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
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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
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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()