Publications

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2024
Peters B, DiCarlo JJ, Gureckis T, et al. How does the primate brain combine generative and discriminative computations in vision?. 2024. doi: https://doi.org/10.48550/arXiv.2401.06005. (7.22 MB)
Xie Y, Alter E, Schwartz J, DiCarlo JJ. Learning only a handful of latent variables produces neural-aligned CNN models of the ventral stream. In: Computational and Systems Neuroscience (COSYNE) . Computational and Systems Neuroscience (COSYNE) . Lisbon, Portugal: Computational and Systems Neuroscience (COSYNE); 2024. doi:https://hdl.handle.net/1721.1/153744. (2.57 MB)
2023
Zador A, Escola S, Richards B, et al. Catalyzing next-generation Artificial Intelligence through NeuroAI. Nature Communications. 2023;14(1):1597. doi:10.1038/s41467-023-37180-x. (749.44 KB)
Lee MJ, DiCarlo JJ. An empirical assay of view-invariant object learning in humans and comparison with baseline image-computable models. bioRxiv. 2023:2022.12.31.522402. doi:https://www.biorxiv.org/content/10.1101/2022.12.31.522402v1.
Mieczkowski E, Abate A, De Faria W, et al. fROI-level computational models enable broad-scale experimental testing and expose key divergences between models and brains. Journal of Vision. 2023;23(9):5788 - 5788. Available at: https://doi.org/10.1167/jov.23.9.5788.
Lee MJ, DiCarlo JJ. How well do rudimentary plasticity rules predict adult visual object learning?. Kietzmann TChristian. PLOS Computational Biology. 2023;19(12):e1011713. doi:10.1371/journal.pcbi.1011713. (11.69 MB)
Lee MJ, DiCarlo JJ. How well do rudimentary plasticity rules predict adult visual object learning?. Kietzmann TChristian. PLOS Computational Biology. 2023;19(12):e1011713. doi:10.1371/journal.pcbi.1011713. (11.69 MB)
DiCarlo JJ, Yamins DLK, Ferguson ME, et al. Let's move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human visionAbstract. Behavioral and Brain Sciences. 2023;4634. doi:10.1017/S0140525X23001607. (2.56 MB)
Kuoch M, Chou C-N, Parthasarathy N, et al. Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds. In: Conference on Parsimony and Learning (Proceedings Track). Conference on Parsimony and Learning (Proceedings Track). Hong Kong, China; 2023. Available at: https://openreview.net/forum?id=MxBS6aw5Gd. (2.75 MB)
Kar K, DiCarlo JJ. The Quest for an Integrated Set of Neural Mechanisms Underlying Object Recognition in Primates. arXiv. 2023. doi: https://doi.org/10.48550/arXiv.2312.05956. (5.76 MB)
Gaziv G, Lee MJ, DiCarlo JJ. Robustified ANNs Reveal Wormholes Between Human Category Percepts. arXiv. 2023. doi: https://doi.org/10.48550/arXiv.2308.06887 Focus to learn more. (3.53 MB)
Gaziv G, Lee MJ, DiCarlo JJ. Strong and Precise Modulation of Human Percepts via Robustified ANNs. In: Neural Information Processing Systems. Neural Information Processing Systems. New Orleans, Louisiana; 2023. Available at: https://openreview.net/pdf?id=5GmTI4LNqX. (3.26 MB)
Margalit E, Lee H, Finzi D, DiCarlo JJ, Grill-Spector K, Yamins DLK. A Unifying Principle for the Functional Organization of Visual Cortex. bioRxiv. 2023. doi: https://doi.org/10.1101/2023.05.18.541361. (6.57 MB)
2022
Guo C, Lee MJ, Leclerc G, et al. Adversarially trained neural representations may already be as robust as corresponding biological neural representations. arXiv. 2022. doi:https://doi.org/10.48550/arXiv.2206.11228. (1.99 MB)
Dapello J, Kar K, Schrimpf M, et al. Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness. bioRxiv. 2022. doi:https://doi.org/10.1101/2022.07.01.498495.
Bagus AMarliawaty, Marques T, Sanghavi S, DiCarlo JJ, Schrimpf M. Primate Inferotemporal Cortex Neurons Generalize Better to Novel Image Distributions Than Analogous Deep Neural Networks Units. In: SVHRM Workshop at Neural Information Processing Systems (NeurIPS). SVHRM Workshop at Neural Information Processing Systems (NeurIPS). Lisbon, Portugal; 2022. Available at: https://openreview.net/pdf?id=iPF7mhoWkOl. (3.86 MB)
Geiger F, Schrimpf M, Marques T, DiCarlo JJ. Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream. In: International Conference on Learning Representations 2022 Spotlight. International Conference on Learning Representations 2022 Spotlight.; 2022. doi:10.1101/2020.06.08.140111. (1.45 MB)
2021
Kar K, Schrimpf M, Schmidt K, DiCarlo JJ. Chemogenetic suppression of macaque V4 neurons produces retinotopically specific deficits in downstream IT neural activity patterns and core object recognition behavior. Journal of Vision. 2021;21(9):2489-2489. doi:https://doi.org/10.1167/jov.21.9.2489.
Rajalingham R, Sorenson M, Azadi R, Bohn S, DiCarlo JJ, Afraz A. Chronically implantable LED arrays for behavioral optogenetics in primates. Nature Methods. 2021;18(9):1112 - 1116. doi:10.1038/s41592-021-01238-9. (6.95 MB)
Baidya A, Dapello J, DiCarlo JJ, Marques T. Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs. Shared Visual Representations in Human & Machine Intelligence - NeurIPS Workshop. 2021. Available at: https://arxiv.org/abs/2110.10645. (1000.31 KB)
Murty NAR, Bashivan P, Abate A, DiCarlo JJ, Kanwisher N. Computational models of category-selective brain regions enable high-throughput tests of selectivity. Nature Communications. 2021;12(1). doi:10.1038/s41467-021-25409-6. (6.47 MB)
Kar K, DiCarlo JJ. Fast Recurrent Processing Via Ventral Prefrontal Cortex is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition. Neuron. 2021;109(1):164-167.e5. doi:https://doi.org/10.1016/j.neuron.2020.09.035. (3.92 MB)
Marques T, Schrimpf M, DiCarlo JJ. Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior. bioRxiv. 2021. doi:10.1101/2021.03.01.433495. (3.12 MB)
Dapello J, Feather J, Marques T, et al. Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception. In: Neural Information Processing Systems (NeurIPS). Neural Information Processing Systems (NeurIPS). Lisbon, Portugal; 2021. Available at: https://proceedings.neurips.cc/paper/2021/file/8383f931b0cefcc631f070480ef340e1-Paper.pdf. (4.04 MB)
Gan C, Zhou S, Schwartz J, et al. The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI. arXiv. 2021. doi:arXiv:2103.14025. (6.79 MB)
Schrimpf M, Grath PMc, DiCarlo JJ. Topographic ANNs Predict the Behavioral Effects of Causal Perturbations in Primate Visual Ventral Stream IT. Champalimaud Research Symposium (CRS21). 2021. (3.47 MB)
Jia X, Hong H, DiCarlo JJ. Unsupervised changes in core object recognition behavior are predicted by neural plasticity in inferior temporal cortex. eLife. 2021;10. doi:10.7554/eLife.60830.
Zhuang C, Yan S, Nayebi A, et al. Unsupervised neural network models of the ventral visual stream. Proceedings of the National Academy of Sciences. 2021;118(3):e2014196118. doi:10.1073/pnas.2014196118. (2.71 MB)
2020
Rajalingham R, Sorenson M, Azadi R, Bohn S, DiCarlo JJ, Afraz A. Chronically implantable LED arrays for behavioral optogenetics in primates. bioRxiv. 2020. doi:10.1101/2020.09.10.291583. (2.64 MB)
Margalit E, Lee H, Marques T, DiCarlo JJ, Yamins DLK. Correlation-based spatial layout of deep neural network features generates ventral stream topography. Computation and Systems Neuroscience (COSYNE). 2020. Available at: http://cosyne.org/cosyne20/Cosyne2020_program_book.pdf.
Kar K, DiCarlo JJ. Fast recurrent processing via ventral prefrontal cortex is needed by the primate ventral stream for robust core visual object recognition. BioRxiv. 2020. doi:https://doi.org/10.1101/2020.05.10.086959.
Rajalingham R, Kar K, Sanghavi S, Dehaene S, DiCarlo JJ. The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys. Nature Communications. 2020;11(1). doi:10.1038/s41467-020-17714-3.
Schrimpf M, Kubilius J, Lee MJ, Murty NAR, Ajemian R, DiCarlo JJ. Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence. Neuron. 2020. doi:10.1016/j.neuron.2020.07.040. (1.04 MB)
Tremblay S, Acker L, Afraz A, et al. An Open Resource for Non-human Primate Optogenetics. Neuron. 2020. doi:10.1016/j.neuron.2020.09.027.
Dapello J, Marques T, Schrimpf M, Geiger F, Cox DD, DiCarlo JJ. Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations. Neural Information Processing Systems (NeurIPS; spotlight). 2020. doi:10.1101/2020.06.16.154542. (2.48 MB)
Gan C, Schwartz J, Alter S, et al. ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. arXiv. 2020. Available at: https://arxiv.org/abs/2007.04954. (7.06 MB)
Lee H, Margalit E, Jozwik KM, et al. Topographic deep artificial neural networks reproduce the hallmarks of the primate inferior temporal cortex face processing network. bioRxiv. 2020. doi:https://doi.org/10.1101/2020.07.09.185116.
Jia X, Hong H, DiCarlo JJ. Unsupervised changes in core object recognition behavioral performance are accurately predicted by unsupervised neural plasticity in inferior temporal cortex. BioRxiv. 2020. doi:https://doi.org/10.1101/2020.01.13.900837.
Zhuang C, Yan S, Nayebi A, et al. Unsupervised Neural Network Models of the Ventral Visual Stream. bioRxiv. 2020. doi:10.1101/2020.06.16.155556. (2.7 MB)
2019
Kubilius J, Schrimpf M, Hong H, et al. Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. In: Neural Information Processing Systems. Neural Information Processing Systems.; 2019. doi:https://papers.nips.cc/paper/9441-brain-like-object-recognition-with-high-performing-shallow-recurrent-anns.
Lee MJ, DiCarlo JJ. Comparing novel object learning in humans, models, and monkeys. Journal of Vision. 2019;19(10):114b. doi:10.1167/19.10.114b.
Kar K, Kubilius J, Schmidt K, Issa EB, DiCarlo JJ. Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior. Nature Neuroscience. 2019;22(6):974 - 983. doi:10.1038/s41593-019-0392-5.
Harris KD, Groh JM, DiCarlo JJ, et al. Funcitional Properties of Circuits, Cellular Populations, and Areas. In: Singer W, Sejnowski TJ, Rakic P The Neocortex.Vol 27. The Neocortex. Cambridge, MA: The MIT Press; 2019:223-265. doi:10.7551/mitpress/12593.001.0001. (1.06 MB)
Bashivan P, Kar K, DiCarlo JJ. Neural population control via deep image synthesis. Science. 2019;364(6439):eaav9436. doi:10.1126/science.aav9436.
Rajalingham R, DiCarlo JJ. Reversible Inactivation of Different Millimeter-Scale Regions of Primate IT Results in Different Patterns of Core Object Recognition Deficits. Neuron. 2019;102(2):493 - 505.e5. doi:10.1016/j.neuron.2019.02.001.
Jozwik KM, Schrimpf M, Kanwisher N, DiCarlo JJ. To find better neural network models of human vision, find better neural network models of primate vision. BioRxiv. 2019. doi:https://doi.org/10.1101/688390.
Schrimpf M, Kubilius J, Hong H, et al. Using Brain-Score to Evaluate and Build Neural Networks for Brain-Like Object Recognition. In: Computational and Systems Neuroscience (COSYNE). Computational and Systems Neuroscience (COSYNE). Denver, CO; 2019.
2018
Schrimpf M, Kubilius J, Hong H, et al. Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?. bioRxiv. 2018. doi:https://doi.org/10.1101/407007.
Kubilius J, Schrimpf M, Nayebi A, Bear D, Yamins DLK, DiCarlo JJ. CORnet: Modeling the Neural Mechanisms of Core Object Recognition. bioRxiv. 2018. doi:https://doi.org/10.1101/408385.
Batista AP, DiCarlo JJ. Deep learning reaches the motor system. Nature Methods. 2018;15(10):772 - 773. doi:10.1038/s41592-018-0152-6.
Kar K, Kubilius J, Schmidt K, Issa EB, DiCarlo JJ. Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior. bioRxiv. 2018. doi:https://doi.org/10.1101/354753.
Rajalingham R, Issa EB, Bashivan P, Kar K, Schmidt K, DiCarlo JJ. Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks. The Journal of Neuroscience. 2018;38(33):7255 - 7269. doi:10.1523/JNEUROSCI.0388-18.2018.
Rajalingham R, Issa EB, Bashivan P, Kar K, Schmidt K, DiCarlo JJ. Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks. bioRxiv. 2018. doi:https://doi.org/10.1101/240614.
Ohayon S, Caravaca-Aguirre A, Piestun R, DiCarlo JJ. Minimally invasive multimode optical fiber microendoscope for deep brain fluorescence imaging. Biomedical Optics Express. 2018;9(4):1492-1509. doi:10.1364/BOE.9.001492.
Issa EB, Cadieu CF, DiCarlo JJ. Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals. eLife. 2018;7. doi:10.7554/eLife.42870.
Rajalingham R, DiCarlo JJ. Reversible inactivation of different millimeter-scale regions of primate IT results in different patterns of core object recognition deficits. bioRxiv. 2018. doi:https://doi.org/10.1101/390245.
Nayebi A, Bear D, Kubilius J, et al. Task-Driven Convolutional Recurrent Models of the Visual System. arXiv. 2018. doi:https://arxiv.org/abs/1807.00053.
Bashivan P, Tensen M, DiCarlo JJ. Teacher Guided Architecture Search. arXiv. 2018. doi:https://arxiv.org/abs/1808.01405.
2014
Cadieu CF, Hong H, Yamins DLK, et al. Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition. Bethge M. PLoS Computational Biology. 2014;10(12):e1003963. doi:10.1371/journal.pcbi.1003963.
Afraz A, Yamins DLK, DiCarlo JJ. Neural Mechanisms Underlying Visual Object Recognition. Cold Spring Harbor Symposia on Quantitative Biology. 2014;79:99 - 107. doi:10.1101/sqb.2014.79.024729.
Yamins DLK, Hong H, Cadieu CF, Solomon EA, Seibert D, DiCarlo JJ. Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Sciences. 2014:8619 - 8624. doi:10.1073/pnas.1403112111.
2013
Yamins DLK, Hong H, Cadieu CF, DiCarlo JJ. Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream. In: Advances in Neural Information Processing Systems. Advances in Neural Information Processing Systems. Lake Tahoe, Nevada, United States.; 2013. doi:https://papers.nips.cc/paper/4991-hierarchical-modular-optimization-of-convolutional-networks-achieves-representations-similar-to-macaque-it-and-human-ventral-stream.
Issa EB, Papanastassiou AM, DiCarlo JJ. Large-Scale, High-Resolution Neurophysiological Maps Underlying fMRI of Macaque Temporal Lobe. Journal of Neuroscience. 2013;33(38):15207 - 15219. doi:10.1523/JNEUROSCI.1248-13.2013.
Cadieu CF, Hong H, Yamins DLK, Pinto N, Majaj NJ, DiCarlo JJ. The Neural Representation Benchmark and its Evaluation on Brain and Machine. arXiv. 2013. doi:https://arxiv.org/abs/1301.3530.
Baldassi C, Alemi-Neissi A, Pagan M, DiCarlo JJ, Zecchina R, Zoccolan D. Shape Similarity, Better than Semantic Membership, Accounts for the Structure of Visual Object Representations in a Population of Monkey Inferotemporal Neurons. PLoS Computational Biology. 2013;9(8):e1003167. doi:10.1371/journal.pcbi.1003167.
2012
Rust NC, DiCarlo JJ. Balanced Increases in Selectivity and Tolerance Produce Constant Sparseness along the Ventral Visual Stream. Journal of Neuroscience. 2012;32(30):10170 - 10182. doi:10.1523/JNEUROSCI.6125-11.2012.
DiCarlo  J, Zoccolan D, Rust  C. How Does the Brain Solve Visual Object Recognition?. Neuron. 2012;73(3):415 - 434. doi:10.1016/j.neuron.2012.01.010.
Li N, DiCarlo JJ. Neuronal Learning of Invariant Object Representation in the Ventral Visual Stream Is Not Dependent on Reward. Journal of Neuroscience. 2012;32(19):6611 - 6620. doi:10.1523/JNEUROSCI.3786-11.2012.
Issa EB, DiCarlo JJ. Precedence of the Eye Region in Neural Processing of Faces. Journal of Neuroscience. 2012;32(47):16666 - 16682. doi:10.1523/JNEUROSCI.2391-12.2012.
Majaj NJ, Hong H, Solomon EA, DiCarlo JJ. A unified neuronal population code fully explains human object recognition. In: Computation and Systems Neuroscience (COSYNE). Computation and Systems Neuroscience (COSYNE). Salt Lake City, Utah, USA; 2012. doi:http://www.cosyne.org/c/index.php?title=Cosyne_12.
2010
DiCarlo JJ. Do we have a strategy for understanding how the visual system accomplishes object recognition?. In: Dickenson SJ, Leonardis A, Schiele B, Tarr MJ Object Categorization: Computer and Human Vision Perspectives. Object Categorization: Computer and Human Vision Perspectives. New York, NY, USA: Cambridge University Press; 2010.
Li N, DiCarlo JJ. Does the visual system use natural experience to construct size invariant object representations?. Computation and Systems Neuroscience (COSYNE). 2010. doi:10.3389/conf.fnins.2010.03.00326.
Pinto N, DiCarlo JJ, Cox DD. A High-Throughput Screening Approach to Biologically-Inspired Object Recognition. Learning Workshop-Computation and Systems Neuroscience (COSYNE). 2010.
Pinto N, Majaj NJ, Barhomi Y, Solomon EA, Cox DD, DiCarlo JJ. Human versus machine: comparing visual object recognition systems on a level playing field. Computation and Systems Neuroscience (COSYNE). 2010. doi:10.3389/conf.fnins.2010.03.00283.
Rust NC, DiCarlo JJ. Selectivity and Tolerance ("Invariance") Both Increase as Visual Information Propagates from Cortical Area V4 to IT. Journal of Neuroscience. 2010;30(39):12978 - 12995. doi:10.1523/JNEUROSCI.0179-10.2010.
Issa EB, Papanastassiou AM, Andken BB, DiCarlo JJ. Towards large-scale, high resolution maps of object selectivity in inferior temporal cortex. Computation and Systems Neuroscience (COSYNE). 2010. doi:10.3389/conf.fnins.2010.03.00154.
Li N, DiCarlo JJ. Unsupervised Natural Visual Experience Rapidly Reshapes Size-Invariant Object Representation in Inferior Temporal Cortex. Neuron. 2010;67(6):1062 - 1075. doi:10.1016/j.neuron.2010.08.029.
Aparicio PL, Issa EB, DiCarlo JJ. What is the middle face patch?. Society for Neuroscience. 2010;40:581.8. Available at: https://www.abstractsonline.com/Plan/ViewAbstract.aspx?sKey=e08f5ff4-1ba9-4faf-a459-5c9d4be0a1bf&cKey=36fa0d7d-3e83-4910-be75-57d361ae9e58&mKey=%7bE5D5C83F-CE2D-4D71-9DD6-FC7231E090FB%7d.
2009
Rust NC, DiCarlo JJ. Balanced increases in selectivity and invariance produce constant sparseness across the ventral visual pathway. Journal of Vision. 2009;9(8):738 - 738. doi:10.1167/9.8.738.
Pinto N, Doukhan D, DiCarlo JJ, Cox DD. A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation. Friston KJ. PLoS Computational Biology. 2009;5(11):e1000579. doi:10.1371/journal.pcbi.1000579. (538.96 KB) (141.46 KB)
Pinto N, DiCarlo JJ, Cox DD. How far can you get with a modern face recognition test set using only simple features?. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). Miami, FL: IEEE; 2009. doi:10.1109/CVPR.2009.5206605. (375.73 KB)
Zoccolan D, Oertelt N, DiCarlo JJ, Cox DD. A rodent model for the study of invariant visual object recognition. Proceedings of the National Academy of Sciences. 2009;106(21):8748 - 8753. doi:10.1073/pnas.0811583106. (730.6 KB)
Li N, DiCarlo JJ. The size invariance of neuronal object representations can be reshaped by temporally contiguous visual experience. Society for Neuroscience. 2009:306.10. Available at: https://www.abstractsonline.com/Plan/ViewAbstract.aspx?sKey=8bb461de-0fd1-4f6a-9dfe-d62b65382083&cKey=507938c9-dc2c-4a47-a74f-601df562eddc&mKey=%7b081F7976-E4CD-4F3D-A0AF-E8387992A658%7d.
Papanastassiou AM, de Beeck HPOp, Andken BB, DiCarlo JJ. A systematic exploration of the relationship of fMRI signals and neuronal activity in the primate temporal lobe. Society for Neuroscience. 2009.
Pinto N, Cox DD, DiCarlo JJ. Unlocking Biologically-Inspired Computer Vision: a High-Throughput Approach. NVIDIA GPU Technology Conference. 2009.
Pinto N, Cox DD, DiCarlo JJ. Unlocking Brain-Inspired Computer Vision. GPU@BU. 2009.
Pinto N, Cox DD, DiCarlo JJ. The Visual Cortex and GPUs. GPU Computing for Biomedical Research. 2009.
Li N, Cox DD, Zoccolan D, DiCarlo JJ. What Response Properties Do Individual Neurons Need to Underlie Position and Clutter “Invariant” Object Recognition?. Journal of Neurophysiology. 2009;102(1):360 - 376. doi:10.1152/jn.90745.2008. (773.41 KB)
2008
Rust NC, DiCarlo JJ. Concurrent increases in selectivity and tolerance produce constant sparseness across the ventral visual stream. Computation and Systems Neuroscience (COSYNE). 2008. Available at: http://www.cosyne.org/c/images/8/8e/Cosyne_pf_new.pdf.
Cox DD, DiCarlo JJ. Does Learned Shape Selectivity in Inferior Temporal Cortex Automatically Generalize Across Retinal Position?. Journal of Neuroscience. 2008;28(40):10045 - 10055. doi:10.1523/JNEUROSCI.2142-08.2008. (8.59 MB)
Pinto N, DiCarlo JJ, Cox DD. Establishing Good Benchmarks and Baselines for Face Recognition. In: European Conference on Computer Vision-Faces in 'Real-Life' Images Workshop. European Conference on Computer Vision-Faces in 'Real-Life' Images Workshop. Marseille, France: EECV; 2008. (1.74 MB)

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