effects minimize the worst-case
paragon
发表于 2025-3-22 06:03:23
Federated Frank-Wolfe Algorithmrithms for constrained machine learning problems are still limited, particularly when the projection step is costly. To this end, we propose a Federated Frank-Wolfe Algorithm (.). . features data privacy, low per-iteration cost, and communication of sparse signals. In the deterministic setting, . ac
Costume
发表于 2025-3-22 09:29:44
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Malleable
发表于 2025-3-22 16:21:04
Deep Sketched Output Kernel Regression for Structured Predictionwide variety of output modalities. In particular, they have been successfully used in the context of surrogate non-parametric regression, where the kernel trick is typically exploited in the input space as well. However, when inputs are images or texts, more expressive models such as deep neural net
jealousy
发表于 2025-3-22 19:21:47
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deriver
发表于 2025-3-23 00:00:25
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不近人情
发表于 2025-3-23 01:41:46
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ANIM
发表于 2025-3-23 08:19:40
Probabilistic Circuits with Constraints via Convex Optimization class of tractable models that allow efficient computations (such as conditional and marginal probabilities) while achieving state-of-the-art performance in some domains. The proposed approach takes both a PC and constraints as inputs, and outputs a new PC that satisfies the constraints. This is do