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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Frequently asked questions
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Yes — SourceScore verified this claim with 82% confidence as of 2026-06-02. The verification uses 2 primary sources cross-referenced against the SourceScore methodology (version veritas-v0.1). Full source list + signed JSON envelope linked below.
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Evidence comes from 2 primary sources: arXiv (Aaron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu), Hugging Face. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/e19ab9089aadf1f5.json includes an HMAC-SHA256 signature for audit verification.
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Last verified 2026-06-02 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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// "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")
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# "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()