IEEE

Multi-Scale Adaptive Convolutional Graph for Multi-Stream Concept Drift

Abstract: Concept drift—characterized by the evolving and unpredictable nature of data distributions—is a persistent challenge for long-term modeling in non-stationary environments. In multi-stream ...
Technology Networks

Deep Learning for Variant Calling: How DeepVariant and Neural Networks Are Improving Genomic Accuracy

Deep learning variant calling has transformed genomic accuracy. Discover how DeepVariant works, outperforms classical tools, ...
Communications of the ACM

Can We Understand How Large Language Models Reason?

Large language models can write essays, solve math problems, and generate computer code, but it’s not fully understood how ...
IEEE

Residual Graph Convolutional Network for Protein Structure Understanding with Self-Derived Spatial Features

Abstract: ResidualGNNPlus is a residue-level Graph Neural Network that reconstructs 39 structural and biochemical features-such as hydrophobicity, SASA, polarity, Secondary Structure data, and ...