The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …

We consider the problem of recovering a complete (ie, square and invertible) matrix A 0,
from Y∈ R n× p with Y= A 0 X 0, provided X 0 is sufficiently sparse. This recovery problem is …

Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …

Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …

In this paper, we aim to make the best joint decision of device selection and computing and
spectrum resource allocation for optimizing federated learning (FL) performance in …

We study how neural networks trained by gradient descent extrapolate, ie, what they learn
outside the support of the training distribution. Previous works report mixed empirical results …

Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers
Page 1 Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two …

Physics-informed neural networks (PINNs) encode physical conservation laws and prior
physical knowledge into the neural networks, ensuring the correct physics is represented …

Neural networks have many successful applications, while much less theoretical
understanding has been gained. Towards bridging this gap, we study the problem of …

Machine learning is widely used in practice to produce predictive models for applications
such as image processing, speech and text recognition. These models are more accurate …