In this work, we introduce Boolformer, the first Transformer architectur...
We examine how transformers cope with two challenges: learning basic int...
Symbolic regression, the task of predicting the mathematical expression ...
Learning rate schedules are ubiquitously used to speed up and improve
op...
Symbolic regression, i.e. predicting a function from the observation of ...
Vision Transformers (ViT) have recently emerged as a powerful alternativ...
Convolutional architectures have proven extremely successful for vision
...
One of the central features of deep learning is the generalization abili...
Direct Feedback Alignment (DFA) is emerging as an efficient and biologic...
Scarcity of training data for task-oriented dialogue systems is a well k...
A recent line of research has highlighted the existence of a double desc...
Deep neural networks can achieve remarkable generalization performances ...
Scarcity of training data for task-oriented dialogue systems is a well k...
Despite the phenomenal success of deep neural networks in a broad range ...
We provide a description for the evolution of the generalization perform...
We argue that in fully-connected networks a phase transition delimits th...
Deep learning has been immensely successful at a variety of tasks, rangi...