SOURCE-LINKED INTELLIGENCE
HyCoSeq: Contextual Hyperbolic Representation Learning for Genomic Sequences
Hyperbolic geometry provides a natural inductive bias for genomic representation learning, but existing hyperbolic genomic models primarily use Lorentz convolutions to learn local sequence representations, while their residual pathways do not directly aggregate full Lorentz representations. We propose HyCoSeq, a contextual hyperbolic representation learning framework for genomic sequences. HyCoSeq incorporates weighted Lorentzian residual aggregation into multi-curvature Lorentz encoding, allowing full Lorentz representations to participate directly in geometry-consistent local aggregation. It
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T10:01:47.000Z
First collected: 2026-09-20T08:40:59.508Z. This is not the publication date.