melancholy
发表于 2025-3-21 18:02:15
书目名称Neural Information Processing影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0663625<br><br> <br><br>书目名称Neural Information Processing影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0663625<br><br> <br><br>书目名称Neural Information Processing网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0663625<br><br> <br><br>书目名称Neural Information Processing网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0663625<br><br> <br><br>书目名称Neural Information Processing被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0663625<br><br> <br><br>书目名称Neural Information Processing被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0663625<br><br> <br><br>书目名称Neural Information Processing年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0663625<br><br> <br><br>书目名称Neural Information Processing年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0663625<br><br> <br><br>书目名称Neural Information Processing读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0663625<br><br> <br><br>书目名称Neural Information Processing读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0663625<br><br> <br><br>
violate
发表于 2025-3-21 23:57:33
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BANAL
发表于 2025-3-22 01:23:57
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condone
发表于 2025-3-22 08:06:15
https://doi.org/10.1007/978-981-99-8070-3pattern recognition; affective and cognitive learning; big data; bioinformatics; brain-machine interface
青春期
发表于 2025-3-22 08:57:28
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SIT
发表于 2025-3-22 15:56:55
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价值在贬值
发表于 2025-3-22 19:49:49
GRF-GMM: A Trajectory Optimization Framework for Obstacle Avoidance in Learning from Demonstrationaussian mixture model/Gaussian mixture regression (GMM/GMR) has been widely used for its robustness and effectiveness. However, there still exist many problems of GMM when an obstacle, which is not presented in original demonstrations, appears in the workspace of robots. To address these problems, t
声明
发表于 2025-3-23 00:23:51
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正常
发表于 2025-3-23 03:56:26
CrowdNav-HERO: Pedestrian Trajectory Prediction Based Crowded Navigation with Human-Environment-Roboonmental layout usually significantly impacts crowd distribution and robotic motion decision-making during crowded navigation. However, previous methods almost either learn and evaluate navigation strategies in unrealistic barrier-free settings or assume that expensive features like pedestrian speed
使人烦燥
发表于 2025-3-23 08:05:35
Modeling User’s Neutral Feedback in Conversational Recommendationns. Although CRS has shown success in generating recommendation lists based on user’s preferences, existing methods restrict users to make binary responses, i.e., accept and reject, after recommending, which limits users from expressing their needs. In fact, the user’s rejection feedback may contain