Feed Forward Network (FFN) block structure within a standard Transformer model.

To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models.

In this section, we will describe the evaluation process in detail. We evaluated various L VLMs, including medical-specific models, open-source general models, and closed-source API general models.

The key challenge is that the existing literature has only focused on designing algorithms with minimalcomputation, ignoring the fact that the practical latency can also be influenced byscheduling strategiesand hardware properties.

Our method incorporates three novel mechanisms to leverage the unique characteristics of MedTS: cross-channel patching to leverage inter-channel correlations, multi-granularity embedding for capturing features at different scales, and two-stage (intra-and inter-granularity) multi-granularity self-attention for learning features and correlations within and among granularities. We conduct extensive experiments on five public datasets under both subject-dependent and challenging subject-independent setups. Results demonstrate Medformer's superiority over 10 baselines, achieving top averaged ranking across five datasets on all six evaluation metrics. These findings underscore the significant impact of our method on healthcare applications, such as diagnosing Myocardial Infarction, Alzheimer's, and Parkinson's disease.