📖 NLP Understanding

🧪 ICML2025 · 1 paper notes

📌 Same area in other venues: 🔬 ICLR2026 (2) · 💬 ACL2026 (34) · 🧪 ICML2026 (2) · 🤖 AAAI2026 (1) · 🧠 NeurIPS2025 (3) · 📹 ICCV2025 (1)

Cover Learning for Large-Scale Topology Representation

Proposes Cover Learning as a unified unsupervised learning problem. From an optimization perspective, three loss functions (measure, geometry, topology) are designed to learn topologically faithful covers of datasets. The resulting simplicial complexes are more compact than standard geometric complexes in topological inference and can represent higher-dimensional information than Mapper graphs in large-scale topological visualization.