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Machine learning is advancing towards a data-science approach, implying a necessity to a line of investigation to divulge the knowledge learnt by deep neuronal networks.
Visual object recognition
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Comparative mapping of higher visual areas in monkeys and humans
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Invariant visual representation by single neurons in the human brain
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Initial sequence of the chimpanzee genome and comparison with the human genome
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Local luminance and contrast in natural images
Frazor, R. A. and Geisler, W. S · 2006
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Network science
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Image noise models
Boncelet, C · 2009
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Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E. and Sporns, O · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2010
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
Stallkamp, J., Schlipsing, M., Salmen, J., and Igel, C · 2012
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Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
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Analyzing the performance of multilayer neural networks for object recognition
Agrawal, P., Girshick, R., and Malik, J · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
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Understanding deep image representations by inverting them
Mahendran, A. and Vedaldi, A · 2015
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How is contrast encoded in deep neural networks?
Akbarinia, A. and Gegenfurtner, K. R · 2018
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Processing of chromatic information in a deep convolutional neural network
Flachot, A. and Gegenfurtner, K. R · 2018
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Generalisation in humans and deep neural networks
Geirhos, R., Temme, C. R. M., Rauber, J., Schütt, H. H., Bethge, M., and Wichmann, F. A · 2018
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A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy
Kell, A. J., Yamins, D. L., Shook, E. N., Norman-Haignere, S. V., and McDermott, J. H · 2018
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Multi-task generalization and adaptation between noisy digit datasets: An empirical study
Schneider, S., Ecker, A. S., Macke, J. H., and Bethge, M · 2018
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Comparison of object recognition behavior in human and monkey
Rajalingham, R., Schmidt, K., and DiCarlo, J. J · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Toward an integration of deep learning and neuroscience
Marblestone, A. H., Wayne, G., and Kording, K. P · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.-M., Fawzi, A., and Frossard, P · 2016
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Network dissection: Quantifying interpretability of deep visual representations
Bau, D., Zhou, B., Khosla, A., Oliva, A., and Torralba, A · 2017
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On the robustness of convolutional neural networks to internal architecture and weight perturbations
Cheney, N., Schrimpf, M., and Kreiman, G · 2017
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al · 2018
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Deeptest: Automated testing of deep-neural-network-driven autonomous cars
Tian, Y., Pei, K., Jana, S., and Ray, B · 2018
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Automated deep-neural-network surveillance of cranial images for acute neurologic events
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Interpretable convolutional neural networks
Zhang, Q., Wu, Y. N., and Zhu, S.-C · 2018
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Visual interpretability for deep learning: a survey
Zhang, Q.-s. and Zhu, S.-C · 2018
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Zhao, C. W., Daley, M. J., and Pruszynski, J. A · 2018
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Manifestation of image contrast in deep networks
Akbarinia, A. and Gegenfurtner, K. R · 2019
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A tradeoff in the neural code across regions and species
Pryluk, R., Kfir, Y., Gelbard-Sagiv, H., Fried, I., and Paz, R · 2019
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Task representations in neural networks trained to perform many cognitive tasks
Yang, G. R., Joglekar, M. R., Song, H. F., Newsome, W. T., and Wang, X.-J · 2019
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