Pith Number

pith:T6XEL2WT

pith:2026:T6XEL2WTSBDY67RYNVU4G5N2DO

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Possibilistic Predictive Uncertainty for Deep Learning

Jeremie Houssineau, Piotr Koniusz, Yao Ni, Yew Soon Ong

Deep neural networks can quantify epistemic uncertainty by projecting possibilistic posteriors over parameters onto predictions via supremum operators and approximating them with learnable Dirichlet possibility functions.

arxiv:2605.00600 v2 · 2026-05-01 · cs.LG · cs.AI · cs.CV

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Claims

C1strongest claim

we introduce Dirichlet-approximated possibilistic posterior predictions (DAPPr), a principled framework leveraging possibility theory. We define a possibilistic posterior over parameters, projects this posterior to the prediction space via supremum operators, and approximates the projected posterior using learnable Dirichlet possibility functions. This projection-and-approximation strategy yields a simple training objective with closed-form solutions.

C2weakest assumption

That the supremum-based projection of the possibilistic posterior onto prediction space, followed by Dirichlet approximation, rigorously quantifies epistemic uncertainty rather than merely producing a convenient training objective.

C3one line summary

DAPPr introduces a possibilistic framework that projects parameter posteriors to predictions via supremum and approximates them with Dirichlet possibility functions to yield efficient, closed-form epistemic uncertainty estimates.

Receipt and verification

First computed	2026-06-02T02:04:53.543821Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9fae45ead390478f7e386d69c375ba1ba314bf093372b0045a94700113f2344b

Aliases

arxiv: 2605.00600 · arxiv_version: 2605.00600v2 · doi: 10.48550/arxiv.2605.00600 · pith_short_12: T6XEL2WTSBDY · pith_short_16: T6XEL2WTSBDY67RY · pith_short_8: T6XEL2WT

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/T6XEL2WTSBDY67RYNVU4G5N2DO \
  | 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: 9fae45ead390478f7e386d69c375ba1ba314bf093372b0045a94700113f2344b

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "d4976aefeb262f5e472946b53607a1234cf48d2e6cb2caedd6c05be1b7c61fc9",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.CV"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-01T12:14:01Z",
    "title_canon_sha256": "b190207710f69394955f9d4c4d4a1f46d9456e1342bdbb7aebfd4a522dfc787d"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.00600",
    "kind": "arxiv",
    "version": 2
  }
}