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Sparsely-Gated Mixture-of-Experts (MoE) introduced in paper: Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer (Shazeer et al., 2017).
Last verified 2026-05-16 · Methodology veritas-v0.1 · 2d6d7f61f1db6493
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Structured fields
- Subject
- Sparsely-Gated Mixture-of-Experts (MoE)
- Predicate
introduced_in_paper- Object
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer (Shazeer et al., 2017)
- Confidence
- 100%
- Tags
- moe · foundational · shazeer · 2017 · google
Sources (1)
[1] preprint · arXiv (Shazeer, Mirhoseini, Maziarz, Davis, Le, Hinton, Dean) · 2017-01-23
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer“We introduce a Sparsely-Gated Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward sub-networks. A trainable gating network determines a sparse combination of these experts to use for each example.”
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curl https://sourcescore.org/api/v1/claims/2d6d7f61f1db6493.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/2d6d7f61f1db6493.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "Sparsely-Gated Mixture-of-Experts (MoE) introduced in paper: Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer (Shazeer et al., 2017)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/2d6d7f61f1db6493.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Sparsely-Gated Mixture-of-Experts (MoE) introduced in paper: Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer (Shazeer et al., 2017)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_sparsely_gated_mixture_of_experts_moe_fact() -> dict:
"""Fetch the verified SourceScore claim for Sparsely-Gated Mixture-of-Experts (MoE)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/2d6d7f61f1db6493.json")
return r.json()