Citations per year, relative to Jim Mutch Jim Mutch (= 1×)
peers
Stanley Bileschi
Countries citing papers authored by Jim Mutch
Since
Specialization
Citations
This map shows the geographic impact of Jim Mutch'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 Jim Mutch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jim Mutch more than expected).
This network shows the impact of papers produced by Jim Mutch. 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 Jim Mutch. The network helps show where Jim Mutch may publish in the future.
Co-authorship network of co-authors of Jim Mutch
This figure shows the co-authorship network connecting the top 25 collaborators of Jim Mutch.
A scholar is included among the top collaborators of Jim Mutch 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 Jim Mutch. Jim Mutch is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Anselmi, Fabio, Joel Z. Leibo, Lorenzo Rosasco, et al.. (2014). Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning?.14 indexed citations
3.
Poggio, Tomaso, Jim Mutch, Joel Z. Leibo, Lorenzo Rosasco, & Andrea Tacchetti. (2012). The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work).. DSpace@MIT (Massachusetts Institute of Technology).13 indexed citations
4.
Leibo, Joel Z., Jim Mutch, & Tomaso Poggio. (2011). Why The Brain Separates Face Recognition From Object Recognition. DSpace@MIT (Massachusetts Institute of Technology). 24. 711–719.21 indexed citations
5.
Işık, Leyla, Joel Z. Leibo, Jim Mutch, Sang Wan Lee, & Tomaso Poggio. (2011). A hierarchical model of peripheral vision. DSpace@MIT (Massachusetts Institute of Technology).4 indexed citations
6.
Mutch, Jim, Joel Z. Leibo, Steve Smale, Lorenzo Rosasco, & Tomaso Poggio. (2010). Neurons That Confuse Mirror-Symmetric Object Views. DSpace@MIT (Massachusetts Institute of Technology).1 indexed citations
7.
Leibo, Joel Z., Jim Mutch, Shimon Ullman, & Tomaso Poggio. (2010). From primal templates to invariant recognition. DSpace@MIT (Massachusetts Institute of Technology).2 indexed citations
8.
Leibo, Joel Z., Jim Mutch, Lorenzo Rosasco, Shimon Ullman, & Tomaso Poggio. (2010). Learning Generic Invariances in Object Recognition: Translation and Scale. DSpace@MIT (Massachusetts Institute of Technology).10 indexed citations
9.
Poggio, Tomaso, Ulf Knoblich, & Jim Mutch. (2010). CNS: a GPU-based framework for simulating cortically-organized networks. DSpace@MIT (Massachusetts Institute of Technology).65 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.