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.
Sparse Bayesian Learning for Basis Selection
20041.1k citationsDavid Wipf, Bhaskar D. Raoprofile →
An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem
2007667 citationsDavid Wipf, Bhaskar D. Raoprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of David Wipf'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 David Wipf with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Wipf more than expected).
This network shows the impact of papers produced by David Wipf. 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 David Wipf. The network helps show where David Wipf may publish in the future.
Co-authorship network of co-authors of David Wipf
This figure shows the co-authorship network connecting the top 25 collaborators of David Wipf.
A scholar is included among the top collaborators of David Wipf 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 David Wipf. David Wipf is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hu, Xiaobin, Wenqi Ren, Jiaolong Yang, et al.. (2021). Face Restoration via Plug-and-Play 3D Facial Priors. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(12). 8910–8926.24 indexed citations
5.
Wang, Ziyu, Bin Dai, David Wipf, & Jun Zhu. (2020). Further Analysis of Outlier Detection with Deep Generative Models. Neural Information Processing Systems. 33. 8982–8992.1 indexed citations
Dai, Bin, Chen Zhu, Baining Guo, & David Wipf. (2018). Compressing Neural Networks using the Variational Information Bottleneck.. International Conference on Machine Learning. 1135–1144.28 indexed citations
8.
Dai, Bin, et al.. (2017). Veiled Attributes of the Variational Autoencoder.. arXiv (Cornell University).1 indexed citations
9.
Wang, Yu, Bin Dai, Gang Hua, John A. D. Aston, & David Wipf. (2017). Green Generative Modeling: Recycling Dirty Data using Recurrent Variational Autoencoders.. Uncertainty in Artificial Intelligence.2 indexed citations
10.
Xin, Bo, Yizhou Wang, Wen Gao, & David Wipf. (2017). Data-Dependent Sparsity for Subspace Clustering.. Uncertainty in Artificial Intelligence.4 indexed citations
11.
Fan, Qingnan, David Wipf, Gang Hua, & Baoquan Chen. (2017). Revisiting Deep Image Smoothing and Intrinsic Image Decomposition.. arXiv (Cornell University).6 indexed citations
12.
Xin, Bo, Yizhou Wang, Wen Gao, David Wipf, & Baoyuan Wang. (2016). Maximal Sparsity with Deep Networks. Neural Information Processing Systems. 29. 4340–4348.39 indexed citations
13.
Oh, Tae-Hyun, Yasuyuki Matsushita, In-So Kweon, & David Wipf. (2016). A Pseudo-Bayesian Algorithm for Robust PCA. Open Access System for Information Sharing (Pohang University of Science and Technology). 29. 1390–1398.6 indexed citations
14.
Wu, Yi, et al.. (2015). Understanding and Evaluating Sparse Linear Discriminant Analysis. International Conference on Artificial Intelligence and Statistics. 1070–1078.14 indexed citations
15.
Stahlhut, Carsten, et al.. (2012). Probabilistic M/EEG source imaging from sparse spatio-temporal event structure. Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU).1 indexed citations
16.
Wipf, David. (2011). Sparse Estimation with Structured Dictionaries. Neural Information Processing Systems. 24. 2016–2024.29 indexed citations
17.
Owen, Julia P., Hagai Attias, Kensuke Sekihara, Srikantan S. Nagarajan, & David Wipf. (2008). Estimating the Location and Orientation of Complex, Correlated Neural Activity using MEG. Neural Information Processing Systems. 21. 1777–1784.11 indexed citations
18.
Wipf, David, Jason Palmer, Bhaskar D. Rao, & Kenneth Kreutz-Delgado. (2007). Performance Analysis of Latent Variable Models with Sparse Priors. International Conference on Acoustics, Speech, and Signal Processing.7 indexed citations
19.
Wipf, David & Bhaskar D. Rao. (2004). ℓâ‚€-norm Minimization for Basis Selection. Neural Information Processing Systems. 17. 1513–1520.19 indexed citations
20.
Palmer, Jason, Bhaskar D. Rao, & David Wipf. (2003). Perspectives on Sparse Bayesian Learning. Neural Information Processing Systems. 16. 249–256.74 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.