IEEE

Deep Quasi-Recurrent Self-Attention With Dual Encoder-Decoder in Biomedical CT Image Segmentation

Abstract: Developing deep learning models for accurate segmentation of biomedical CT images is challenging due to their complex structures, anatomy variations, noise, and unavailability of sufficient ...
marktechpost

Google Introduces T5Gemma 2: Encoder Decoder Models with Multimodal Inputs via SigLIP and 128K Context

T5Gemma 2 follows the same adaptation idea introduced in T5Gemma, initialize an encoder-decoder model from a decoder-only checkpoint, then adapt with UL2. In the above figure the research team show ...
IEEE

Encoder-Decoder Based Deep Reinforcement Learning for Multi-AUV Assisted Data Collection in UWSNs

Abstract: Reliable and timely data collection poses a significant challenge for underwater wireless sensor networks (UWSNs), primarily due to the extremely low data rate of underwater communication ...
Microsoft

Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR1 Imaging

Purpose: To propose a flexible and scalable imaging transformer (IT) architecture with three attention modules for multi-dimensional imaging data and apply it to MRI denoising with very low input SNR.