Learning Macroscopic Brain Connectomes via Group-Sparse Factorization

Farzane Aminmansour, Andrew Patterson, Lei Le, Yisu Peng, Daniel Mitchell, Franco Pestilli, Cesar F. Caiafa, Russell Greiner, Martha White

Oct-2-2025, 01:08:58 GMT–Neural Information Processing Systems

A data-driven approach, however, could overcome limitations in such rule-based approaches and improve precision mappings for individuals.

artificial intelligence, machine learning, orientation, (18 more...)

Neural Information Processing Systems

Oct-2-2025, 01:08:58 GMT

Conferences PDF

Country:
- Africa > Senegal
  - Kolda Region > Kolda (0.04)
- Asia > Japan
  - Honshū > Kantō > Tokyo Metropolis Prefecture > Tokyo (0.14)
- North America
  - Canada > Alberta
    - Census Division No. 11 > Edmonton Metropolitan Region > Edmonton (0.04)
  - United States
    - Indiana > Monroe County
      - Bloomington (0.04)
    - Massachusetts > Suffolk County
      - Boston (0.04)
- South America > Argentina (0.04)

Industry:
- Health & Medicine > Diagnostic Medicine > Imaging (0.47)

Technology:
- Information Technology > Artificial Intelligence
  - Machine Learning > Statistical Learning (0.68)
  - Representation & Reasoning
    - Optimization (0.68)
    - Rule-Based Reasoning (0.54)

Duplicate Docs Excel Report

Title
Learning Macroscopic Brain Connectomes via Group-Sparse Factorization

Similar Docs Excel Report more

Title	Similarity	Source
None found