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Titlebook: Neural Information Processing; 25th International C Long Cheng,Andrew Chi Sing Leung,Seiichi Ozawa Conference proceedings 2018 Springer Nat

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51#
發(fā)表于 2025-3-30 11:16:06 | 只看該作者
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發(fā)表于 2025-3-30 12:48:40 | 只看該作者
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發(fā)表于 2025-3-30 18:57:04 | 只看該作者
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發(fā)表于 2025-3-30 22:32:39 | 只看該作者
Estimation of Student Classroom Attention Using a Novel Measure of Head Motion Coherence on semantic events in the classroom (e.g., lecture slides), making it difficult to stably estimate student attention. In this article, we propose an index of students’ attention in the classroom based on head motion coherence among students. We evaluated this index using 40 students’ data recorded
55#
發(fā)表于 2025-3-31 01:13:08 | 只看該作者
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發(fā)表于 2025-3-31 08:52:17 | 只看該作者
Neural Network Collaborative Filtering for Group Recommendationlect the correlation among the members of the group. In this paper, group recommendation based on neural collaborative filtering (GNCF) and convolutional neural collaborative filtering (GCNCF) frameworks are proposed, which simulate the interaction between the members of the group and make recommend
57#
發(fā)表于 2025-3-31 11:17:26 | 只看該作者
Influence of Clustering on the Opinion Formation Dynamics in Online Social Networkslatform of social interactions and our growing exposure to others’ opinions instantly. When presented with neighbours’ opinions in OSNs, the natural clustering ability of human agents enables them to perceive the grouping of opinions formed in the neighbourhood. A group with similar opinions exhibit
58#
發(fā)表于 2025-3-31 13:53:12 | 只看該作者
59#
發(fā)表于 2025-3-31 19:03:11 | 只看該作者
Question Rewrite Based Dialogue Response Generationdevelopment in neural networks, the sequence to sequence (seq2seq) model which employed recurrent neural networks (RNN) encoder-decoder has archived great success in machine translation. Many researchers began to apply this model in dialogue response generation. However, the conventional seq2seq mod
60#
發(fā)表于 2025-3-31 23:03:26 | 只看該作者
A Lightweight Cloud Execution Stack for Neural Network Simulationn employ it by simple RESTful service calls. This service oriented approach allows easy and user-friendly importing, training and evaluating of arbitrary neural network models. This work is influenced by N2Sky, a framework for the exchange of neural network specific knowledge and is based on ViNNSL,
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