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.
From learning models of natural image patches to whole image restoration
This map shows the geographic impact of Daniel Zoran'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 Daniel Zoran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Zoran more than expected).
This network shows the impact of papers produced by Daniel Zoran. 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 Daniel Zoran. The network helps show where Daniel Zoran may publish in the future.
Co-authorship network of co-authors of Daniel Zoran
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Zoran.
A scholar is included among the top collaborators of Daniel Zoran 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 Daniel Zoran. Daniel Zoran is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kosiorek, Adam R., Heiko Strathmann, Daniel Zoran, et al.. (2021). NeRF-VAE: A Geometry Aware 3D Scene Generative Model. International Conference on Machine Learning. 5742–5752.19 indexed citations
Greff, Klaus, Rishabh Kabra, Christopher Burgess, et al.. (2019). Multi-Object Representation Learning with Iterative Variational Inference. arXiv (Cornell University). 2424–2433.13 indexed citations
6.
Zoran, Daniel, et al.. (2018). LaVAN: Localized and Visible Adversarial Noise. International Conference on Machine Learning. 2507–2515.23 indexed citations
7.
Ruderman, Avraham, Neil C. Rabinowitz, Ari S. Morcos, & Daniel Zoran. (2018). Learned Deformation Stability in Convolutional Neural Networks.. arXiv (Cornell University).6 indexed citations
8.
Mott, A., Daniel Zoran, Mike Chrzanowski, Daan Wierstra, & Danilo Jimenez Rezende. (2018). S3TA: A Soft, Spatial, Sequential, Top-Down Attention Model.1 indexed citations
9.
Watters, Nicholas, Daniel Zoran, Théophane Weber, et al.. (2017). Visual Interaction Networks: Learning a Physics Simulator from Video. Neural Information Processing Systems. 30. 4539–4547.74 indexed citations
10.
Bornschein, Jörg, Andriy Mnih, Daniel Zoran, & Danilo Jimenez Rezende. (2017). Variational Memory Addressing in Generative Models. Neural Information Processing Systems. 30. 3920–3929.4 indexed citations
11.
Zoran, Daniel, Phillip Isola, Dilip Krishnan, & William T. Freeman. (2015). Learning Ordinal Relationships for Mid-Level Vision. DSpace@MIT (Massachusetts Institute of Technology). 388–396.95 indexed citations
12.
Zoran, Daniel, Dilip Krishnan, José Bento, & Bill Freeman. (2014). Shape and Illumination from Shading using the Generic Viewpoint Assumption. DSpace@MIT (Massachusetts Institute of Technology). 27. 226–234.11 indexed citations
Rosenbaum, Dan, Daniel Zoran, & Yair Weiss. (2013). Learning the Local Statistics of Optical Flow. Neural Information Processing Systems. 26. 2373–2381.15 indexed citations
15.
Zoran, Daniel & Yair Weiss. (2012). Natural Images, Gaussian Mixtures and Dead Leaves. Neural Information Processing Systems. 25. 1736–1744.58 indexed citations
16.
Zoran, Daniel & Yair Weiss. (2011). From learning models of natural image patches to whole image restoration. 479–486.961 indexed citations breakdown →
17.
Zoran, Daniel & Yair Weiss. (2009). The 'tree-dependent components' of natural scenes are edge filters. Neural Information Processing Systems. 22. 2340–2347.5 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.