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Found 6 papers, 1 papers with code
Evaluating Tree Explanation Methods for Anomaly Reasoning: A Case Study of SHAP TreeExplainer and TreeInterpreter
Understanding predictions made by Machine Learning models is critical in many applications.
Examination and Extension of Strategies for Improving Personalized Language Modeling via Interpolation
In this paper, we detail novel strategies for interpolating personalized language models and methods to handle out-of-vocabulary (OOV) tokens to improve personalized language models.
Griffon: Reasoning about Job Anomalies with Unlabeled Data in Cloud-based Platforms
In contrast, in Griffin we cast the problem to a corresponding regression one that predicts the runtime of a job, and show how the relative contributions of the features used to train our interpretable model can be exploited to rank the potential causes of job slowdowns.
Generating an Overview Report over Many Documents
To overcome this obstacle, we present NDORGS (Numerous Documents' Overview Report Generation Scheme) that integrates text filtering, keyword scoring, single-document summarization (SDS), topic modeling, MDS, and title generation to generate a coherent, well-structured ORPT.
DTATG: An Automatic Title Generator based on Dependency Trees
We study automatic title generation for a given block of text and present a method called DTATG to generate titles.
Efficient and Effective Single-Document Summarizations and A Word-Embedding Measurement of Quality
We show that the orderings of the ROUGE and WESM scores of our algorithms are highly comparable, suggesting that WESM may serve as a viable alternative for measuring the quality of a summary.
