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of 37 859
pro vyhledávání: '"scarce data"'
Autor:
Kumar, Vivek, Ntoutsi, Eirini, Rajawat, Pushpraj Singh, Medda, Giacomo, Recupero, Diego Reforgiato
Large language models (LLMs) have shown promising capabilities in healthcare analysis but face several challenges like hallucinations, parroting, and bias manifestation. These challenges are exacerbated in complex, sensitive, and low-resource domains
Externí odkaz:
http://arxiv.org/abs/2412.12981
Retrieval augmented generation (RAG) pipelines are commonly used in tasks such as question-answering (QA), relying on retrieving relevant documents from a vector store computed using a pretrained embedding model. However, if the retrieved context is
Externí odkaz:
http://arxiv.org/abs/2410.12890
Autor:
Ganatra, Vaibhav, Goel, Drishti
Deep learning models used for medical image classification tasks are often constrained by the limited amount of training data along with severe class imbalance. Despite these problems, models should be explainable to enable human trust in the models'
Externí odkaz:
http://arxiv.org/abs/2408.05754
Autor:
Wen, Ximing, Weber, Rosina O., Sen, Anik, Hannan, Darryl, Nesbit, Steven C., Chan, Vincent, Goffi, Alberto, Morris, Michael, Hunninghake, John C., Villalobos, Nicholas E., Kim, Edward, MacLellan, Christopher J.
Point-of-Care Ultrasound (POCUS) is the practice of clinicians conducting and interpreting ultrasound scans right at the patient's bedside. However, the expertise needed to interpret these images is considerable and may not always be present in emerg
Externí odkaz:
http://arxiv.org/abs/2407.06206
Advances in Deep Learning bring further investigation into credibility and robustness, especially for safety-critical engineering applications such as the nuclear industry. The key challenges include the availability of data set (often scarce and spa
Externí odkaz:
http://arxiv.org/abs/2405.17862
Machine learning (ML) methods, which fit to data the parameters of a given parameterized model class, have garnered significant interest as potential methods for learning surrogate models for complex engineering systems for which traditional simulati
Externí odkaz:
http://arxiv.org/abs/2403.08627
High-fidelity full-field micro-mechanical modeling of the non-linear path-dependent materials demands a substantial computational effort. Recent trends in the field incorporates data-driven Artificial Neural Networks (ANNs) as surrogate models. Howev
Externí odkaz:
http://arxiv.org/abs/2311.14557
The understanding of the convoluted evolution of infant brain networks during the first postnatal year is pivotal for identifying the dynamics of early brain connectivity development. Existing deep learning solutions suffer from three major limitatio
Externí odkaz:
http://arxiv.org/abs/2401.01383
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