In computer vision, generalization of neural representations is
usually measured on i.i.d. data. This hides the fact that
representations often struggle to generalize to non-i.i.d data and
fail to overcome the biases inherent in visual datasets. I...
Recent deep learning models have achieved impressive predictive
performance by learning complex functions of many variables, often
at the cost of interpretability. This lecture first defines
interpretable machine learning in general and introduces...
In computer vision, generalization of neural representations is
usually measured on i.i.d. data. This hides the fact that
representations often struggle to generalize to non-i.i.d data and
fail to overcome the biases inherent in visual datasets. I...
In computer vision, generalization of neural representations is
usually measured on i.i.d. data. This hides the fact that
representations often struggle to generalize to non-i.i.d data and
fail to overcome the biases inherent in visual datasets. I...
To an element in the completion of the set of Lagrangians for
the spectral distance we associate a support. We show that
such a support is γ-coisotropic (a notion we shall define in the
talk) and we shall give examples and counterexamples of γ...
This lecture will partly survey branching laws for real and
p-adic groups which often is related to period integrals of
automorphic representations, discuss some of the more recent
developments, focusing attention on homological aspects and
the...
