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Understanding CNNs as a model of the inferior temporal cortex: using mediation analysis to unpack the contribution of perceptual and semantic features in random and trained networks
The ability of the CNNs to predict IT activity patterns and the role played by perceptual and semantic features was evaluated using regression models, multidimensional scaling, and mediation analysis.
The Next Big Thing(s) in Unsupervised Machine Learning: Five Lessons from Infant Learning
After a surge in popularity of supervised Deep Learning, the desire to reduce the dependence on curated, labelled data sets and to leverage the vast quantities of unlabelled data available recently triggered renewed interest in unsupervised learning algorithms.
Layerwise Learning Rates for Object Features in Unsupervised and Supervised Neural Networks And Consequent Predictions for the Infant Visual System
CORnet and DeepCluster differ in both supervision and in the convolutional networks at their heart, thus to isolate the effect of supervision, we ran a control experiment in which we trained the convolutional network from DeepCluster (an AlexNet variant) in a supervised manner.
