GitHub

Deep Reinforcement Learning for Service Function Chain Placement with Graph Attention and Transformer Encoder

This repository contains the implementation of the paper of paper Deep Reinforcement Learning for Service Function Chain Placement with Graph Attention and Transformer Encoder. In this paper, we ...
Frontiers

A combined approach to lithology identification using reinforcement learning and transformer algorithms

Lithology identification plays a pivotal role in logging interpretation during drilling operations, directly influencing drilling decisions and efficiency. Conventional lithology identification ...
Scientific Research Publishing

Chen, H., Zhang, Y., Kalra, M.K., Lin, F., Chen, Y., Liao, P., et al. (2017) Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network. IEEE Transactions on ...

ABSTRACT: Magnetic Resonance Imaging (MRI) is commonly applied to clinical diagnostics owing to its high soft-tissue contrast and lack of invasiveness. However, its sensitivity to noise, attributable ...
GitHub

utrobinmv/dinov3-finetune_experiments

This repository explores finetuning DINOv3 (Siméoni et al., 2025) or DINOv2 (Oquab et al., 2024) encoder weights using Low-Rank Adaptation (Hu et al., 2021) (LoRA) and a simple 1x1 convolution decoder ...
IEEE

Deep Learning-Based Iterative Residual Encoder-Decoder Solution for Electromagnetic Modeling over a Broad Frequency Band

Abstract: In this letter, we propose a deep learning-based iterative residual encoder-decoder method (IRED), which provides an efficient deep learning framework for electromagnetic modeling over a ...
Scientific Research Publishing

Feng, L., Zhao, C. and Sun, Y. (2021) Dual Attention-Based Encoder-Decoder: A Customized Sequence-to-Sequence Learning for Soft Sensor Development. IEEE Transactions on Neural ...

ABSTRACT: In this paper, a novel multilingual OCR (Optical Character Recognition) method for scanned papers is provided. Current open-source solutions, like Tesseract, offer extremely high accuracy ...
ahajournals.org

Abstract P3089: Predicting Reductions In Hemoglobin A1c From Large Dietary Datasets Using Artificial Intelligence Pipeline With Interpretable Encoder-Decoder Deep Neural Networks

Introduction: Artificial intelligence algorithms can help understand and predict the complex interactions between dietary intake and health outcomes, especially from large datasets. Precision ...