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Titlebook: KI 2019: Advances in Artificial Intelligence; 42nd German Conferen Christoph Benzmüller,Heiner Stuckenschmidt Conference proceedings 2019 S

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樓主: 萬靈藥
21#
發(fā)表于 2025-3-25 03:22:13 | 只看該作者
22#
發(fā)表于 2025-3-25 08:25:11 | 只看該作者
23#
發(fā)表于 2025-3-25 14:06:33 | 只看該作者
Clustering of Argument Graphs Using Semantic Similarity Measures,aph-based semantic measure that takes the structure of the argument into account with a semantic word2vec measure on the pure textual argument representation. Experiments based on the Microtext corpus show that the graph-based similarity is best on internal evaluation measures, while the pure textua
24#
發(fā)表于 2025-3-25 17:48:53 | 只看該作者
25#
發(fā)表于 2025-3-25 21:38:36 | 只看該作者
26#
發(fā)表于 2025-3-26 03:44:49 | 只看該作者
Enhancing Explainability of Deep Reinforcement Learning Through Selective Layer-Wise Relevance Propizes only the most relevant neurons of each convolutional layer and thus generates more selective saliency maps. We test our approach with a dueling Deep Q-Network (DQN) agent which we trained on three different Atari games of varying complexity. Since the dueling DQN approach considerably alters th
27#
發(fā)表于 2025-3-26 06:18:27 | 只看該作者
28#
發(fā)表于 2025-3-26 10:37:41 | 只看該作者
Gaussian Lifted Marginal Filtering,valuate our approach on an activity prediction task in an online multiplayer game. The results suggest that compared to the conventional approach, where all entities are handled individually, decrease in prediction accuracy is small, while inference runtime decreases significantly.
29#
發(fā)表于 2025-3-26 14:59:31 | 只看該作者
30#
發(fā)表于 2025-3-26 19:44:45 | 只看該作者
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