AI deciphers long-range DNA signals behind RNA splicing

Accurate RNA splicing is essential for gene expression and human health, yet predicting how DNA sequence variations affect ...
Tech Times

Open-Source Coding Model Ornith-1.0 Writes Its Own Training Scaffold in Reinforcement Learning

Open-source agentic coding model Ornith-1.0, released today under the MIT license, uses a self-improving reinforcement ...
Frontiers

Early detection of brain pathologies through deep learning and advanced neuroimaging approaches

Recent advances in the field of medical imaging and computational neuroscience have transformed the landscape of brain pathology detection. The application ...
Tech Times

DeepSeek V4 Architecture: How Sparse Attention Cuts Inference Costs, What NIST Found

DeepSeek V4 architecture uses sparse attention to cut inference costs 73% at one-million-token contexts, but a NIST ...
GitHub

SARMAE: Masked Autoencoder for SAR Representation Learning

Synthetic Aperture Radar (SAR) imagery plays a critical role in all-weather, day-and-night remote sensing applications. However, existing SAR-oriented deep learning is constrained by data scarcity, ...
IEEE

Infinite Gaussian Mixture Autoencoder for Adaptive Deep Unsupervised Clustering

Abstract: Clustering is a fundamental task in machine learning and data mining. The success of deep learning, especially deep generative models, has given birth to the next generation of clustering - ...
GitHub

graph-autoencoder

This project detects structural network anomalies using a GNN autoencoder. It contrasts this deep learning approach with the classic DBSCAN method. While DBSCAN only uses node features (CPU, RAM), the ...
Scientific Research Publishing

Traffic Speed Prediction Based on Autoencoder and Deep Learning ()

Traffic prediction is the core of intelligent transportation system, and accurate traffic speed prediction is the key to optimize traffic management. Currently, the traffic speed prediction model ...
IEEE

TDAE: Tensored Deep Autoencoder for Classification of Hyperspectral Images

Abstract: Deep learning has achieved outstanding success in the hyperspectral image (HSI) classification task. Almost all the current deep learning methods are used to conduct classification ...