Verified claim · AI-ML · 82% confidence
PixelRNN introduced in paper: Pixel Recurrent Neural Networks (van den Oord et al., 2016).
Last verified 2026-06-02 · Methodology veritas-v0.1 · e19ab9089aadf1f5
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Structured fields
- Subject
- PixelRNN
- Predicate
introduced_in_paper- Object
- Pixel Recurrent Neural Networks (van den Oord et al., 2016)
- Confidence
- 82%
- Tags
- pixelrnn · autoregressive · image-generation · generative-model · van-den-oord · deepmind · foundational · 2016
Sources (2)
[1] preprint · arXiv (Aaron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu) · 2016-01-25
Pixel Recurrent Neural Networks“We present a deep neural network that sequentially predicts the pixels in an image along the two spatial dimensions. Our method models the discrete probability of the raw pixel values and encodes the complete set of dependencies in the image. Architectural novelties include fast two-dimensional recurrent layers and an effective use of residual connections in deep recurrent networks.”
[2] docs · Hugging Face
Pixel Recurrent Neural Networks (Hugging Face Papers)Hugging Face is rated by SourceScore — see its reliability →
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PixelRNN introduced in paper: Pixel Recurrent Neural Networks (van den Oord et al., 2016). — SourceScore Claim e19ab9089aadf1f5 (verified 2026-06-02). https://sourcescore.org/api/v1/claims/e19ab9089aadf1f5.jsonEmbed this claim
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The record lists 2 cited source(s): arXiv (Aaron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu), Hugging Face. Each is shown below with a short excerpt and URL. The JSON record at https://sourcescore.org/api/v1/claims/e19ab9089aadf1f5.json includes SourceScore-issued HMAC integrity metadata, not a public verification proof.
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curl https://sourcescore.org/api/v1/claims/e19ab9089aadf1f5.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/e19ab9089aadf1f5.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "PixelRNN introduced in paper: Pixel Recurrent Neural Networks (van den Oord et al., 2016)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/e19ab9089aadf1f5.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "PixelRNN introduced in paper: Pixel Recurrent Neural Networks (van den Oord et al., 2016)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_pixelrnn_fact() -> dict:
"""Fetch the verified SourceScore claim for PixelRNN."""
r = httpx.get("https://sourcescore.org/api/v1/claims/e19ab9089aadf1f5.json")
return r.json()