Who Cited It

Machine learning: Trends, perspectives, and prospects

2015 · Science · 9,947 citations · 6 from inside this corpus

Michael I. Jordan, Tom M. Mitchell

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

Machine learning: Trends, perspectives, and prospects (2015)Machine learning: Trends, per…Reinforcement Learning: An Introduction (1998)Reinforcement Learning: An In…Reducing the Dimensionality of Data with Neural Networks (2006)Reducing the Dimensionality o…Deep learning in neural networks: An overview (2014)Deep learning in neural netwo…Distributed Optimization and Statistical Learning via the Alternating Direction Method of… (2011)Distributed Optimization and …Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Fou… (2012)Deep Neural Networks for Acou…Machine learning a probabilistic perspective (2012)Machine learning a probabilis…Calibrating Noise to Sensitivity in Private Data Analysis (2006)Calibrating Noise to Sensitiv…Learning Deep Architectures for AI (2009)Learning Deep Architectures f…Probabilistic topic models (2012)Probabilistic topic modelsTransfer Learning for Reinforcement Learning Domains: A Survey (2009)Transfer Learning for Reinfor…On hyperparameter optimization of machine learning algorithms: Theory and practice (2020)On hyperparameter optimizatio…Deep learning for healthcare: review, opportunities and challenges (2017)Deep learning for healthcare:…Explainable AI: A Review of Machine Learning Interpretability Methods (2020)Explainable AI: A Review of M…Review of Deep Learning Algorithms and Architectures (2019)Review of Deep Learning Algor…Causability and explainability of artificial intelligence in medicine (2019)Causability and explainabilit…Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the E… (2016)Deep Patient: An Unsupervised…
16 of 16 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
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Citation edge evidence

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What this paper cites, inside the corpus

PaperYearCited
Reinforcement Learning: An Introduction199827,409
Reducing the Dimensionality of Data with Neural Networks200621,247
Deep learning in neural networks: An overview201418,236
Distributed Optimization and Statistical Learning via the Alternating Direction Method of…201116,313
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Fou…201210,399
Machine learning a probabilistic perspective20129,324
Calibrating Noise to Sensitivity in Private Data Analysis20067,210
Learning Deep Architectures for AI20096,951
Probabilistic topic models20125,623
Transfer Learning for Reinforcement Learning Domains: A Survey20091,564

What cites it, inside the corpus

PaperYearCited
On hyperparameter optimization of machine learning algorithms: Theory and practice20203,249
Deep learning for healthcare: review, opportunities and challenges20173,115
Explainable AI: A Review of Machine Learning Interpretability Methods20202,907
Review of Deep Learning Algorithms and Architectures20191,878
Causability and explainability of artificial intelligence in medicine20191,851
Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the E…20161,785

Links

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Topics

Anomaly Detection Techniques and ApplicationsComputer Science
Machine Learning and Data ClassificationComputer Science
Data Stream Mining TechniquesComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports2 author record(s) attached.
  • supports32 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:40+00:00.

sha256 7e3d99a592f7f61f…