Orthosis 发表于 2025-3-21 17:44:59
书目名称Neural Information Processing影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0663649<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=BK0663649<br><br> <br><br>书目名称Neural Information Processing网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0663649<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=BK0663649<br><br> <br><br>书目名称Neural Information Processing被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0663649<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=BK0663649<br><br> <br><br>书目名称Neural Information Processing年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0663649<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=BK0663649<br><br> <br><br>书目名称Neural Information Processing读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0663649<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=BK0663649<br><br> <br><br>GRE 发表于 2025-3-21 23:48:00
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A Just-In-Time Compilation Approach for Neural Dynamics Simulationchers in different disciplines. However, the current neural simulators based on low-level language programming or pseudo-programming using high-level descriptive language can not full fill users’ basic requirements, including easy-to-learn-and-use, high flexibility, good transparency, and high-speed不透明 发表于 2025-3-22 19:29:46
STCN-GR: Spatial-Temporal Convolutional Networks for Surface-Electromyography-Based Gesture Recognit, which aims to classify gestures according to signals obtained from human hands. Since sEMG signals are characterized by spatial relevancy and temporal nonstationarity, sEMG-based gesture recognition is a challenging task. Previous works attempt to model this structured information and extract spat推迟 发表于 2025-3-22 22:19:25
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A Reinforcement Learning Approach for Abductive Natural Language GenerationCommonsense Reasoning (.) is a benchmark that investigates model’s ability on inferencing the most plausible explanation within the given context, which requires model using commonsense knowledge about the world. . consists of two datasets, . and ., that challenge models from . and . settings respec