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Bringing Quantum Algorithms to Automated Machine Learning: A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms
This work describes the selection approach and analysis of existing AutoML frameworks regarding their capability of a) incorporating Quantum Machine Learning (QML) algorithms into this automated solving approach of the AutoML framing and b) solving a set of industrial use-cases with different ML problem types by benchmarking their most important characteristics.
Fine-tuning and aligning question answering models for complex information extraction tasks
The emergence of Large Language Models (LLMs) has boosted performance and possibilities in various NLP tasks.
Benchmarking Automated Machine Learning Methods for Price Forecasting Applications
It is thus of high interest to automate the forecasting process based on current market data.
Combining Deep Learning and Reasoning for Address Detection in Unstructured Text Documents
Extracting information from unstructured text documents is a demanding task, since these documents can have a broad variety of different layouts and a non-trivial reading order, like it is the case for multi-column documents or nested tables.
