Robust training under label noise by over-parameterization

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[HTML][HTML] A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics

K He, R Mao, Q Lin, Y Ruan, X Lan, M Feng… - Information …, 2025 - Elsevier
The utilization of large language models (LLMs) for Healthcare has generated both
excitement and concern due to their ability to effectively respond to free-text queries with …
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In-context vectors: Making in context learning more effective and controllable through latent space steering

S Liu, H Ye, L ** the model prediction
H Wei, H Zhuang, R **e, L Feng… - International …, 2023 - proceedings.mlr.press
In the presence of noisy labels, designing robust loss functions is critical for securing the
generalization performance of deep neural networks. Cross Entropy (CE) loss has been …
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Can neural network memorization be localized?

P Maini, MC Mozer, H Sedghi, ZC Lipton… - arxiv preprint arxiv …, 2023 - arxiv.org
Recent efforts at explaining the interplay of memorization and generalization in deep
overparametrized networks have posited that neural networks $\textit {memorize} $" hard" …
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Learning from AI-generated annotations for medical image segmentation

Y Song, Y Liu, Z Lin, J Zhou, D Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Learning from AI-generated annotations is wellrecognized as a key advance of deep
learning techniques in medical image segmentation. Towards this direction, in this paper …
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