脱离
发表于 2025-3-25 03:25:56
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污点
发表于 2025-3-25 07:54:07
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希望
发表于 2025-3-25 11:51:15
Dynamics Adaptive Safe Reinforcement Learning with a Misspecified Simulatortraditional methods. Subsequently, DASaR aligns the estimated value functions in the simulator and the real-world environment via inverse dynamics-based relabeling of reward and cost signals. Furthermore, to deal with the underestimation of cost value functions, DASaR employs uncertainty estimation
群居男女
发表于 2025-3-25 19:07:01
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consent
发表于 2025-3-25 23:38:55
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crescendo
发表于 2025-3-26 02:54:10
FairFlow: An Automated Approach to Model-Based Counterfactual Data Augmentation for NLP paper proposes FairFlow, an automated approach to generating parallel data for training counterfactual text generator models that limits the need for human intervention. Furthermore, we show that FairFlow significantly overcomes the limitations of dictionary-based word-substitution approaches whils
助记
发表于 2025-3-26 05:56:13
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不要不诚实
发表于 2025-3-26 12:16:29
MEGA: Multi-encoder GNN Architecture for Stronger Task Collaboration and Generalizationng of each task. This architecture allows for independent learning from multiple pretext tasks, followed by a simple self-supervised dimensionality reduction technique to combine the insights gleaned. Through extensive experiments, we demonstrate the superiority of our approach, showcasing an averag
Estrogen
发表于 2025-3-26 15:01:41
MetaQuRe: Meta-learning from Model Quality and Resource Consumptionurce consumption of models evaluated across hundreds of data sets and four execution environments. We use this data to put our methodology into practice and conduct an in-depth analysis of how our approach and data set can help in making AutoML more resource-aware, which represents our third contrib
Fracture
发表于 2025-3-26 20:03:39
Propagation Structure-Semantic Transfer Learning for Robust Fake News Detectiontion under a teacher-student architecture. Specifically, we design dual teacher models to learn semantics knowledge and structure knowledge from noisy news content and propagation structure independently. Besides, we design a Multi-channel Knowledge Distillation (MKD) loss to enable the student mode