Landscaper: Understanding Loss Landscapes Through Multi-Dimensional Topological Analysis
#Landscaper #Loss landscapes #Neural networks #Python package #Topological Data Analysis #Hessian matrix #Optimization
📌 Key Takeaways
- Researchers launched Landscaper, an open-source Python tool for analyzing neural network loss landscapes.
- The package overcomes the limitations of traditional low-dimensional analysis by supporting arbitrary-dimensional visualization.
- It integrates Hessian-based subspace construction with Topological Data Analysis (TDA).
- The tool reveals critical geometric features like basin hierarchies and the connectivity between local minima.
📖 Full Retelling
🏷️ Themes
Machine Learning, Topology, Data Science
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🔗 Entity Intersection Graph
Connections for Neural network:
- 🌐 Deep learning (4 shared articles)
- 🌐 Reinforcement learning (2 shared articles)
- 🌐 Machine learning (2 shared articles)
- 🌐 Large language model (2 shared articles)
- 🌐 Censorship (1 shared articles)
- 🌐 CSI (1 shared articles)
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📄 Original Source Content
arXiv:2602.07135v1 Announce Type: cross Abstract: Loss landscapes are a powerful tool for understanding neural network optimization and generalization, yet traditional low-dimensional analyses often miss complex topological features. We present Landscaper, an open-source Python package for arbitrary-dimensional loss landscape analysis. Landscaper combines Hessian-based subspace construction with topological data analysis to reveal geometric structures such as basin hierarchy and connectivity. A