Pith Number

pith:KF7PHI2J

pith:2025:KF7PHI2JNNVYP3XHYAM6HM4RAD

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CHUCKLE -- When Humans Teach AI To Learn Emotions The Easy Way

Ankush Pratap Singh, Houwei Cao, Yong Liu

Ordering emotion samples by human annotator agreement boosts model performance and efficiency

arxiv:2510.09382 v3 · 2025-10-10 · cs.LG

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3 Author claim open · sign in to claim

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 suggest that CHUCKLE enhances the performance of LSTMs and Transformers over non-curriculum baselines, while reducing the number of gradient updates, thereby enhancing both training efficiency and model robustness in both subject-dependent and subject-independent settings.

C2weakest assumption

The central assumption that clips challenging for humans (measured by annotator disagreement and alignment) are similarly hard for neural networks; this premise is stated explicitly in the abstract and is required for the curriculum ordering to transfer from human perception to model training.

C3one line summary

CHUCKLE defines training sample difficulty for emotion recognition using crowdsourced annotator agreement and alignment, then applies this ordering to improve LSTM and Transformer performance while cutting gradient updates.

Formal links

2 machine-checked theorem links

Cited by

1 paper in Pith

CHUCKLE -- When Humans Teach AI To Learn Emotions The Easy Way

Receipt and verification

First computed	2026-06-23T02:13:17.522164Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

517ef3a3496b6b87eee7c019e3b39100d8018203482b10a7a84d742aa10787f2

Aliases

arxiv: 2510.09382 · arxiv_version: 2510.09382v3 · doi: 10.48550/arxiv.2510.09382 · pith_short_12: KF7PHI2JNNVY · pith_short_16: KF7PHI2JNNVYP3XH · pith_short_8: KF7PHI2J

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/KF7PHI2JNNVYP3XHYAM6HM4RAD \
  | 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: 517ef3a3496b6b87eee7c019e3b39100d8018203482b10a7a84d742aa10787f2

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "a60b69dd5064024e5565f998d189b4c4cacac78cc6d9fcaaa6e6c618c46d7607",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2025-10-10T13:38:06Z",
    "title_canon_sha256": "2c18c9762319f0607f93f4dcaa1c7682318c50a7776dc8659d9b914d33ea41e9"
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  "source": {
    "id": "2510.09382",
    "kind": "arxiv",
    "version": 3
  }
}