Modality-agnostic decoders leverage modality-invariant representations in human subjects' brain activity to predict stimuli irrespective of their modality (image, text, mental imagery).

Abstract: Capitalizing on image-level pre-trained models for various downstream tasks has recently emerged with promising performance. However, the paradigm of “image pre-training followed by video ...

Abstract: This paper explores human activity recognition (HAR) using machine learning models to classify activities based on sensor data with high precision. The study leverages the UCI HAR dataset, ...