Viruses and their mammalian hosts have co-evolved for millions of years, resulting in intricate host–pathogen interactions. As a result viruses have evolved several mechanisms to 'hide' from the host ...

DeepSeek’s latest technical paper, co-authored by the firm’s founder and CEO Liang Wenfeng, has been cited as a potential game changer in developing artificial intelligence models, as it could ...

DeepSeek’s proposed “mHC” architecture could transform the training of large language models (LLMs) – the technology behind artificial intelligence chatbots – as developers look for ways to scale ...

The major histocompatibility complex (MHC) is a cluster of genes encoding molecules that are important for lymphocyte activation. MHC molecules bind small fragments of proteins from inside a cell and ...

DeepSeek debuted Manifold-Constrained Hyper-Connections, or mHCs. They offer a way to scale LLMs without incurring huge costs. The company postponed the release of its R2 model in mid-2025. Just ...

Morningstar Quantitative Ratings for Stocks are generated using an algorithm that compares companies that are not under analyst coverage to peer companies that do receive analyst-driven ratings.

Morningstar Quantitative Ratings for Stocks are generated using an algorithm that compares companies that are not under analyst coverage to peer companies that do receive analyst-driven ratings.

The paper comes at a time when most AI start-ups have been focusing on turning AI capabilities in LLMs into agents and other products DeepSeek's latest technical paper, co-authored by the firm's ...

Aiming to solve the “exploding signal” problem plaguing massive AI models, DeepSeek has introduced Manifold-Constrained Hyper-Connections (mHC), a novel architecture designed to stabilize training on ...

A PyTorch implementation of Manifold-Constrained Hyper-Connections (mHC) based on the DeepSeek-AI paper. deepseek-mhc/ ├── mhc/ # Core mHC implementation │ ├── sinkhorn.py # Sinkhorn-Knopp algorithm │ ...

mHC-GNN addresses the over-smoothing problem in deep Graph Neural Networks (GNNs) through manifold-constrained hyper-connections. Our method enables training networks exceeding 100 layers while ...