Zobrazeno 1 - 4
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pro vyhledávání: '"Manousakas, Dionysis"'
Autor:
Leemann, Tobias, Petridis, Periklis, Vietri, Giuseppe, Manousakas, Dionysis, Roth, Aaron, Aydore, Sergul
While retrieval augmented generation (RAG) has been shown to enhance factuality of large language model (LLM) outputs, LLMs still suffer from hallucination, generating incorrect or irrelevant information. One common detection strategy involves prompt
Externí odkaz:
http://arxiv.org/abs/2410.03461
Autor:
Manousakas, Dionysis, Aydöre, Sergül
Despite recent advances in synthetic data generation, the scientific community still lacks a unified consensus on its usefulness. It is commonly believed that synthetic data can be used for both data exchange and boosting machine learning (ML) traini
Externí odkaz:
http://arxiv.org/abs/2306.15636
Recent advances in coreset methods have shown that a selection of representative datapoints can replace massive volumes of data for Bayesian inference, preserving the relevant statistical information and significantly accelerating subsequent downstre
Externí odkaz:
http://arxiv.org/abs/2211.02377
Autor:
Manousakas, Dionysis, Mascolo, Cecilia
Modern machine learning applications should be able to address the intrinsic challenges arising over inference on massive real-world datasets, including scalability and robustness to outliers. Despite the multiple benefits of Bayesian methods (such a
Externí odkaz:
http://arxiv.org/abs/2008.13600