Research map / Probabilistic and scientific modeling / Variational and generative sampling
Latent Variable Graphical Model Selection Using Harmonic Analysis: Applications to the Human Connectome Project (HCP)
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Represent latent effects with low-frequency components and conditional dependencies with sparse high-frequency components | Proposed harmonic-analysis method · Synthetic brain connectivity, 10 latent variables | conditional dependency recovery | Helped |
| Use the proposed harmonic-analysis method instead of graphical Lasso or the method from [5] | Latent-variable precision matrix estimation · HCP | sparsity and interpretability | Helped |
Closest papers by meaning
- Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks (CVPR 2016 · similarity 0.89)
- Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity (NeurIPS 2016 · similarity 0.88)
- NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics (NeurIPS 2023 · similarity 0.88)
- Data-Driven Network Neuroscience: On Data Collection and Benchmark (NeurIPS 2023 · similarity 0.87)
- CASL-VAE: Learning Structured Latent Variables from Unpaired Data for Semi-supervised Clustering and Paired Sample Generation (NeurIPS 2026 · similarity 0.85)
- MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation (NeurIPS 2026 · similarity 0.85)
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