Journal of Machine Learning Research
Academic journal
| Discipline | Machine learning |
|---|---|
| Language | English |
| Edited by | Francis Bach, David Blei |
| Publication details | |
| History | 2000–present |
| Publisher | JMLR, Inc. and Microtome Publishing (United States) |
Open access | Yes |
Impact factor | 4.091 (2018) |
| Standard abbreviations ISO 4 (alt) · Bluebook (alt1 · alt2) NLM (alt) · MathSciNet (alt  ) | |
| ISO 4 | J. Mach. Learn. Res. |
| Indexing CODEN (alt · alt2) · JSTOR (alt) · LCCN (alt) MIAR · NLM (alt) · Scopus | |
| CODEN | JMLRAJ |
| ISSN | 1532-4435 (print) 1533-7928 (web) |
| LCCN | 00212568 |
| OCLC no. | 712803341 |
| Links | |
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The Journal of Machine Learning Research is a peer-reviewed open access scientific journal covering machine learning. It was established in 2000 and the first editor-in-chief was Leslie Kaelbling.[1] The current editors-in-chief are Francis Bach (Inria) and David Blei (Columbia University).
History
The journal was established as an open-access alternative to the journal Machine Learning. In 2001, forty editorial board members of Machine Learning resigned, saying that in the era of the Internet, it was detrimental for researchers to continue publishing their papers in expensive journals with pay-access archives. The open access model employed by the Journal of Machine Learning Research allows authors to publish articles for free and retain copyright, while archives are freely available online.[2]
Print editions of the journal were published by MIT Press until 2004 and by Microtome Publishing thereafter. From its inception, the journal received no revenue from the print edition and paid no subvention to MIT Press or Microtome Publishing.[1]
In response to the prohibitive costs of arranging workshop and conference proceedings publication with traditional academic publishing companies, the journal launched a proceedings publication arm in 2007[3] and now publishes proceedings for several leading machine learning conferences, including the International Conference on Machine Learning, COLT, AISTATS, and workshops held at the Conference on Neural Information Processing Systems.
References
- ^ a b Shieber, Stuart (6 March 2012). "An efficient journal". The Occasional Pamphlet. Retrieved 12 February 2017.
- ^ "Editorial Board of the Kluwer Journal, Machine Learning: Resignation Letter". SIGIR Forum. 35 (2). 2001.
- ^ Lawrence, Neil (30 March 2015). "Proceedings of Machine Learning Research". Inverseprobability. Retrieved 12 February 2017.
Further reading
- "Top journals in computer science". Times Higher Education. 14 May 2009. Retrieved 22 August 2009.
External links
- Official website
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Differentiable computing
- Gradient descent
- Clustering
- Regression
- Hallucination
- Adversary
- Attention
- Convolution
- Loss functions
- Backpropagation
- Batchnorm
- Activation
- Regularization
- Datasets
- Diffusion
- Autoregression
- TensorFlow
- PyTorch
- Keras
- Theano
- JAX
- Flux.jl
- MindSpore
| Audio–visual | |
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| Verbal |
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| Decisional |
- Yoshua Bengio
- Alex Graves
- Ian Goodfellow
- Stephen Grossberg
- Demis Hassabis
- Geoffrey Hinton
- Yann LeCun
- Fei-Fei Li
- Andrew Ng
- Jürgen Schmidhuber
- David Silver
- Ilya Sutskever
- Neural Turing machine
- Differentiable neural computer
- Transformer
- Recurrent neural network (RNN)
- Long short-term memory (LSTM)
- Gated recurrent unit (GRU)
- Echo state network
- Multilayer perceptron (MLP)
- Convolutional neural network
- Residual neural network
- Mamba
- Autoencoder
- Variational autoencoder (VAE)
- Generative adversarial network (GAN)
- Graph neural network
Portals - Computer programming
- Technology
Categories - Artificial neural networks
- Machine learning
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