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.
Classification and clustering via dictionary learning with structured incoherence and shared features
2010532 citationsIgnacio Ramírez, Pablo Sprechmann et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Pablo Sprechmann
Since
Specialization
Citations
This map shows the geographic impact of Pablo Sprechmann'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 Pablo Sprechmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pablo Sprechmann more than expected).
Fields of papers citing papers by Pablo Sprechmann
This network shows the impact of papers produced by Pablo Sprechmann. 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 Pablo Sprechmann. The network helps show where Pablo Sprechmann may publish in the future.
Co-authorship network of co-authors of Pablo Sprechmann
This figure shows the co-authorship network connecting the top 25 collaborators of Pablo Sprechmann.
A scholar is included among the top collaborators of Pablo Sprechmann 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 Pablo Sprechmann. Pablo Sprechmann is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Sprechmann, Pablo, Steven Hansen, André Barreto, et al.. (2021). Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning. arXiv (Cornell University).1 indexed citations
3.
Badia, Adrià Puigdomènech, Bilal Piot, Steven Kapturowski, et al.. (2020). Agent57: Outperforming the Atari Human Benchmark. International Conference on Machine Learning. 1. 507–517.71 indexed citations
4.
Tompson, Jonathan, et al.. (2016). Accelerating Eulerian Fluid Simulation With Convolutional Networks. arXiv (Cornell University).9 indexed citations
Arias, Pablo, et al.. (2005). Segmentación con información a priori de forma aplicada a Sistema de Valoración Cárnica.1 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.