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In naturalistic learning problems, a model's input contains a wide range of features, some useful for the task at hand, and others not.
Perceptrons, 1969
Marvin, M., and Seymour, A. P · 1969
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Forest before trees: The precedence of global features in visual perception
Navon, D · 1977
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Catastrophic interference in connectionist networks: The sequential learning problem
McCloskey, M., and Cohen, N. J · 1989
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Mitigating unwanted biases with adversarial learning
Zhang, B. H., Lemoine, B., and Mitchell, M · 2006
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Representational similarity analysis-connecting the branches of systems neuroscience
Kriegeskorte, N., Mur, M., and Bandettini, P. A · 2008
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What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M., and LeCun, Y · 2009
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Saxe, A. M., McClelland, J. L., and Ganguli, S · 2013
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Learning fair representations
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Deep supervised, but not unsupervised, models may explain it cortical representation
Khaligh-Razavi, S.-M., and Kriegeskorte, N · 2014
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Adam: A method for stochastic optimization
Kingma, D. P., and Ba, J · 2014
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
Yamins, D. L., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., and DiCarlo, J. J · 2014
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Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence
Cichy, R. M., Khosla, A., Pantazis, D., Torralba, A., and Oliva, A · 2016
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Explicit information for category-orthogonal object properties increases along the ventral stream
Hong, H., Yamins, D. L., Majaj, N. J., and DiCarlo, J. J · 2016
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What makes imagenet good for transfer learning?
Huh, M., Agrawal, P., and Efros, A. A · 2016
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Fine-grained analysis of sentence embeddings using auxiliary prediction tasks
Adi, Y., Kermany, E., Belinkov, Y., Lavi, O., and Goldberg, Y · 2017
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Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks
Cichy, R. M., Khosla, A., Pantazis, D., and Oliva, A · 2017
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al · 2017
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Opening the black box of deep neural networks via information
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Continual learning through synaptic intelligence
Zenke, F., Poole, B., and Ganguli, S · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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An analytic theory of generalization dynamics and transfer learning in deep linear networks
Lampinen, A. K., and Ganguli, S · 2019
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What’s hidden in a randomly weighted neural network?
Ramanujan, V., Wortsman, M., Kembhavi, A., Farhadi, A., and Rastegari, M · 2019
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Generative continual concept learning
Rostami, M., Kolouri, S., McClelland, J., and Pilly, P · 2019
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On the information bottleneck theory of deep learning
Saxe, A. M., Bansal, Y., Dapello, J., Advani, M., Kolchinsky, A., Tracey, B. D., and Cox, D. D · 2019
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A mathematical theory of semantic development in deep neural networks
Saxe, A. M., McClelland, J. L., and Ganguli, S · 2019
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Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 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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Deep image prior
Ulyanov, D., Vedaldi, A., and Lempitsky, V · 2018
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Reconciling modern machine-learning practice and the classical bias–variance trade-off
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Weight agnostic neural networks
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Rethinking imagenet pre-training
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Deep learning generalizes because the parameter-function map is biased towards simple functions
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Training batchnorm and only batchnorm: On the expressive power of random features in cnns
Frankle, J., Schwab, D. J., and Morcos, A. S · 2020
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How much position information do convolutional neural networks encode?
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Individual differences among deep neural network models
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Learning explanations that are hard to vary
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The pitfalls of simplicity bias in neural networks
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Diverse deep neural networks all predict human it well, after training and fitting
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