Nicolas Pinto

According to our database1, Nicolas Pinto authored at least 20 papers between 2008 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2022
HEAR 2021: Holistic Evaluation of Audio Representations.
CoRR, 2022

2021

2020
Experience Grounds Language.
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

2014
Learning Person-Specific Representations From Faces in the Wild.
IEEE Trans. Inf. Forensics Secur., 2014

Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition.
PLoS Comput. Biol., 2014

2013
GPU Scripting and Code Generation with PyCUDA
CoRR, 2013

The Neural Representation Benchmark and its Evaluation on Brain and Machine
Proceedings of the 1st International Conference on Learning Representations, 2013

SkData: Data Sets and Algorithm Evaluation Protocols in Python.
Proceedings of the 12th Python in Science Conference, 2013


2012
PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation.
Parallel Comput., 2012

High-throughput-derived biologically-inspired features for unconstrained face recognition.
Image Vis. Comput., 2012

Person-Specific Subspace Analysis for Unconstrained Familiar Face Identification.
Proceedings of the British Machine Vision Conference, 2012

2011
Comparing state-of-the-art visual features on invariant object recognition tasks.
Proceedings of the IEEE Workshop on Applications of Computer Vision (WACV 2011), 2011

Beyond simple features: A large-scale feature search approach to unconstrained face recognition.
Proceedings of the Ninth IEEE International Conference on Automatic Face and Gesture Recognition (FG 2011), 2011

Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2011

2010
An Evaluation of the Invariance Properties of a Biologically-Inspired System for Unconstrained Face Recognition.
Proceedings of the Bio-Inspired Models of Network, Information, and Computing Systems, 2010

2009
A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation.
PLoS Comput. Biol., 2009

PyCUDA: GPU Run-Time Code Generation for High-Performance Computing
CoRR, 2009

How far can you get with a modern face recognition test set using only simple features?.
Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 2009

2008
Why is Real-World Visual Object Recognition Hard?
PLoS Comput. Biol., 2008


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