The neural representation benchmark and its evaluation on brain and machine

1st International Conference on Learning Representations, ICLR 2013 - Conference Track Proceedings

Volumn , Issue , 2013, Pages

  Cadieu, Charles F ^a   Hong, Ha ^a   Yamins, Dan ^a   Pinto, Nicolas ^a   Majaj, Najib J ^a   DiCarlo, James J ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; LEARNING SYSTEMS;

COMPUTER VISION ALGORITHMS; EFFECTIVE LEARNING; EIGEN DECOMPOSITION; MACHINE REPRESENTATIONS; NEURAL REPRESENTATIONS; SUPERVISED ALGORITHM; THREE-LAYER MODELS; VISUAL REPRESENTATIONS;

LEARNING ALGORITHMS;

EID: 85083951943     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (44)

1. CI Baker, M Behrmann, and CR Olson. Impact of learning on representation of parts and wholes in monkey inferotemporal cortex. Nature Neuroscience, 2002.
2. ML Braun. Accurate Error Bounds for the Eigenvalues of the Kernel Matrix. JMLR, 2006.
3. ML Braun, JM Buhmann, and KR Müller. On relevant dimensions in kernel feature spaces. JMLR, 2008.
4. RT Canolty, K Ganguly, SW Kennerley, CF Cadieu, K Koepsell, JD Wallis, and JM Carmena. Oscillatory phase coupling coordinates anatomically dispersed functional cell assemblies. PNAS, 2010.
5. MM Churchland, JP Cunningham, MT Kaufman, JD Foster, P Nuyujukian, SI Ryu, and KV Shenoy. Neural population dynamics during reaching. Nature, 2012.
6. A Coates, A Karpathy, and A Ng. Emergence of Object-Selective Features in Unsupervised Feature Learning. NIPS, 2012.
7. JJ DiCarlo, D Zoccolan, and NC Rust. How Does the Brain Solve Visual Object Recognition? Neuron, 2012.
8. M Fabre-Thorpe, G Richard, and SJ Thorpe. Rapid categorization of natural images by rhesus monkeys. Neuroreport, 1998.
9. DJ Felleman and DC Van Essen. Distributed hierarchical processing in the primate cerebral cortex. Cerebral cortex, 1991.
10. K Fukushima. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological cybernetics, 1980.
11. CP Hung, G Kreiman, T Poggio, and JJ DiCarlo. Fast readout of object identity from macaque inferior temporal cortex. Science, 2005.
12. R Kiani, H Esteky, K Mirpour, and K Tanaka. Object category structure in response patterns of neuronal population in monkey inferior temporal cortex. Journal of Neurophysiology, 2007.
13. E Kobatake, G Wang, and K Tanaka. Effects of shape-discrimination training on the selectivity of inferotemporal cells in adult monkeys. Journal of Neurophysiology, 1998.
14. N Kriegeskorte, Mur M, and Bandettini P. Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience. Frontiers in Systems Neuroscience, 2008a.
15. N Kriegeskorte, M Mur, DA Ruff, R Kiani, J Bodurka, H Esteky, K Tanaka, and PA Bandettini. Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron, 2008b.
16. A Krizhevsky, I Sutskever, and G Hinton. ImageNet classification with deep convolutional neural networks. NIPS, 2012.
17. QV Le, R Monga, M Devin, K Chen, GS Corrado, J Dean, and AY Ng. Building high-level features using large scale unsupervised learning. ICML, 2012.
18. N Li and JJ DiCarlo. Unsupervised Natural Visual Experience Rapidly Reshapes Size-Invariant Object Representation in Inferior Temporal Cortex. Neuron, 2010.
19. DG Lowe. Towards a computational model for object recognition in IT cortex. In BMVC, 2000.
20. DG Lowe. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 2004.
21. N Majaj, H Hong, E Solomon, and JJ DiCarlo. A unified neuronal population code fully explains human object recognition. In COSYNE, 2012.
22. G Montavon and KR Müller. Deep Boltzmann Machines and the Centering Trick. Neural Networks: Tricks of the Trade, 2012.
23. G Montavon, ML Braun, and KR Müller. Kernel Analysis of Deep Networks. JMLR, 2011.
24. M Mur, DA Ruff, J Bodurka, P De Weerd, PA Bandettini, and N Kriegeskorte. Categorical, Yet Graded – Single-Image Activation Profiles of Human Category-Selective Cortical Regions. The Journal of Neuroscience, 2012.
25. A Oliva and A Torralba. The role of context in object recognition. Trends in Cognitive Sciences, 2007.
26. GA Orban. Higher order visual processing in macaque extrastriate cortex. Physiological Reviews, 2008.
27. N Pinto and D Cox. Beyond simple features: A large-scale feature search approach to unconstrained face recognition. FG, 2011.
28. N Pinto, David D Cox, and JJ DiCarlo. Why is Real-World Visual Object Recognition Hard? PLoS Computational Biology, 2008.
29. N Pinto, D Doukhan, JJ DiCarlo, and DD Cox. A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation. PLoS Computational Biology, 2009.
30. N Pinto, N Majaj, Y Barhomi, E Solomon, and JJ DiCarlo. Human versus machine: comparing visual object recognition systems on a level playing field. In COSYNE, 2010.
31. N Pinto, Y Barhomi, DD Cox, and JJ DiCarlo. Comparing state-of-the-art visual features on invariant object recognition tasks. WACV, 2011.
32. M Riesenhuber and T Poggio. Hierarchical models of object recognition in cortex. Nature Neuroscience, 1999.
33. F Rosenblatt. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological review, 1958.
34. NC Rust and JJ DiCarlo. Selectivity and tolerance (“invariance”) both increase as visual information propagates from cortical area V4 to IT. Journal of Neuroscience, 2010.
35. T Serre, L Wolf, S Bileschi, M Riesenhuber, and T Poggio. Robust Object Recognition with Cortex-Like Mechanisms. PAMI, 2007.
36. N Sigala and NK Logothetis. Visual categorization shapes feature selectivity in the primate temporal cortex. Nature, 2002.
37. AJ Smola, B Schölkopf, and KR Müller. The connection between regularization operators and support vector kernels. Neural Networks, 1998.
38. IH Stevenson and KP Kording. How advances in neural recording affect data analysis. Nature Neuroscience, 2011.
39. IH Stevenson, BM London, ER Oby, and NA Sachs. Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons. PLOS Computational Biology, 2012.
40. SM Stringer and ET Rolls. Invariant Object Recognition in the Visual System with Novel Views of 3D Objects. Neural Computation, 2002.
41. DC Van Essen. Organization of Visual Areas in Macaque and Human Cerebral Cortex. In The Visual Neuro-sciences. 2003.
42. G Wallis and ET Rolls. Invariant Face and Object Recognition in the Visual System. Progress in Neurobiology, 1997.
43. MC Wu, SV David, and JL Gallant. Complete functional characterization of sensory neurons by system identification. Annual Review of Neuroscience, 2006.
44. Y Zhang, EM Meyers, NP Bichot, T Serre, T Poggio, and R Desimone. Object decoding with attention in inferior temporal cortex. PNAS, 2011.