pith:VL3WXSYJ
Gaussian Error Linear Units (GELUs)
The GELU activation xΦ(x) outperforms ReLU and ELU on computer vision, natural language processing, and speech tasks.
arxiv:1606.08415 v5 · 2016-06-27 · cs.LG
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Claims
We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all considered computer vision, natural language processing, and speech tasks.
That the observed gains on the specific tasks and models tested will generalize to other architectures, datasets, and training regimes without further tuning.
GELU activation xΦ(x) outperforms ReLU and ELU on computer vision, NLP, and speech tasks by weighting inputs by value rather than gating by sign.
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| First computed | 2026-07-05T06:17:34.981584Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/VL3WXSYJ2FP34EL2QPKVVPYFNS \
| 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())"
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Canonical record JSON
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