Verified claim · AI-ML · 100% confidence
AdamW optimizer introduced in paper: Decoupled Weight Decay Regularization (Loshchilov & Hutter, 2017).
Last verified 2026-05-16 · Methodology veritas-v0.1 · b6d51eba4fc7f918
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Structured fields
- Subject
- AdamW optimizer
- Predicate
introduced_in_paper- Object
- Decoupled Weight Decay Regularization (Loshchilov & Hutter, 2017)
- Confidence
- 100%
- Tags
- adamw · optimizer · weight-decay · foundational · 2017 · iclr
Sources (2)
[1] preprint · arXiv (Loshchilov, Hutter) · 2017-11-14
Decoupled Weight Decay Regularization“We propose a simple modification to recover the original formulation of weight decay regularization by decoupling the weight decay from the optimization steps taken w.r.t. the loss function.”
[2] peer reviewed · OpenReview / ICLR · 2019-05-06
Decoupled Weight Decay Regularization (ICLR 2019)
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// "AdamW optimizer introduced in paper: Decoupled Weight Decay Regularization (Loshchilov & Hutter, 2017)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/b6d51eba4fc7f918.json")
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# "AdamW optimizer introduced in paper: Decoupled Weight Decay Regularization (Loshchilov & Hutter, 2017)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_adamw_optimizer_fact() -> dict:
"""Fetch the verified SourceScore claim for AdamW optimizer."""
r = httpx.get("https://sourcescore.org/api/v1/claims/b6d51eba4fc7f918.json")
return r.json()