( en | de | ru )

 

Disambiguation of "Pinto, Nicolas"

Author of the publication

copydeleteadd this publication to your clipboard
A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation.

, , , and . PLoS Comput. Biol., (2009)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Sonia Maria Pinto Escoval

Elizabeth Pinto

Mariano Aurelio Ernesto Pinto-Bazurco Mendieta

Sofia Pinto

Christian Pinto

 

Other publications of authors with the same name

High-throughput-derived biologically-inspired features for unconstrained face recognition., and . Image Vis. Comput., 30 (3): 159-168 (2012)Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition., , , , , , , and . PLoS Comput. Biol., (2014)PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation, , , , , and . Parallel Computing, 38 (3): 157--174 (March 2012)The Neural Representation Benchmark and its Evaluation on Brain and Machine, , , , , and . ICLR, (2013)How far can you get with a modern face recognition test set using only simple features?., , and . CVPR, page 2591-2598. IEEE Computer Society, (2009)HEAR 2021: Holistic Evaluation of Audio Representations., , , , , , , , , and 13 other author(s). CoRR, (2022)Comparing state-of-the-art visual features on invariant object recognition tasks., , , and . WACV, page 463-470. IEEE Computer Society, (2011)SkData: Data Sets and Algorithm Evaluation Protocols in Python., , and . SciPy, page 20-26. scipy.org, (2013)An Evaluation of the Invariance Properties of a Biologically-Inspired System for Unconstrained Face Recognition., and . BIONETICS, volume 87 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, page 505-518. Springer, (2010)Why is Real-World Visual Object Recognition Hard?, , and . PLoS Comput. Biol., (2008)