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Feed-forward convolutional neural networks (CNNs) are currently state-of-the-art for object classification tasks such as ImageNet.
Finding structure in time
J. L. Elman · 1990
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
R. P. Rao and D. H. Ballard · 1999
Earlier work this paper cites.
Fast readout of object identity from macaque inferior temporal cortex
C. P. Hung, G. Kreiman, T. Poggio, and J. J. DiCarlo · 2005
Earlier work this paper cites.
Why is real-world visual object recognition hard?
N. Pinto, D. D. Cox, and J. J. Dicarlo · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Theta oscillations provide temporal windows for local circuit computation in the entorhinal-hippocampal loop
K. Mizuseki, A. Sirota, E. Pastalkova, and G. Buzsáki · 2009
Earlier work this paper cites.
Algorithms for hyper-parameter optimization
J. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl · 2011
Earlier work this paper cites.
How does the brain solve visual object recognition?
J. J. DiCarlo, D. Zoccolan, and N. C. Rust · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Top-down influences on visual processing
C. D. Gilbert and L. Wu · 2013
Earlier work this paper cites.
Context-dependent computation by recurrent dynamics in prefrontal cortex
V. Mante, D. Sussillo, K. V. Shenoy, and W. T. Newsome · 2013
Earlier work this paper cites.
Deep supervised, but not unsupervised, models may explain it cortical representation
S.-M. Khaligh-Razavi and N. Kriegeskorte · 2014
Earlier work this paper cites.
Performance-optimized hierarchical models predict neural responses in higher visual cortex
D. L. K. Yamins, H. Hong, C. F. Cadieu, E. A. Solomon, D. Seibert, and J. J. DiCarlo · 2014
Earlier work this paper cites.
Hyperopt: a python library for model selection and hyperparameter optimization
J. Bergstra, B. Komer, C. Eliasmith, D. Yamins, and D. D. Cox · 2015
Cited alongside, same era.
Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream
U. Güçlü and M. A. van Gerven · 2015
Cited alongside, same era.
Feature-based attention in convolutional neural networks
G. W. Lindsay · 2015
Cited alongside, same era.
Simple learned weighted sums of inferior temporal neuronal firing rates accurately predict human core object recognition performance
N. J. Majaj, H. Hong, E. A. Solomon, and J. J. DiCarlo · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al · 2016
Cited alongside, same era.
Recurrent segmentation for variable computational budgets
L. McIntosh, N. Maheswaranathan, D. Sussillo, and J. Shlens · 2017
Later among the works it cites.
Recurrent convolutional neural networks: a better model of biological object recognition
C. J. Spoerer, P. McClure, and N. Kriegeskorte · 2017
Later among the works it cites.
Feedback networks
A. R. Zamir, T.-L. Wu, L. Sun, W. B. Shen, B. E. Shi, J. Malik, and S. Savarese · 2017
Later among the works it cites.
Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2017
Later among the works it cites.
Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals
E. B. Issa, C. F. Cadieu, and J. J. DiCarlo · 2018
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Explicit information for category-orthogonal object properties increases along the ventral stream
H. Hong, D. L. Yamins, N. J. Majaj, and J. J. DiCarlo · 2016
Cited alongside, same era.
Bridging the gaps between residual learning, recurrent neural networks and visual cortex
Q. Liao and T. Poggio · 2016
Cited alongside, same era.
Systematic evaluation of cnn advances on the imagenet
D. Mishkin, N. Sergievskiy, and J. Matas · 2016
Cited alongside, same era.
Using goal-driven deep learning models to understand sensory cortex
D. L. Yamins and J. J. DiCarlo · 2016
Cited alongside, same era.
Deep convolutional models improve predictions of macaque v1 responses to natural images
S. A. Cadena, G. H. Denfield, E. Y. Walker, L. A. Gatys, A. S. Tolias, M. Bethge, and A. S. Ecker · 2017
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Capacity and trainability in recurrent neural networks
J. Collins, J. Sohl-Dickstein, and D. Sussillo · 2017
Cited alongside, same era.
Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior
K. Kar, J. Kubilius, K. M. Schmidt, E. B. Issa, and J. J. DiCarlo · 2018
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Iamnn: iterative and adaptive mobile neural network for efficient image classification
S. Leroux, P. Molchanov, P. Simoens, B. Dhoedt, T. Breuel, and J. Kautz · 2018
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Learning with rethinking: recurrently improving convolutional neural networks through feedback
X. Li, Z. Jie, J. Feng, C. Liu, and S. Yan · 2018
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Learning long-range spatial dependencies with horizontal gated-recurrent units
D. Linsley, J. Kim, V. Veerabadran, and T. Serre · 2018
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One-shot segmentation in clutter
C. Michaelis, M. Bethge, and A. S. Ecker · 2018
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Beyond core object recognition: Recurrent processes account for object recognition under occlusion
K. Rajaei, Y. Mohsenzadeh, R. Ebrahimpour, and S.-M. Khaligh-Razavi · 2018
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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, E. B. Issa, P. Bashivan, K. Kar, K. Schmidt, and J. J. DiCarlo · 2018
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Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision
J. Shi, H. Wen, Y. Zhang, K. Han, and Z. Liu · 2018
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