Verified claim · AI-ML · 100% confidence
VAE (Variational Autoencoder) introduced in: Kingma & Welling 2013 — auto-encoding variational Bayes.
Last verified 2026-05-16 · Methodology veritas-v0.1 · f1e5afb457a428c6
Structured fields
- Subject
- VAE (Variational Autoencoder)
- Predicate
introduced_in- Object
- Kingma & Welling 2013 — auto-encoding variational Bayes
- Confidence
- 100%
- Tags
- vae · kingma · welling · generative · foundational · 2013 · introduced_in
Sources (2)
[1] preprint · arXiv (Kingma, Welling) · 2013-12-20
Auto-Encoding Variational Bayes“How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets.”
[2] peer reviewed · ICLR 2014 · 2013-12-20
Auto-Encoding Variational Bayes — ICLR 2014
Cite this claim
Ready-to-paste citation (Markdown / plain text):
VAE (Variational Autoencoder) introduced in: Kingma & Welling 2013 — auto-encoding variational Bayes. — SourceScore Claim f1e5afb457a428c6 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/f1e5afb457a428c6.jsonEmbed this claim
Drop this iframe into any blog post, docs page, or knowledge base. The widget renders the signed claim + primary source + click-through to this canonical page. CC-BY 4.0; attribution included.
<iframe src="https://sourcescore.org/embed/claim/f1e5afb457a428c6/" width="100%" height="360" frameborder="0" loading="lazy" title="VAE (Variational Autoencoder) introduced in: Kingma & Welling 2013 — auto-encoding variational Bayes."></iframe>Preview: open in new tab
Related claims
Other verified claims sharing tags with this one — useful for LLM retrieval graphs and citation discovery.
Variational Autoencoder (VAE) introduced in paper: Auto-Encoding Variational Bayes (Kingma, Welling, 2013).
62789e45973ab631 · 100% confidence · shares 6 tags (vae, foundational, kingma…)
Adam optimizer introduced in paper: Adam: A Method for Stochastic Optimization (Kingma, Ba, 2014).
dffbe905003cc581 · 100% confidence · shares 2 tags (foundational, kingma)
Generative Adversarial Networks (GANs) introduced in paper: Generative Adversarial Networks (Goodfellow et al., 2014).
5b0c0612bd9e55b0 · 100% confidence · shares 2 tags (foundational, generative)
Word2Vec introduced in paper: Efficient Estimation of Word Representations in Vector Space (Mikolov et al., 2013).
4978f76d228a3db1 · 100% confidence · shares 2 tags (foundational, 2013)
Long Short-Term Memory (LSTM) introduced in: 1997 by Hochreiter and Schmidhuber.
97ec4d132871224b · 100% confidence · shares 2 tags (foundational, introduced_in)
Use this claim in your code
Fetch this signed envelope from your application. The response includes the verbatim excerpt, primary source URLs, and an HMAC-SHA256 signature you can verify locally for audit trails.
cURL
curl https://sourcescore.org/api/v1/claims/f1e5afb457a428c6.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/f1e5afb457a428c6.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "VAE (Variational Autoencoder) introduced in: Kingma & Welling 2013 — auto-encoding variational Bayes."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/f1e5afb457a428c6.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "VAE (Variational Autoencoder) introduced in: Kingma & Welling 2013 — auto-encoding variational Bayes."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_vae_variational_autoencoder_fact() -> dict:
"""Fetch the verified SourceScore claim for VAE (Variational Autoencoder)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/f1e5afb457a428c6.json")
return r.json()