Journal of Machine Learning Research
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In The Last Decade
Journal of Machine Learning Research
2.0k papers receiving 187.0k citations
Fields of papers published in Journal of Machine Learning Research
This network shows the impact of papers published in Journal of Machine Learning Research. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Journal of Machine Learning Research.
Countries where authors publish in Journal of Machine Learning Research
This map shows the geographic impact of research published in Journal of Machine Learning Research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers published in Journal of Machine Learning Research with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Journal of Machine Learning Research more than expected).
- Visualizing Data using t-SNE (2008)
- Dropout: a simple way to prevent neural networks from overfitting (2014)
- Latent dirichlet allocation (2003)
- Statistical Comparisons of Classifiers over Multiple Data Sets (2006)
- Understanding the difficulty of training deep feedforward neural networks (2010)
- An introduction to variable and feature selection (2003)
- Adaptive Subgradient Methods for Online Learning and Stochastic Optimization (2010)
- Scikit-learn: Machine Learning in Python (2011)
- LIBLINEAR: A Library for Large Linear Classification (2008)
- Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion (2010)
- Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion (2010)
- Distance Metric Learning for Large Margin Nearest Neighbor Classification (2009)
- Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples (2006)
- An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons (2008)
- Working Set Selection Using Second Order Information for Training Support Vector Machines (2005)
- Large Margin Methods for Structured and Interdependent Output Variables (2005)
- A comprehensive survey on safe reinforcement learning (2015)
- No Unbiased Estimator of the Variance of K-Fold Cross-Validation (2003)
- On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning (2005)
- Tensor decompositions for learning latent variable models (2014)
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar’s output or impact.