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James J DiCarlo
James J DiCarlo
mit.edu의 이메일 확인됨
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
DLK Yamins, H Hong, CF Cadieu, EA Solomon, D Seibert, JJ DiCarlo
Proceedings of the national academy of sciences 111 (23), 8619-8624, 2014
20342014
How does the brain solve visual object recognition?
JJ DiCarlo, D Zoccolan, NC Rust
Neuron 73 (3), 415-434, 2012
18992012
Using goal-driven deep learning models to understand sensory cortex
DLK Yamins, JJ DiCarlo
Nature neuroscience 19 (3), 356-365, 2016
15872016
Untangling invariant object recognition
JJ DiCarlo, DD Cox
Trends in cognitive sciences 11 (8), 333-341, 2007
10602007
Fast readout of object identity from macaque inferior temporal cortex
CP Hung, G Kreiman, T Poggio, JJ DiCarlo
Science 310 (5749), 863-866, 2005
9942005
Deep neural networks rival the representation of primate IT cortex for core visual object recognition
CF Cadieu, H Hong, DLK Yamins, N Pinto, D Ardila, EA Solomon, ...
PLoS computational biology 10 (12), e1003963, 2014
7992014
Why is real-world visual object recognition hard?
N Pinto, DD Cox, JJ DiCarlo
PLoS computational biology 4 (1), e27, 2008
7522008
Why is real-world visual object recognition hard?
N Pinto, DD Cox, JJ DiCarlo
PLoS computational biology 4 (1), e27, 2008
7482008
Brain-score: Which artificial neural network for object recognition is most brain-like?
M Schrimpf, J Kubilius, H Hong, NJ Majaj, R Rajalingham, EB Issa, K Kar, ...
BioRxiv, 407007, 2018
4632018
Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior
K Kar, J Kubilius, K Schmidt, EB Issa, JJ DiCarlo
Nature neuroscience 22 (6), 974-983, 2019
4262019
Neural population control via deep image synthesis
P Bashivan, K Kar, JJ DiCarlo
Science 364 (6439), eaav9436, 2019
3912019
Selectivity and tolerance (“invariance”) both increase as visual information propagates from cortical area V4 to IT
NC Rust, JJ DiCarlo
Journal of Neuroscience 30 (39), 12978-12995, 2010
3882010
A high-throughput screening approach to discovering good forms of biologically inspired visual representation
N Pinto, D Doukhan, JJ DiCarlo, DD Cox
PLoS computational biology 5 (11), e1000579, 2009
3792009
A high-throughput screening approach to discovering good forms of biologically inspired visual representation
N Pinto, D Doukhan, JJ DiCarlo, DD Cox
PLoS computational biology 5 (11), e1000579, 2009
3792009
Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks
R Rajalingham, EB Issa, P Bashivan, K Kar, K Schmidt, JJ DiCarlo
Journal of Neuroscience 38 (33), 7255-7269, 2018
3732018
Object selectivity of local field potentials and spikes in the macaque inferior temporal cortex
G Kreiman, CP Hung, A Kraskov, RQ Quiroga, T Poggio, JJ DiCarlo
Neuron 49 (3), 433-445, 2006
3642006
Explicit information for category-orthogonal object properties increases along the ventral stream
H Hong, DLK Yamins, NJ Majaj, JJ DiCarlo
Nature neuroscience 19 (4), 613-622, 2016
3492016
Stimulus configuration, classical conditioning, and hippocampal function.
NA Schmajuk, JJ DiCarlo
Psychological review 99 (2), 268, 1992
3281992
Unsupervised neural network models of the ventral visual stream
C Zhuang, S Yan, A Nayebi, M Schrimpf, MC Frank, JJ DiCarlo, ...
Proceedings of the National Academy of Sciences 118 (3), e2014196118, 2021
3162021
Unsupervised natural experience rapidly alters invariant object representation in visual cortex
N Li, JJ DiCarlo
science 321 (5895), 1502-1507, 2008
3082008
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