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 ...
Tech Times

Baidu OCR Breaks Long-Document Memory Wall: New Architecture Beats DeepSeek

Open-source OCR from Baidu eliminates the GPU memory wall that limits long-document parsing. Unlimited OCR uses a constant KV ...
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 ...
GitHub

Full Sequence-to-Sequence Encoder-Decoder Transformer for Neural Machine Translation

This repository contains a mathematically precise implementation of the original "Attention Is All You Need" Transformer architecture. Built entirely from scratch, the model maps English source ...
GitHub

Whisfusion: Parallel ASR Decoding via a Diffusion Transformer

Official implementation of Whisfusion - the first Diffusion Transformer ASR framework that fuses a Whisper encoder with a diffusion decoder for faster, non-autoregressive transcription.
InfoQ

Gemma 4 12B Enables On-Device, Multimodal Agentic Workflows with an Encoder-free Architecture

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
Frontiers

DPCrossU-Net: a dual-branch parallel CNN–Transformer network for lung nodule segmentation

We propose DPCrossU-Net, a dual-branch parallel encoder–decoder network that integrates convolutional and Vision Transformer representations. The encoder employs parallel CNN and ViT branches with a ...