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To build a self-supervised magnetic resonance imaging (MRI) foundation model from routine clinical scans and to test whether it can support key glioma-related applications, including post-therapy ...
We cross-validated four pretrained Bidirectional Encoder Representations from Transformers (BERT)–based models—BERT, BioBERT, ClinicalBERT, and MedBERT—by fine-tuning them on 90% of 3,261 sentences ...
Abstract: The existing deep-learning based robust watermarking model generally applies a discriminator to form generative adversarial network (GAN) for increasing the quality of encoded images, and ...
Why was a new multilingual encoder needed? XLM-RoBERTa (XLM-R) has dominated multilingual NLP for more than 5 years, an unusually long reign in AI research. While encoder-only models like BERT and ...
1 Faculty of Technology and Electrical Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. 2 Academy of Language Studies, Universiti Teknologi MARA Kampus Seremban, Negeri Sembilan, ...
Abstract: In this letter, we propose a deep learning-based iterative residual encoder-decoder method (IRED), which provides an efficient deep learning framework for electromagnetic modeling over a ...
I tried to use vjepa2_vit_large model to do inference. Although the scale of parameters is about 300M, the memory consumption is about 40GB. I wonder why it is so large and can you optimize this part?
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