A fascinating hypothesis is that human and animal intelligence could be explained by a few
principles (rather than an encyclopaedic list of heuristics). If that hypothesis was correct, we …

On the Implicit Bias in Deep-Learning Algorithms Page 1 DEEP LEARNING HAS been highly
successful in recent years and has led to dramatic improvements in multiple domains …

Audio-visual learning helps to comprehensively understand the world, by integrating
different senses. Accordingly, multiple input modalities are expected to boost model …

Learning robust models that generalize well under changes in the data distribution is critical
for real-world applications. To this end, there has been a growing surge of interest to learn …

Scalability and privacy are two critical concerns for cross-device federated learning (FL)
systems. In this work, we identify that synchronous FL–cannot scale efficiently beyond a few …

Continual Learning has inspired a plethora of approaches and evaluation settings; however,
the majority of them overlooks the properties of a practical scenario, where the data stream …

Abstract Recently, Sharpness-Aware Minimization (SAM), which connects the geometry of
the loss landscape and generalization, has demonstrated a significant performance boost …

It is not clear yet why ADAM-alike adaptive gradient algorithms suffer from worse
generalization performance than SGD despite their faster training speed. This work aims to …

The staple of human intelligence is the capability of acquiring knowledge in a continuous
fashion. In stark contrast, Deep Networks forget catastrophically and, for this reason, the sub …

A quantum generalization of Natural Gradient Descent is presented as part of a general-
purpose optimization framework for variational quantum circuits. The optimization dynamics …