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- Erik CambriaSuggested from co-authorship
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Works10 from public data
- A comprehensive review on financial explainable AI151
A comparative survey of methods that aim to improve the explainability of deep learning models within the context of finance and describes the collection of explainable AI methods according to their corresponding characteristics.
- Self-training Large Language Models through Knowledge Detection19
A self-training paradigm, where the LLM autonomously curates its own labels and selectively trains on unknown data samples identified through a reference-free consistency method is explored, suggesting that such an approach can substantially reduce the dependency on large labeled datasets, paving the way for more scalable and cost-effective language model training.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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