Scientists have used DNA's self-assembling properties to engineer intricate moiré superlattices at the nanometer ...

Students often train large language models (LLMs) as part of a group. In that case, your group should implement robust access ...

Anomaly types that are difficult to identify with basic statistical methods, especially those with strong correlation and complex patterns, this study designs a time series analysis model based on ...

Tuli et al. (2022) successfully developed deep Transformer networks by attention-based sequence encoders, to solve the problem of anomaly detection of multivariate time series data in modern ...

In recent works on semantic segmentation, there has been a significant focus on designing and integrating transformer-based encoders. However, less attention has been given to transformer-based ...

This comprehensive guide delves into decoder-based Large Language Models (LLMs), exploring their architecture, innovations, and applications in natural language processing. Highlighting the evolution ...

Conversely, transformers inhabit a co-product completion of the category, constituting a topos. This distinction implies that the internal language of the transformer possesses a higher-order richness ...

Transformer-based models are a type of neural network architecture that has revolutionised natural language processing in recent years.