Task-Driven Convolutional Recurrent Models of the Visual System
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Surya Ganguli | Aran Nayebi | Jonas Kubilius | James J. DiCarlo | David Sussillo | Daniel Bear | Kohitij Kar | Daniel L. Yamins | J. DiCarlo | David Sussillo | Daniel Yamins | S. Ganguli | Kohitij Kar | Aran Nayebi | J. Kubilius | Daniel Bear
[1] Yoshua Bengio,et al. Algorithms for Hyper-Parameter Optimization , 2011, NIPS.
[2] Jeffrey L. Elman,et al. Finding Structure in Time , 1990, Cogn. Sci..
[3] Nikolaus Kriegeskorte,et al. Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation , 2014, PLoS Comput. Biol..
[4] Nicolas Pinto,et al. Why is Real-World Visual Object Recognition Hard? , 2008, PLoS Comput. Biol..
[5] James J. DiCarlo,et al. How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.
[6] Vijay Vasudevan,et al. Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[7] Yalda Mohsenzadeh,et al. Beyond Core Object Recognition: Recurrent processes account for object recognition under occlusion , 2019, PLoS Comput. Biol..
[8] G. Buzsáki,et al. Theta Oscillations Provide Temporal Windows for Local Circuit Computation in the Entorhinal-Hippocampal Loop , 2009, Neuron.
[9] Leon A. Gatys,et al. Deep convolutional models improve predictions of macaque V1 responses to natural images , 2019, PLoS Comput. Biol..
[10] Pavlo Molchanov,et al. IamNN: Iterative and Adaptive Mobile Neural Network for Efficient Image Classification , 2018, ICLR.
[11] Jascha Sohl-Dickstein,et al. Capacity and Trainability in Recurrent Neural Networks , 2016, ICLR.
[12] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[13] Chris Eliasmith,et al. Hyperopt: a Python library for model selection and hyperparameter optimization , 2015 .
[14] Grace W. Lindsay. Feature-based Attention in Convolutional Neural Networks , 2015, ArXiv.
[15] W. James,et al. The Principles of Psychology. , 1983 .
[16] J. DiCarlo,et al. Using goal-driven deep learning models to understand sensory cortex , 2016, Nature Neuroscience.
[17] Rajesh P. N. Rao,et al. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. , 1999 .
[18] Jiri Matas,et al. Systematic evaluation of convolution neural network advances on the Imagenet , 2017, Comput. Vis. Image Underst..
[19] Ha Hong,et al. Explicit information for category-orthogonal object properties increases along the ventral stream , 2016, Nature Neuroscience.
[20] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[21] Gabriel Kreiman,et al. Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning , 2016, ICLR.
[22] Alexander S. Ecker,et al. One-Shot Segmentation in Clutter , 2018, ICML.
[23] Lin Sun,et al. Feedback Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Nando de Freitas,et al. Cortical microcircuits as gated-recurrent neural networks , 2017, NIPS.
[25] C. Gilbert,et al. Top-down influences on visual processing , 2013, Nature Reviews Neuroscience.
[26] Thomas Serre,et al. Learning long-range spatial dependencies with horizontal gated-recurrent units , 2018, NeurIPS.
[27] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Marcel A. J. van Gerven,et al. Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream , 2014, The Journal of Neuroscience.
[29] Tomaso A. Poggio,et al. Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex , 2016, ArXiv.
[30] Ha Hong,et al. Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance , 2015, The Journal of Neuroscience.
[31] Ha Hong,et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.
[32] Elias B. Issa,et al. Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals , 2018, eLife.
[33] W. Newsome,et al. Context-dependent computation by recurrent dynamics in prefrontal cortex , 2013, Nature.
[34] Yizhen Zhang,et al. Deep Recurrent Neural Network Reveals a Hierarchy of Process Memory during Dynamic Natural Vision , 2017, bioRxiv.
[35] Jonathon Shlens,et al. Recurrent Segmentation for Variable Computational Budgets , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[36] Nikolaus Kriegeskorte,et al. Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition , 2017, bioRxiv.
[37] James J. DiCarlo,et al. Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior , 2018, Nature Neuroscience.
[38] Yuan Yu,et al. TensorFlow: A system for large-scale machine learning , 2016, OSDI.
[39] Antonio Torralba,et al. Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence , 2016, Scientific Reports.
[40] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[41] Tomaso Poggio,et al. Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.
[42] Xin Li,et al. Learning with Rethinking: Recurrently Improving Convolutional Neural Networks through Feedback , 2017, Pattern Recognit..