pith:IPPKFWT7
Sheaf Neural Networks on SPD Manifolds: Second-Order Geometric Representation Learning
Sheaf neural networks defined on SPD manifolds represent second-order geometric features that Euclidean sheaves cannot.
arxiv:2604.20308 v2 · 2026-04-22 · cs.LG
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Record completeness
Claims
SPD-valued sheaves are strictly more expressive than Euclidean sheaves: they admit consistent configurations (global sections) that vector-valued sheaves cannot represent, directly translating to richer learned representations.
The SPD manifold admits a Lie group structure, enabling well-posed analogs of sheaf operators without projecting to Euclidean space.
Sheaf neural networks on the SPD manifold enable strictly more expressive second-order geometric representations than Euclidean versions and achieve SOTA results on most MoleculeNet benchmarks.
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Receipt and verification
| First computed | 2026-06-02T01:04:16.374169Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
43dea2da7f75d0ddc5b9f8d3a7b801d1b619d19a2eb612d61b23e5b439bca9b6
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/IPPKFWT7OXIN3RNZ7DJ2POAB2G \
| 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())"
# expect: 43dea2da7f75d0ddc5b9f8d3a7b801d1b619d19a2eb612d61b23e5b439bca9b6
Canonical record JSON
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