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
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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)
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curl https://sourcescore.org/api/v1/claims/18409e7f8a6d7aac.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/18409e7f8a6d7aac.json");
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// "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()