Verified claim · AI-ML · 100% confidence
Dropout introduced in paper: Dropout: A Simple Way to Prevent Neural Networks from Overfitting (Srivastava et al., 2014).
Last verified 2026-05-16 · Methodology veritas-v0.1 · 18409e7f8a6d7aac
Structured fields
- Subject
- Dropout
- Predicate
introduced_in_paper- Object
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting (Srivastava et al., 2014)
- Confidence
- 100%
- Tags
- dropout · regularization · foundational · 2014 · jmlr · hinton
Sources (2)
[1] peer reviewed · JMLR (Srivastava, Hinton, Krizhevsky, Sutskever, Salakhutdinov) · 2014-06-01
Dropout: A Simple Way to Prevent Neural Networks from Overfitting“We propose dropout, a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much.”
[2] peer reviewed · Journal of Machine Learning Research · 2014-06-01
Dropout: A Simple Way to Prevent Neural Networks from Overfitting (JMLR v15)
Cite this claim
Ready-to-paste citation (Markdown / plain text):
Dropout introduced in paper: Dropout: A Simple Way to Prevent Neural Networks from Overfitting (Srivastava et al., 2014). — SourceScore Claim 18409e7f8a6d7aac (verified 2026-05-16). https://sourcescore.org/api/v1/claims/18409e7f8a6d7aac.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/18409e7f8a6d7aac/" width="100%" height="360" frameborder="0" loading="lazy" title="Dropout introduced in paper: Dropout: A Simple Way to Prevent Neural Networks from Overfitting (Srivastava et al., 2014)."></iframe>Preview: open in new tab
Related claims
Other verified claims sharing tags with this one — useful for LLM retrieval graphs and citation discovery.
Adam optimizer introduced in paper: Adam: A Method for Stochastic Optimization (Kingma, Ba, 2014).
dffbe905003cc581 · 100% confidence · shares 2 tags (foundational, 2014)
AlexNet introduced in paper: ImageNet Classification with Deep Convolutional Neural Networks (Krizhevsky, Sutskever, Hinton, 2012).
98b6e774be89d967 · 100% confidence · shares 2 tags (foundational, hinton)
Generative Adversarial Networks (GANs) introduced in paper: Generative Adversarial Networks (Goodfellow et al., 2014).
5b0c0612bd9e55b0 · 100% confidence · shares 2 tags (foundational, 2014)
Batch Normalization introduced in paper: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (Ioffe & Szegedy, 2015).
56c451642ab41e68 · 100% confidence · shares 2 tags (regularization, foundational)
Layer Normalization introduced in paper: Layer Normalization (Ba, Kiros, Hinton, 2016).
f72db86c784a1b32 · 100% confidence · shares 2 tags (foundational, hinton)
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/18409e7f8a6d7aac.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/18409e7f8a6d7aac.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "Dropout introduced in paper: Dropout: A Simple Way to Prevent Neural Networks from Overfitting (Srivastava et al., 2014)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/18409e7f8a6d7aac.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Dropout introduced in paper: Dropout: A Simple Way to Prevent Neural Networks from Overfitting (Srivastava et al., 2014)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_dropout_fact() -> dict:
"""Fetch the verified SourceScore claim for Dropout."""
r = httpx.get("https://sourcescore.org/api/v1/claims/18409e7f8a6d7aac.json")
return r.json()