Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Breast cancer classification and prognosis based on gene expression profiles from a population-based study
Countries citing papers authored by Philip M. Long
Since
Specialization
Citations
This map shows the geographic impact of Philip M. Long's 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 Philip M. Long with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philip M. Long more than expected).
This network shows the impact of papers produced by Philip M. Long. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Philip M. Long. The network helps show where Philip M. Long may publish in the future.
Co-authorship network of co-authors of Philip M. Long
This figure shows the co-authorship network connecting the top 25 collaborators of Philip M. Long.
A scholar is included among the top collaborators of Philip M. Long based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Philip M. Long. Philip M. Long is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Chatterji, Niladri S. & Philip M. Long. (2020). Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime. Journal of Machine Learning Research. 22(129). 1–30.2 indexed citations
2.
Long, Philip M. & Hanie Sedghi. (2019). Size-free generalization bounds for convolutional neural networks. arXiv (Cornell University).6 indexed citations
3.
Sedghi, Hanie, Vineet Gupta, & Philip M. Long. (2018). The Singular Values of Convolutional Layers. International Conference on Learning Representations.29 indexed citations
4.
Bartlett, Peter L., David P. Helmbold, & Philip M. Long. (2018). Gradient descent with identity initialization efficiently learns positive definite linear transformations.. International Conference on Machine Learning. 520–529.3 indexed citations
5.
Helmbold, David P. & Philip M. Long. (2015). On the inductive bias of dropout. Journal of Machine Learning Research. 16(1). 3403–3454.7 indexed citations
6.
Long, Philip M.. (2014). Staff and students’ conceptions of good written feedback: Implications for Practice. Insight (University of Cumbria). 8(1). 54–63.8 indexed citations
7.
Long, Philip M. & Rocco A. Servedio. (2011). Algorithms and hardness results for parallel large margin learning. Journal of Machine Learning Research. 14(1). 1314–1322.2 indexed citations
8.
Bshouty, Nader H. & Philip M. Long. (2010). Finding Planted Partitions in Nearly Linear Time using Arrested Spectral Clustering. International Conference on Machine Learning. 135–142.6 indexed citations
9.
Long, Philip M. & Rocco A. Servedio. (2010). Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate. International Conference on Machine Learning. 703–710.24 indexed citations
Abe, Naoki & Philip M. Long. (1999). Associative Reinforcement Learning using Linear Probabilistic Concepts. International Conference on Machine Learning. 3–11.25 indexed citations
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.