Verified claim · AI-ML · 100% confidence
AlexNet introduced in paper: ImageNet Classification with Deep Convolutional Neural Networks (Krizhevsky, Sutskever, Hinton, 2012).
Last verified 2026-05-16 · Methodology veritas-v0.1 · 98b6e774be89d967
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Structured fields
- Subject
- AlexNet
- Predicate
introduced_in_paper- Object
- ImageNet Classification with Deep Convolutional Neural Networks (Krizhevsky, Sutskever, Hinton, 2012)
- Confidence
- 100%
- Tags
- alexnet · foundational · vision · krizhevsky · hinton · 2012 · nips · imagenet
Sources (2)
[1] peer reviewed · NeurIPS Foundation (Krizhevsky, Sutskever, Hinton) · 2012-12-03
ImageNet Classification with Deep Convolutional Neural Networks (NeurIPS 2012)“We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes.”
[2] docs · Wikipedia
AlexNet — WikipediaWikipedia is rated by SourceScore — see its reliability →
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AlexNet introduced in paper: ImageNet Classification with Deep Convolutional Neural Networks (Krizhevsky, Sutskever, Hinton, 2012). — SourceScore Claim 98b6e774be89d967 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/98b6e774be89d967.jsonEmbed this claim
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Frequently asked questions
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Yes — SourceScore verified this claim with 100% confidence as of 2026-05-16. The verification uses 2 primary sources cross-referenced against the SourceScore methodology (version veritas-v0.1). Full source list + signed JSON envelope linked below.
What is the evidence for "AlexNet introduced in paper: ImageNet Classification with Deep Convolutional Neural Networks (Krizhevsky, Sutskever, Hinton, 2012)."?
Evidence comes from 2 primary sources: NeurIPS Foundation (Krizhevsky, Sutskever, Hinton), Wikipedia. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/98b6e774be89d967.json includes an HMAC-SHA256 signature for audit verification.
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Last verified 2026-05-16 under methodology version veritas-v0.1. The signed JSON envelope is dated and cryptographically signed for audit trail. Re-verification cadence depends on the claim type and source freshness.
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Fetch the signed JSON envelope from https://sourcescore.org/api/v1/claims/98b6e774be89d967.json which includes the verbatim claim, primary sources, confidence, methodology version, last-verified date, and HMAC-SHA256 signature for audit. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
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curl https://sourcescore.org/api/v1/claims/98b6e774be89d967.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/98b6e774be89d967.json");
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// "AlexNet introduced in paper: ImageNet Classification with Deep Convolutional Neural Networks (Krizhevsky, Sutskever, Hinton, 2012)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/98b6e774be89d967.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "AlexNet introduced in paper: ImageNet Classification with Deep Convolutional Neural Networks (Krizhevsky, Sutskever, Hinton, 2012)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_alexnet_fact() -> dict:
"""Fetch the verified SourceScore claim for AlexNet."""
r = httpx.get("https://sourcescore.org/api/v1/claims/98b6e774be89d967.json")
return r.json()