pith:SVE6YRE4
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
A simple convolutional architecture outperforms LSTMs on diverse sequence tasks while showing longer effective memory.
arxiv:1803.01271 v2 · 2018-03-04 · cs.LG · cs.AI · cs.CL
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\pithnumber{SVE6YRE4FOXSWXTFMY2IJHX555}
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Record completeness
Claims
Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory.
That the chosen tasks and datasets are representative of general sequence modeling challenges and that the generic convolutional and recurrent architectures are implemented and compared fairly without hidden advantages.
A simple convolutional network outperforms LSTMs across diverse sequence tasks while showing longer effective memory.
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| First computed | 2026-07-04T22:42:36.846473Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
9549ec449c2baf2b5e656634849efdef4a3f6c70ae61abf2678d6ca080bc418e
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/SVE6YRE4FOXSWXTFMY2IJHX555 \
| 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: 9549ec449c2baf2b5e656634849efdef4a3f6c70ae61abf2678d6ca080bc418e
Canonical record JSON
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"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
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"submitted_at": "2018-03-04T00:20:29Z",
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