User-friendly Foundation Model Adapters for Multivariate Time Series
Classification
User-friendly Foundation Model Adapters for Multivariate Time Series
Classification
Foundation models, while highly effective, are often resource-intensive, requiring substantial inference time and memory. This paper addresses the challenge of making these models more accessible with limited computational resources by exploring dimensionality reduction techniques. Our goal is to enable users to run large pre-trained foundation models on standard GPUs without …