周兴旺
发表于 2025-3-23 09:52:33
http://reply.papertrans.cn/24/2331/233083/233083_11.png
冒烟
发表于 2025-3-23 15:35:58
Mention Recommendation with Context-Aware Probabilistic Matrix Factorizationa real-world dataset from Weibo, the empirically study demonstrates the effectiveness of discovered mention contextual factors. We also observe that topic relevance and mention affinity play a much significant role in the mention recommendation task. The results demonstrate our proposed method outpe
门窗的侧柱
发表于 2025-3-23 19:37:06
Nilanjan Ghosh,Sayanangshu Modakich acts like an encryption. Furthermore, we propose to utilize supervised adversarial training method to train a robust steganalyzer, which is utilized to discriminate whether an image contains secret information. Extensive experiments demonstrate the effectiveness of the proposed method on publicl
Audiometry
发表于 2025-3-24 02:17:00
http://reply.papertrans.cn/24/2331/233083/233083_14.png
Congestion
发表于 2025-3-24 04:20:28
Isela Martínez Fuentes,Rocío García Martínezthe number of their Nearest Neighbors as time progresses. We use an .-approximation scheme to implement the model of sliding window to compute Nearest Neighbors on the fly. We conduct widely experiments to examine our approach for time sensitive anomaly detection using three real-world data sets. Th
训诫
发表于 2025-3-24 09:08:38
http://reply.papertrans.cn/24/2331/233083/233083_16.png
visual-cortex
发表于 2025-3-24 14:02:44
http://reply.papertrans.cn/24/2331/233083/233083_17.png
消音器
发表于 2025-3-24 15:14:53
http://reply.papertrans.cn/24/2331/233083/233083_18.png
Wordlist
发表于 2025-3-24 19:22:54
Evgeny V. Konyshev,Anna K. Lutoshkina efficiently for multi-class classification. DunDi can not only build and train a new customized model but also support the incorporation of the available pre-trained neural network models to take full advantage of their capabilities. The results show that DunDi is able to defend 94.39% and 88.91% o
Frequency-Range
发表于 2025-3-25 01:24:06
https://doi.org/10.1007/978-981-15-9554-7y comparing the results for a function and pattern extrapolation task with those obtained using the nonlinear support vector machine (SVM) and a standard neural network (standard NN). Convergence behavior of stochastic gradient descent is discussed and the feasibility of the approach is demonstrated