The Financial Express

How Mark Zuckerberg’s brain-to-text AI model promises to outshine Elon Musk’s Neuralink

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

Temporal Convolutional and Fusional Transformer Model With Bi-LSTM Encoder-Decoder for Multi-Time-Window Remaining Useful Life Prediction

Abstract: Health prediction is crucial for ensuring reliability, minimizing downtime, and optimizing maintenance in industrial systems. Remaining Useful Life (RUL) prediction is a key component of ...
IEEE

MaS-TransUNet: A Multiattention Swin Transformer U-Net for Medical Image Segmentation

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 ...
Virtualization Review

Using Speculative Decoding to Improve Chatbot Performance

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.
GitHub

HanhaiNotHai/attention-is-all-you-need-build-the-transformer-from-scratch

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, ...
GitHub

Satyam1942/attention-is-all-you-need-build-the-transformer-from-scratch

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, ...
Frontiers

GAESA-iFormer: A Graph Attention-Enhanced Scale-Aware Inverted Transformer for Performance Characterization of Aircraft Engine Compressors

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 ...