Within a single sniff, the mammalian olfactory system can decode the identity and
concentration of odorants wafted on turbulent plumes of air. Yet, it must do so given access …

In machine learning, there is a long history of trying to build neural networks that can learn
from fewer example data by baking in strong geometric priors. However, it is not always …

The vulnerability of neural network classifiers to adversarial attacks is a major obstacle to
their deployment in safety-critical applications. Regularization of network parameters during …

While the last five years have seen considerable progress in understanding the internal
representations of deep learning models, many questions remain. This is especially true …

Exploring different geometries has shown to be useful for exploiting inherent properties of
data at hand, becoming attractive to compute embeddings therein. For example, hyperbolic …