Meta’s Brain2Qwerty v2 offers a breakthrough non-invasive brain-to-text AI model with 61% word accuracy, challenging ...

Abstract: U-shaped encoder-decoder models have excelled in automatic medical image segmentation due to their hierarchical feature learning capabilities, robustness, and upgradability. Purely CNN-based ...

Abstract: Transformer-based methods are recently popular in vision tasks because of their capability to model global dependencies alone. However, it limits the performance of networks due to the lack ...

What is Covert Speech Decoding? Covert speech (imagined speech) is the internal experience of articulating words silently — thinking in language without any audible output or visible movement.

These local LLMs are changing the game in lots of fun ways.

Speculative decoding can help AI chatbots improve throughput and reduce hardware demand by using a smaller model to draft tokens that a larger model validates.

Reimplement the original encoder-decoder Transformer end to end in PyTorch, from token vocabularies and sinusoidal positional encodings through multi-head attention, label smoothing, Noam scheduling, ...

To address this challenge, this paper proposes the Graph Attention Enhanced Scale-Aware Inverted Transformer (GAESA-iFormer), a novel surrogate modeling framework integrating a Graph Attention Network ...