Unlike other deep learning (DL) models, Transformer has the ability to extract long-range dependency features from hyperspectral image (HSI) data. Masked autoencoder (MAE), which is based on ...

Neural networks first treat sentences like puzzles solved by word order, but once they read enough, a tipping point sends ...

The language capabilities of today's artificial intelligence systems are astonishing. We can now engage in natural ...

Figure 3. Vision Transformer architecture, processing signal inputs as tokenized patches [3]. Figure 4. MAE-EEG-Transformer, integrating masked autoencoder pretraining for EEG classification [26]. 2.4 ...

We also develop an efficient spike-driven Transformer architecture and a spike-masked autoencoder to prevent performance degradation during SNN scaling. On ImageNet-1k, we achieve state-of-the-art top ...

Features Tech Culture Meet Transformers: The Google Breakthrough that Rewrote AI's Roadmap How Attention Replaced Recurrence and Changed the Rules of AI By Julio Franco December 24, 2024 ...

We also develop an efficient spike-driven Transformer architecture and a spike-masked autoencoder to prevent performance degradation during SNN scaling. On ImageNet-1k, we achieve state-of-the-art top ...

Unlike other deep learning (DL) models, Transformer has the ability to extract long-range dependency features from hyperspectral image (HSI) data. Masked autoencoder (MAE), which is based on ...