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Properties of invariant object recognition in human one-shot learning suggests a hierarchical architecture different from deep convolutional neural networks

Han, Y., Roig, G., Geiger, G. & Poggio, T. Properties of invariant object recognition in human one-shot learning suggests a hierarchical architecture different from deep convolutional neural networks. Vision Science Society (2019).

Visual Features for Invariant Coding by Face Selective Neurons

Zarco, W. & Freiwald, W. A. Visual Features for Invariant Coding by Face Selective Neurons . 2019 Conference on Cognitive Computational Neuroscience (CCN) (2019).

Neural mechanisms supporting facial expressions

Vazquez, Y., Ianni, G. & Freiwald, W. A. Neural mechanisms supporting facial expressions . unknown (2019).

Toward human-like object naming in artificial neural systems

Eisape, T., Levy, R., Tenenbaum, J. B. & Zaslavsky, N. Toward human-like object naming in artificial neural systems . International Conference on Learning Representations (ICLR 2020), Bridging AI and Cognitive Science workshop (2020).

Effects of Face Familiarity in Humans and Deep Neural Networks

Dobs, K. et al. Effects of Face Familiarity in Humans and Deep Neural Networks . European Conference on Visual Perception (2019).

Evidence that recurrent pathways between the prefrontal and inferior temporal cortex is critical during core object recognition

Kar, K. & DiCarlo, J. J. Evidence that recurrent pathways between the prefrontal and inferior temporal cortex is critical during core object recognition . COSYNE (2020).

Evidence that recurrent pathways between the prefrontal and inferior temporal cortex is critical during core object recognition

Kar, K. & DiCarlo, J. J. Evidence that recurrent pathways between the prefrontal and inferior temporal cortex is critical during core object recognition . Society for Neuroscience (2019).

A meta-analysis of ANNs as models of primate V1

Marques, T. & DiCarlo, J. J. A meta-analysis of ANNs as models of primate V1 . Bernstein (2019).

Temporal information for action recognition only needs to be integrated at a choice level in neural networks and primates

Schrimpf, M., Sato, F., Sanghavi, S. & DiCarlo, J. J. Temporal information for action recognition only needs to be integrated at a choice level in neural networks and primates . COSYNE (2020).

Hierarchical neural network models that more closely match primary visual cortex tend to better explain higher level visual cortical responses

Marques, T., Schrimpf, M. & DiCarlo, J. J. Hierarchical neural network models that more closely match primary visual cortex tend to better explain higher level visual cortical responses . COSYNE (2020).

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