Manifold-Constrained Hyper-Connections: The Architectural Breakthrough That Might Redefine LLM Training

If mHC scales the way early benchmarks suggest, it could reshape how we think about model capacity, compute budgets and the ...

AI deep denoiser can remove clouds from satellite images

Thick cloud cover can completely obscure the surface of the Earth from satellite view, while thinner haze and shadows distort ...

AI artist Refik Anadol uses massive datasets and AI to create immersive works shown around the world

Art made with AI is selling for over $1 million and being embraced by some of the world's most prestigious museums, but critics question if it really belongs in those spaces.The art world is divided.
IEEE

Enhanced Feature Contrastive Self-Supervised Learning Method Based on Deep Unfolding POCS for Accelerated Parallel MRI Reconstruction

Abstract: The application of deep learning has significantly accelerated magnetic resonance imaging (MRI). However, these methods encounter substantial challenges when fully sampled datasets are ...
Beebom

What is the Difference Between Deep Learning and Machine Learning

Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...
Beebom

What is Deep Learning in AI? Understanding How AI Really Works

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...
The New York Times

What Scientists Found When a Deep Sea Mining Company Invited Them In

An ocean-mining company has funded some of the most comprehensive scientific studies of the deep seabed to date, and peer-reviewed results have begun to emerge. A collage of foraminifera, a kind of ...
IEEE

Hierarchical and Bidirectional Contrastive Learning for Hyperspectral Image Classification

Abstract: Representing hyperspectral images (HSIs) is a complex and challenging task, primarily due to spectral uncertainty. The learnable prototypical contrastive learning is specialized in ...