pith:UPHINIGV
Direct Bethe Free Energy Minimization for Bayesian Neural Networks
Direct Bethe free energy minimization for BNNs produces losses strictly between MAP and ELBO, enables joint empirical Bayes in one gradient pass, and matches reference methods on UCI benchmarks at single-pass cost.
arxiv:2605.08446 v3 · 2026-05-08 · cs.LG
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Claims
Both objectives sit strictly between MAP and the ELBO (L_MAP ≤ L_Bethe ≤ L_ELBO), removing the structural Jensen gap that no choice of variational family can close. The Z-consistent prior formulation makes the prior precision a differentiable parameter, enabling empirical Bayes in a single gradient pass.
Restricting the weight posterior to a last-layer Gaussian yields analytically tractable losses and that the factor graph is tree-structured so the Bethe free energy is exact; if these do not hold the closed-form claims and inequality may fail.
Direct Bethe free energy minimization for BNNs produces losses strictly between MAP and ELBO, enables joint empirical Bayes in one gradient pass, and matches reference methods on UCI benchmarks at single-pass cost.
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Receipt and verification
| First computed | 2026-07-07T01:16:48.338728Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
a3ce86a0d5360a1afb15fda6a6879bad4a9198458c13427d8d0e7fce9becd3b2
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· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/UPHINIGVGYFBV6YV7WTKNB43VV \
| 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: a3ce86a0d5360a1afb15fda6a6879bad4a9198458c13427d8d0e7fce9becd3b2
Canonical record JSON
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