Abstract: Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the ...

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 ...

Background: Artificial intelligence (AI) can diagnose a wide array of cardiac conditions from electrocardiograms (ECGs). Wearable and portable ECG devices may enable expanded AI-based screening for ...

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 ...

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?

Encoder models like BERT and RoBERTa have long been cornerstones of natural language processing (NLP), powering tasks such as text classification, retrieval, and toxicity detection. However, while ...

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Abstract: Accurate time series forecasting is critical in a variety of fields, including transportation, weather prediction, energy management, infrastructure monitoring, and finance. Forecasting ...

I am currently working on a project involving model retrieval, and I plan to use a bi-encoder + cross-encoder approach for retrieval. However, I have encountered an issue while training the ...