- โExplainable AIยทLast edited by GISH on Dec 9, 2020Linked with GitHubContributed by
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NLP xai
https://arxiv.org/pdf/2010.00711.pdf 總結目前NLP相關paper使用的解釋方法有哪些 Explanation derivation -- 數學上的解釋 Feature importances Explanation presentation -- 如何呈現這些Explanation Visualization
Dec 24, 2020่ฎๆธๆๆ็จ่ฆๅ
閱讀書籍 - Interpretable Machine Learning A Guide for Making Black Box Models Explainable. 兩篇論文： https://arxiv.org/pdf/2001.02478.pdf https://arxiv.org/pdf/1909.03012.pdf 書籍網址 https://christophm.github.io/interpretable-ml-book/ 倚任副理建議直接先閱讀第五章和第六章 讀書計畫初稿如下表，從下週二開始分享，之後視情況可機動調整
Nov 26, 20206.4 Influential Instances
機器學習的模型是訓練數據的產出，刪除任一訓練數據會影響訓練結果。若刪除每一訓練數據對模型產生巨大影響，則稱這個點唯有影響的點(instance)。對有影響的點分析可以幫助我們檢視模型。 Deletion Diagnostics : delete the instance from the training data, retrain the model on the reduced training dataset and observe the difference in the model parameters or predictions Influenced functions : upweight a data instance by approximating the parameter changes based on the gradients of the model parameters. 6.4.1 Deletion Diagnostics DFBETA : 衡量移除某個instance對模型參數的影響。 $DFBETA_i = β-β^{-i}$ 適用於有參數的模型，如 logistic regression or neural networks.
Nov 6, 20206.1 Counterfactual Explanations
A counterfactual explanation of a prediction describes the smallest change to the feature values that changes the prediction to a predefined output. How do we define a good counterfactual explanation? counterfactual instance produces the predefined prediction as closely as possible counterfactual should be as similar as possible to the instance regarding feature values a counterfactual instance should have feature values that are likely
Oct 20, 2020
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