Intrinsic Dimension of Data Representations in Deep Networks


Deep neural networks transform their inputs across multiple layers.
In this Project we studied the intrinsic dimensionality (ID) of data-representations,
i.e. the minimal number of parameters needed to describe a representation.

We estimate ID in multiple CNNs with the TWO-NN algorithm
and find that

Look inside the Repository for an outline of our work, extra materials (long video, poster) and the code.
Full details are in our NeurIPS 2019 paper

Accuracy of Rats in Discriminating Visual Objects Is Explained by the Complexity of Their Perceptual Strategy


Credits to Marco Gigante for his beautiful drawing.

In this Project we studied the perceptual strategies of rats involved in visual discrimination tasks.

With the aid of machine learning techniques based on logistic regression and classification images we found that:

For this work, we had the honour to receive a referral by Philippe G.Schyns in Current Biology.

Full details are in the paper.