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 ...
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, ...
Nature

Deep learning-based high-speed railway communication systems

High-speed railway wireless communication systems are characterized by severe Doppler shifts and fast time-varying multipath, which challenge reliable connectivity in Long-Term Evolution for Railways ...
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 ...
Nature

Ensemble of feature augmented convolutional neural network and deep autoencoder for efficient detection of network attacks

An Intrusion Detection System (IDS) is a type of device that continuously observes system behaviour in promiscuous mode in order to collect network data for further analysis. The NIDS is an essential ...
LinkedIn

DeepSig Autoencoders And Meta-learning systems like DNDR (Deep Neural Decoder with Reinforcement): A Deep Dive

DeepSig employs deep learning-based autoencoders to revolutionize communication system design by optimizing both encoding and decoding processes in an end-to-end manner. This fundamentally departs ...
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 ...
syncedreview

Open Sparse Autoencoders Everywhere: The Ambitious Vision of DeepMind’s Gemma Scope

Sparse autoencoders (SAEs) are an unsupervised learning technique designed to decompose a neural network’s latent representations into sparse, seemingly interpretable features. While these models have ...