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pro vyhledávání: '"Lu, Shengyao"'
Large Language Models (LLMs) require precise alignment with complex instructions to optimize their performance in real-world applications. As the demand for refined instruction tuning data increases, traditional methods that evolve simple seed instru
Externí odkaz:
http://arxiv.org/abs/2410.02795
Publikováno v:
ICML 2024
Understanding and explaining the predictions of Graph Neural Networks (GNNs), is crucial for enhancing their safety and trustworthiness. Subgraph-level explanations are gaining attention for their intuitive appeal. However, most existing subgraph-lev
Externí odkaz:
http://arxiv.org/abs/2405.01762
Autor:
Mills, Keith G., Han, Fred X., Salameh, Mohammad, Lu, Shengyao, Zhou, Chunhua, He, Jiao, Sun, Fengyu, Niu, Di
Neural Architecture Search is a costly practice. The fact that a search space can span a vast number of design choices with each architecture evaluation taking nontrivial overhead makes it hard for an algorithm to sufficiently explore candidate netwo
Externí odkaz:
http://arxiv.org/abs/2403.13293
Understanding the decision-making process of Graph Neural Networks (GNNs) is crucial to their interpretability. Most existing methods for explaining GNNs typically rely on training auxiliary models, resulting in the explanations remain black-boxed. T
Externí odkaz:
http://arxiv.org/abs/2401.14578
Systematicity, i.e., the ability to recombine known parts and rules to form new sequences while reasoning over relational data, is critical to machine intelligence. A model with strong systematicity is able to train on small-scale tasks and generaliz
Externí odkaz:
http://arxiv.org/abs/2205.06454
Publikováno v:
In Life Sciences 15 August 2020 255
Akademický článek
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