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Found 6 papers, 2 papers with code
Lights, Camera, Action! A Framework to Improve NLP Accuracy over OCR documents
Document digitization is essential for the digital transformation of our societies, yet a crucial step in the process, Optical Character Recognition (OCR), is still not perfect.
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.
Model adaptation and unsupervised learning with non-stationary batch data under smooth concept drift
In this paper, we consider the scenario of a gradual concept drift due to the underlying non-stationarity of the data source.
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.
Dealing with Class Imbalance using Thresholding
In advanced manufacturing units, where the manufacturing process has matured over time, the number of instances (or parts) of the product that need to be rejected (based on a strict regime of quality tests) becomes relatively rare and are defined as outliers.
