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Optimization Methods for Large-Scale Machine Learning paper by LΓ©on Bottou
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Paper

Optimization Methods for Large-Scale Machine Learning

by LΓ©on Bottou arxiv/1606.04838
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20.3
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Q: Quality 60
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This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural networks, we discuss how optimization problems arise in machine learning and what makes them challenging. A major theme of our study is that large-scale machine learning represents a distinctive setting in which the stochastic gradient (SG) method has tradit...

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Registry ID 1606.04838
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BibTeX 
@misc{arxiv_1606_04838,
  author = {LΓ©on Bottou},
  title = {Optimization Methods for Large-Scale Machine Learning Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/1606.04838}},
  note = {Accessed via Free2AITools.}
}
APA Style
LΓ©on Bottou. (2026). Optimization Methods for Large-Scale Machine Learning [Paper]. Free2AITools. https://arxiv.org/abs/1606.04838

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Semantic (S) 50

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Authority (A) 0
Popularity (P) 0
Recency (R) 0
Quality (Q) 60

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FNI V2.0 for Optimization Methods for Large-Scale Machine Learning: Authority (A:0), Popularity (P:0), Recency (R:0), Quality (Q:60). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural networks, we discuss how optimization problems arise in machine learning and what makes them challenging. A major theme of our study is that large-scale machine learning represents a distinctive setting in which the stochastic gradient (SG) method has tradit..."

❝ Cite Node 

@article{Bottou2026Optimization,
  title={Optimization Methods for Large-Scale Machine Learning},
  author={LΓ©on Bottou},
  journal={arXiv preprint arXiv:1606.04838},
  year={2026}
}

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LΓ©on Bottou

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id
1606.04838
slug
1606.04838
source
arxiv
author
LΓ©on Bottou
license
arXiv
tags
arxiv:stat.ML, arxiv:cs.LG, arxiv:math.OC

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