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.
Recognizing indoor scenes
2009904 citationsAriadna Quattoni, Antonio Torralba2009 IEEE Conference on Computer Vision and Pattern Recognitionprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Ariadna Quattoni
Since
Specialization
Citations
This map shows the geographic impact of Ariadna Quattoni'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 Ariadna Quattoni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ariadna Quattoni more than expected).
Fields of papers citing papers by Ariadna Quattoni
This network shows the impact of papers produced by Ariadna Quattoni. 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 Ariadna Quattoni. The network helps show where Ariadna Quattoni may publish in the future.
Co-authorship network of co-authors of Ariadna Quattoni
This figure shows the co-authorship network connecting the top 25 collaborators of Ariadna Quattoni.
A scholar is included among the top collaborators of Ariadna Quattoni 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 Ariadna Quattoni. Ariadna Quattoni is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Quattoni, Ariadna, Xavier Carreras, & Matthias Gallé. (2017). A Maximum Matching Algorithm for Basis Selection in Spectral Learning. International Conference on Artificial Intelligence and Statistics. 1477–1485.2 indexed citations
Quattoni, Ariadna, Borja Balle, Xavier Carreras, & Amir Globerson. (2014). Spectral Regularization for Max-Margin Sequence Tagging. QRU Quaderns de Recerca en Urbanisme. 1710–1718.7 indexed citations
11.
Carreras, Xavier, et al.. (2013). Unsupervised spectral learning of FSTs. RECERCAT (Consorci de Serveis Universitaris de Catalunya). 800–808.2 indexed citations
12.
Carreras, Xavier, et al.. (2013). Unsupervised Spectral Learning of Finite State Transducers. RECERCAT (Consorci de Serveis Universitaris de Catalunya). 26. 800–808.8 indexed citations
Luque, Franco M., Ariadna Quattoni, Borja Balle, & Xavier Carreras. (2012). Spectral Learning for Non-Deterministic Dependency Parsing. RECERCAT (Consorci de Serveis Universitaris de Catalunya). 409–419.17 indexed citations
15.
Quattoni, Ariadna, Xavier Carreras, Michael Collins, & Trevor Darrell. (2009). An efficient projection for l1 , ∞ regularization. DSpace@MIT (Massachusetts Institute of Technology). 857–864.84 indexed citations
16.
Quattoni, Ariadna & Antonio Torralba. (2009). Recognizing indoor scenes. 2009 IEEE Conference on Computer Vision and Pattern Recognition. 413–420.904 indexed citations breakdown →
17.
Quattoni, Ariadna, Xavier Carreras, Michael Collins, & Trevor Darrell. (2008). A Projected Subgradient Method for Scalable Multi-Task Learning. DSpace@MIT (Massachusetts Institute of Technology).1 indexed citations
18.
Urtasun, Raquel, Ariadna Quattoni, Neil D. Lawrence, & Trevor Darrell. (2008). Transferring Nonlinear Representations using Gaussian Processes with a Shared Latent Space. DSpace@MIT (Massachusetts Institute of Technology).2 indexed citations
19.
Quattoni, Ariadna, et al.. (2007). Hidden Conditional Random Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence. 29(10). 1848–1852.337 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.