pith:OWPK4BFY
Neural Networks With Dense Weights Are Not Universal Approximators
Dense neural networks with constraints on weights and dimensions cannot approximate all Lipschitz continuous functions.
arxiv:2602.07618 v6 · 2026-02-07 · cs.LG · stat.ML
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Record completeness
Claims
We demonstrate the existence of Lipschitz continuous functions that cannot be approximated by such networks.
The chosen constraints on weights and dimensions accurately model a notion of dense connectivity, and feedforward networks can be interpreted as message passing graph neural networks without loss of generality for the approximation question.
Dense neural networks subject to constraints on weights and dimensions cannot approximate arbitrary Lipschitz continuous functions.
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Receipt and verification
| First computed | 2026-05-20T00:05:41.790668Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
759eae04b8e7c493c2a4af03819fd9f8e792acd6b7cdd40d4aea29745088d97d
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/OWPK4BFY47CJHQVEV4BYDH6Z7D \
| 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: 759eae04b8e7c493c2a4af03819fd9f8e792acd6b7cdd40d4aea29745088d97d
Canonical record JSON
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