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Titlebook: Neural Information Processing; 29th International C Mohammad Tanveer,Sonali Agarwal,Adam Jatowt Conference proceedings 2023 The Editor(s) (

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21#
發(fā)表于 2025-3-25 05:59:57 | 只看該作者
ASLEEP: A Shallow neural modEl for knowlEdge graph comPletionletion is knowledge graph embedding. However, existing embedding methods usually focus on combined models, variant deep neural networks, or additional information, which inevitably increase computational complexity and are unfriendly to real-time applications. In this paper, we take a step back and
22#
發(fā)表于 2025-3-25 07:35:09 | 只看該作者
23#
發(fā)表于 2025-3-25 15:02:11 | 只看該作者
24#
發(fā)表于 2025-3-25 16:05:12 | 只看該作者
25#
發(fā)表于 2025-3-25 21:05:33 | 只看該作者
Disentangling Exploration and?Exploitation in?Deep Reinforcement Learning Using Contingency Awareneshypothesis on hard exploration games from the Atari 2600 platform through The Arcade Learning Environment. We develop a neural network architecture that separately models the extrinsic and intrinsic rewards, showing that it leads to more stable learning. Separately modelling the rewards leads to res
26#
發(fā)表于 2025-3-26 01:55:32 | 只看該作者
27#
發(fā)表于 2025-3-26 08:14:02 | 只看該作者
28#
發(fā)表于 2025-3-26 11:30:30 | 只看該作者
UPFP-growth++: An Efficient Algorithm to?Find Periodic-Frequent Patterns in?Uncertain Temporal Databy challenging due to its enormous search space of . where . represents the number of items (or objects) in a database. Previous studies tried to tackle this problem using some upper-bound constraints. We have observed that these constraints were not tight enough and there exists a possibility to red
29#
發(fā)表于 2025-3-26 15:38:12 | 只看該作者
30#
發(fā)表于 2025-3-26 19:55:44 | 只看該作者
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