Verified claim · AI-ML · 100% confidence
Variational Autoencoder (VAE) introduced in paper: Auto-Encoding Variational Bayes (Kingma, Welling, 2013).
Last verified 2026-05-16 · Methodology veritas-v0.1 · 62789e45973ab631
Structured fields
- Subject
- Variational Autoencoder (VAE)
- Predicate
introduced_in_paper- Object
- Auto-Encoding Variational Bayes (Kingma, Welling, 2013)
- Confidence
- 100%
- Tags
- vae · foundational · kingma · welling · 2013 · iclr · generative
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?”
[2] peer reviewed · OpenReview / ICLR · 2014-04-14
Auto-Encoding Variational Bayes (ICLR 2014)
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curl https://sourcescore.org/api/v1/claims/62789e45973ab631.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/62789e45973ab631.json");
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// "Variational Autoencoder (VAE) introduced in paper: Auto-Encoding Variational Bayes (Kingma, Welling, 2013)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/62789e45973ab631.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Variational Autoencoder (VAE) introduced in paper: Auto-Encoding Variational Bayes (Kingma, Welling, 2013)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_variational_autoencoder_vae_fact() -> dict:
"""Fetch the verified SourceScore claim for Variational Autoencoder (VAE)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/62789e45973ab631.json")
return r.json()