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Nicholas Pinto - Publications

Affiliations: 
Massachusetts Institute of Technology, Cambridge, MA, United States 
Area:
object recognition, computation
Website:
http://pinto.scripts.mit.edu/
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14 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2015 Bergstra J, Pinto N, Cox DD. SkData: Data sets and algorithm evaluation protocols in Python Computational Science and Discovery. 8. DOI: 10.1088/1749-4699/8/1/014007  0.351
2014 Cadieu CF, Hong H, Yamins DL, Pinto N, Ardila D, Solomon EA, Majaj NJ, DiCarlo JJ. Deep neural networks rival the representation of primate IT cortex for core visual object recognition. Plos Computational Biology. 10: e1003963. PMID 25521294 DOI: 10.1371/Journal.Pcbi.1003963  0.512
2014 Chiachia G, Falcão AX, Pinto N, Rocha A, Cox D. Learning person-specific representations from faces in the wild Ieee Transactions On Information Forensics and Security. 9: 2089-2099. DOI: 10.1109/TIFS.2014.2359543  0.501
2012 Chiachia G, Pinto N, Schwartz WR, Rocha A, Falcão AX, Cox D. Person-specific subspace analysis for unconstrained familiar face identification Bmvc 2012 - Electronic Proceedings of the British Machine Vision Conference 2012. DOI: 10.5244/C.26.101  0.375
2012 Bergstra J, Pinto N, Cox D. Machine learning for predictive auto-tuning with boosted regression trees 2012 Innovative Parallel Computing, Inpar 2012. DOI: 10.1109/InPar.2012.6339587  0.301
2012 Pinto N, Cox DD. High-throughput-derived biologically-inspired features for unconstrained face recognition Image and Vision Computing. 30: 159-168. DOI: 10.1016/j.imavis.2011.12.009  0.45
2012 Pinto N, Cox DD. GPU Metaprogramming: A Case Study in Biologically Inspired Machine Vision Gpu Computing Gems Jade Edition. 457-471. DOI: 10.1016/B978-0-12-385963-1.00033-2  0.328
2012 Pinto N, Cox D. An evaluation of the invariance properties of a biologically-inspired system for unconstrained face recognition Lecture Notes of the Institute For Computer Sciences, Social-Informatics and Telecommunications Engineering. 87: 505-518. DOI: 10.1007/978-3-642-32615-8_48  0.425
2011 Pinto N, Barhomi Y, Cox DD, DiCarlo JJ. Comparing state-of-the-art visual features on invariant object recognition tasks 2011 Ieee Workshop On Applications of Computer Vision, Wacv 2011. 463-470. DOI: 10.1109/WACV.2011.5711540  0.665
2011 Cox D, Pinto N. Beyond simple features: A large-scale feature search approach to unconstrained face recognition 2011 Ieee International Conference On Automatic Face and Gesture Recognition and Workshops, Fg 2011. 8-15. DOI: 10.1109/FG.2011.5771385  0.444
2011 Pinto N, Stone Z, Zickler T, Cox D. Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook Ieee Computer Society Conference On Computer Vision and Pattern Recognition Workshops. DOI: 10.1109/CVPRW.2011.5981788  0.417
2009 Pinto N, Doukhan D, DiCarlo JJ, Cox DD. A high-throughput screening approach to discovering good forms of biologically inspired visual representation. Plos Computational Biology. 5: e1000579. PMID 19956750 DOI: 10.1371/journal.pcbi.1000579  0.641
2009 Pinto N, DiCarlo JJ, Cox DD. How far can you get with a modern face recognition test set using only simple features? 2009 Ieee Computer Society Conference On Computer Vision and Pattern Recognition Workshops, Cvpr Workshops 2009. 2591-2598. DOI: 10.1109/CVPRW.2009.5206605  0.604
2008 Pinto N, Cox DD, DiCarlo JJ. Why is real-world visual object recognition hard? Plos Computational Biology. 4: e27. PMID 18225950 DOI: 10.1371/journal.pcbi.0040027  0.656
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