Verified claim · AI-ML · 100% confidence
Knowledge Distillation popularized in: Hinton, Vinyals, Dean 2015 — distilling the knowledge in a neural network.
Last verified 2026-05-16 · Methodology veritas-v0.1 · f14acb906ba6c12f
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Structured fields
- Subject
- Knowledge Distillation
- Predicate
popularized_in- Object
- Hinton, Vinyals, Dean 2015 — distilling the knowledge in a neural network
- Confidence
- 100%
- Tags
- knowledge-distillation · hinton · google · compression · foundational · 2015 · introduced_in
Sources (2)
[1] preprint · arXiv (Hinton, Vinyals, Dean / Google) · 2015-03-09
Distilling the Knowledge in a Neural Network“A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive. We show that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy.”
[2] preprint · arXiv · 2015-03-09
Knowledge Distillation — full paper PDF
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// "Knowledge Distillation popularized in: Hinton, Vinyals, Dean 2015 — distilling the knowledge in a neural network."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/f14acb906ba6c12f.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Knowledge Distillation popularized in: Hinton, Vinyals, Dean 2015 — distilling the knowledge in a neural network."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_knowledge_distillation_fact() -> dict:
"""Fetch the verified SourceScore claim for Knowledge Distillation."""
r = httpx.get("https://sourcescore.org/api/v1/claims/f14acb906ba6c12f.json")
return r.json()