Pith Number

pith:C67XSDJH

pith:2026:C67XSDJHSXOMZV4ZPPXFT74GIS

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A Comparative Analysis on the Performance of Upper Confidence Bound Algorithms in Adaptive Deep Neural Networks

Grigorios Papanikolaou, Ioannis Kontopoulos, Konstantinos Tserpes

Multiple Upper Confidence Bound strategies achieve sub-linear regret in Adaptive Deep Neural Networks and improve accuracy-energy and accuracy-latency trade-offs.

arxiv:2604.24810 v3 · 2026-04-27 · cs.LG · cs.AI

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\pithnumber{C67XSDJHSXOMZV4ZPPXFT74GIS}

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4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Experimental results demonstrate that all strategies achieve sub-linear cumulative regret, with UCB-Bayes converging the fastest, followed by UCB-Tuned and UCB-V. Finally, UCB-V and UCB-Tuned dominate the Pareto Frontiers of accuracy-latency and accuracy-energy trade-offs.

C2weakest assumption

The assumption that the multi-armed bandit reward distributions remain stationary across inference steps and that the chosen datasets and models adequately represent real edge-device conditions with varying input distributions and hardware constraints.

C3one line summary

UCB-V and UCB-Tuned dominate accuracy-energy and accuracy-latency trade-offs while all tested UCB strategies achieve sub-linear regret in adaptive DNN early-exit experiments on CIFAR datasets.

Receipt and verification

First computed	2026-05-25T02:01:21.158039Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

17bf790d2795dcccd7997bee59ff86449096c9bf46c34885e3d899c74d122dcd

Aliases

arxiv: 2604.24810 · arxiv_version: 2604.24810v3 · doi: 10.48550/arxiv.2604.24810 · pith_short_12: C67XSDJHSXOM · pith_short_16: C67XSDJHSXOMZV4Z · pith_short_8: C67XSDJH

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/C67XSDJHSXOMZV4ZPPXFT74GIS \
  | 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: 17bf790d2795dcccd7997bee59ff86449096c9bf46c34885e3d899c74d122dcd

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "d3ef120d0fa5b8e02c510662b287655029ea01cca648c04094e654637d5a3f73",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-04-27T08:51:44Z",
    "title_canon_sha256": "8298ff77409caa6bd2038e49aadafb2e4053ae1733440fa7aee06fd2fd961f44"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.24810",
    "kind": "arxiv",
    "version": 3
  }
}