
Visualizing the geometry of labeled highdimensional data with spheres
Data visualizations summarize highdimensional distributions in two or t...
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Comparing representational geometries using the unbiased distance correlation
Representational similarity analysis (RSA) tests models of brain computa...
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Going in circles is the way forward: the role of recurrence in visual inference
Biological visual systems exhibit abundant recurrent connectivity. State...
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Controversial stimuli: pitting neural networks against each other as models of human recognition
Distinct scientific theories can make similar predictions. To adjudicate...
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Visualizing Representational Dynamics with Multidimensional Scaling Alignment
Representational similarity analysis (RSA) has been shown to be an effec...
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Recurrence required to capture the dynamic computations of the human ventral visual stream
The visual system is an intricate network of brain regions that enables ...
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Deep Learning for Cognitive Neuroscience
Neural network models can now recognise images, understand text, transla...
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Neural network models and deep learning  a primer for biologists
Originally inspired by neurobiology, deep neural network models have bec...
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Adaptive Independence Tests with GeoTopological Transformation
Testing two potentially multivariate variables for statistical dependenc...
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Building machines that adapt and compute like brains
Building machines that learn and think like humans is essential not only...
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Robustly representing inferential uncertainty in deep neural networks through sampling
As deep neural networks (DNNs) are applied to increasingly challenging p...
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Representational Distance Learning for Deep Neural Networks
Deep neural networks (DNNs) provide useful models of visual representati...
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Nikolaus Kriegeskorte
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