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pro vyhledávání: '"Rabus, Maximilian"'
Assessing the importance of individual training samples is a key challenge in machine learning. Traditional approaches retrain models with and without specific samples, which is computationally expensive and ignores dependencies between data points.
Externí odkaz:
http://arxiv.org/abs/2412.04158
Pairwise difference learning (PDL) has recently been introduced as a new meta-learning technique for regression. Instead of learning a mapping from instances to outcomes in the standard way, the key idea is to learn a function that takes two instance
Externí odkaz:
http://arxiv.org/abs/2406.20031
With the rapid growth of data availability and usage, quantifying the added value of each training data point has become a crucial process in the field of artificial intelligence. The Shapley values have been recognized as an effective method for dat
Externí odkaz:
http://arxiv.org/abs/2304.01224
In recent years, Explainable AI (xAI) attracted a lot of attention as various countries turned explanations into a legal right. xAI allows for improving models beyond the accuracy metric by, e.g., debugging the learned pattern and demystifying the AI
Externí odkaz:
http://arxiv.org/abs/2207.14160
Publikováno v:
Automotive & Engine Technology; Dec2022, Vol. 7 Issue 3/4, p229-244, 16p
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