Deep learning based neural decoding from stereotactic electroencephalography (sEEG) would likely benefit from scaling up both dataset and model size. To achieve this, combining data across multiple subjects is crucial.

To mitigate this issue, several normalization methods (e.g., SAN) have recently been specifically designed by normalization in

While optimization-based methods boast gen-eralizability across modalities and robust performance, learning-based methods promise peak performance, incorporating weak supervision and amortized optimization. However, the exact conditions for either paradigm to perform well over the other are shrouded and not explicitly outlined in the existing literature.

However, the field has long faced the "elephant in the room:" critical

Existing VLN models are optimized through expert demonstrations by supervised behavioural cloning or incorporating manual reward engineering.

Image description datasets play a crucial role in the advancement of various applications such as image understanding, text-to-image generation, and text-image retrieval. Currently, image description datasets primarily originate from two sources. One source is the scraping of image-text pairs from the web. Despite their abundance, these descriptions are often of low quality and noisy.

The web has become the primary communal source of data, or "data commons", for general-purpose