Verified claim · AI-ML · 82% confidence
Adadelta introduced in paper: ADADELTA: An Adaptive Learning Rate Method (Zeiler, 2012).
Last verified 2026-06-02 · Methodology veritas-v0.1 · f777d78b0ddbdaec
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Structured fields
- Subject
- Adadelta
- Predicate
introduced_in_paper- Object
- ADADELTA: An Adaptive Learning Rate Method (Zeiler, 2012)
- Confidence
- 82%
- Tags
- adadelta · optimizer · adaptive-learning-rate · gradient-descent · zeiler · foundational · 2012
Sources (2)
[1] preprint · arXiv (Matthew D. Zeiler) · 2012-12-22
ADADELTA: An Adaptive Learning Rate Method“We present a novel per-dimension learning rate method for gradient descent called ADADELTA. The method dynamically adapts over time using only first order information and has minimal computational overhead beyond vanilla stochastic gradient descent. The method requires no manual tuning of a learning rate and appears robust to noisy gradient information, different model architecture choices, various data modalities and selection of hyperparameters.”
[2] docs · Keras
Adadelta optimizer (Keras docs)
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curl https://sourcescore.org/api/v1/claims/f777d78b0ddbdaec.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/f777d78b0ddbdaec.json");
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// "Adadelta introduced in paper: ADADELTA: An Adaptive Learning Rate Method (Zeiler, 2012)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/f777d78b0ddbdaec.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Adadelta introduced in paper: ADADELTA: An Adaptive Learning Rate Method (Zeiler, 2012)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_adadelta_fact() -> dict:
"""Fetch the verified SourceScore claim for Adadelta."""
r = httpx.get("https://sourcescore.org/api/v1/claims/f777d78b0ddbdaec.json")
return r.json()