archetype
发表于 2025-3-28 18:02:18
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medieval
发表于 2025-3-28 20:18:47
A Framework for Explainable NLP,y induced from a sample of . data points . =. by a learning algorithm. In linear classification, . is the set of all possible lines (hyperplanes), and the perceptron learning algorithm, for example, can be used to search for a good line h(•) by iteratively correcting the errors made by the current h
Indicative
发表于 2025-3-29 01:14:07
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遗留之物
发表于 2025-3-29 05:20:05
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青春期
发表于 2025-3-29 08:48:07
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大量
发表于 2025-3-29 12:42:27
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引起痛苦
发表于 2025-3-29 16:41:41
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无能力
发表于 2025-3-29 23:36:25
Local-Forward Explanations of Discrete Output, model is generally sensitive to mathematical symbols appearing in first positions in input sentences. To test this, what would you do? One obvious thing to do is to sample . sentences and design a test data set of (.+1) × . input examples, . examples for each of the original sentences. The number .
时间等
发表于 2025-3-30 00:08:38
Global-Forward Explanations of Discrete Output,comprehended holistically. Obviously, the faithfulness of . depends on the extent to which it agrees with h, e.g., its loss relative to ., .), .)). Some approaches explicitly minimize this loss through a process, sometimes referred to as . of . (Petrov et al., 2010). Related work trains a model . to
famine
发表于 2025-3-30 05:55:46
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