我没有辱骂
发表于 2025-3-21 16:07:21
书目名称Artificial Neural Networks and Machine Learning – ICANN 2022影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0162657<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2022影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0162657<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2022网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0162657<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2022网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0162657<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2022被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0162657<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2022被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0162657<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2022年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0162657<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2022年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0162657<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2022读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0162657<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2022读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0162657<br><br> <br><br>
一窝小鸟
发表于 2025-3-21 21:33:35
,A Unified Multiple Inducible Co-attentions and Edge Guidance Network for Co-saliency Detection,oring the inter-image co-attention are two challenges. In this paper, we propose a unified Multiple INducible co-attentions and Edge guidance network (MineNet) for CoSOD. Firstly, a classified inducible co-attention (CICA) is designed to model the classification interactions from a group of images.
accomplishment
发表于 2025-3-22 04:15:48
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改正
发表于 2025-3-22 06:49:20
,Boosting Both Robustness and Hardware Efficiency via Random Pruning Mask Selection, computation, which greatly hinders DNNs’ deployment on safety-critical yet resource-limited platforms. Although researchers have proposed adversary-aware pruning methods where adversarial training and network pruning are studied jointly to improve the robustness of pruned networks, they failed to a
maintenance
发表于 2025-3-22 11:42:19
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虚弱
发表于 2025-3-22 14:43:05
,CLTS+: A New Chinese Long Text Summarization Dataset with Abstractive Summaries,ly extracted from the source articles. One of the main causes for this problem is the lack of dataset with ., especially for Chinese. In order to solve this problem, we paraphrase the reference summaries in CLTS, the .hinese .ong .ext .ummarization dataset, correct errors of factual inconsistencies,
考得
发表于 2025-3-22 19:18:27
Correlation-Based Transformer Tracking, which is responsible for calculating similarity plays an important role in the development of Siamese tracking. However, the fact that general cross-correlation is a local operation leads to the lack of global contextual information. Although introducing transformer into tracking seems helpful to g
grandiose
发表于 2025-3-22 21:22:32
,Deep Graph and Sequence Representation Learning for Drug Response Prediction,g response prediction. However, these methods only represent drugs as strings or represent drugs as molecular graphs, failing to capture comprehensive information about drugs. To address this challenge, we propose a joint graph and sequence representation learning model for drug response prediction,
cuticle
发表于 2025-3-23 02:49:57
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可用
发表于 2025-3-23 09:00:39
,DuSAG: An Anomaly Detection Method in Dynamic Graph Based on Dual Self-attention,ds of dynamic graph based on random walk did not focus on the important vertices in random walks and did not utilize previous states of vertices, and hence, the extracted structural and temporal features are limited. This paper introduces DuSAG which is a dual self-attention anomaly detection algori