pith:32FFQ7FK
Consistent Geometric Deep Learning via Hilbert Bundles and Cellular Sheaves
Sampling a manifold with a Hilbert bundle induces a cellular sheaf whose Laplacian converges in probability to the connection Laplacian, enabling consistent discrete networks for infinite-dimensional signals.
arxiv:2605.06395 v2 · 2026-05-07 · cs.LG · cs.AI · eess.SP
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Claims
we prove that its sheaf Laplacian converges in probability to the underlying connection Laplacian as the sampling density increases. Notably, this result is a generalization to the infinite-dimensional bundle setting of the Belkin & Niyogi convergence result for the graph Laplacian to the manifold Laplacian
Sampling the manifold induces a Hilbert Cellular Sheaf with edge-wise coupling rules that preserve the necessary structure for the Laplacian convergence to hold in the infinite-dimensional Hilbert bundle case.
HilbNets discretize Hilbert bundle convolutions through Hilbert Cellular Sheaves whose Laplacians converge to the continuous connection Laplacian, enabling consistent learning across samplings.
Receipt and verification
| First computed | 2026-05-21T01:04:26.973934Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
de8a587caa5b40393a606100962a3835f746f59f3fad37bf6a86a5c97cfca67d
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/32FFQ7FKLNADSOTAMEAJMKRYGX \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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