Tag: transformers

Researchers have integrated transformer attention mechanisms into learned primal-dual PET reconstruction, achieving cleaner images from noisy low-count scanner data.

The CMS Collaboration has demonstrated the first machine-learning-based full-event reconstruction applied to hadron collider data, achieving better jet energy resolution ...

A new convolutional architecture called MRTFNet combines multi-resolution temporal embeddings with frequency-domain modeling to outperform Transformer-based models on most long-term ...

Researchers show that temporally correlated input noise, not model capacity, drives unstable long-horizon forecasts, and introduce NoDE, a lightweight embedding ...

A systematic study finds that large language models consistently fail to learn spatial coordinate information in materials property prediction, with ...

A hybrid systematic review and experimental benchmark of five deep learning architectures reveals which neural networks can realistically detect and ...

A systematic review of 38 studies finds that protein language models excel on benchmarks but largely lack experimental validation, prompting ...

Researchers have built a multilingual transformer-based AI system that diagnoses crop diseases, generates farming advice, and delivers market intelligence while ...

A new Nature Machine Intelligence perspective argues that the reasoning tokens produced by large language models should be understood as ...

Researchers in India have built a four-part neural system that uses contrastive learning, graph refinement, and reinforcement learning to find ...