Tag: Graph Neural Networks

A new critical review argues that topological deep learning, spanning persistent homology, simplicial and cellular complexes, and sheaf-based models, is ...

Researchers have developed DCM-Net, a dual conditional modulation graph attention network that adapts both its input features and its own ...

Researchers at China Jiliang University have combined fine-tuned large language models with graph attention networks to complete incomplete knowledge graphs ...

Researchers have developed DIRA, a model that combines dynamic entity embeddings with implicit relation-aware self-attention to significantly improve temporal knowledge ...

A plant-specific deep learning model called DeepGreenGO combines protein language model embeddings with graph neural networks to predict Gene Ontology ...

Researchers have developed a hybrid model that combines RoBERTa contextual embeddings with sentence-level and chunk-level graph neural networks to improve ...

Researchers have developed an adversarial attack framework that rotates molecular torsion angles within strict physical validity constraints, revealing consistent vulnerabilities ...

Researchers have developed NMPPI, a lightweight deep learning framework that predicts protein-protein interactions using only sequence and network structure data ...

Researchers have developed KTransPose, a dual-branch graph neural network framework that reduces protein-ligand pose errors by roughly 12 percent on ...

Researchers have built an AI portfolio manager that converts financial headlines into structured event nodes that dynamically rewire a market ...