Neural machine translation: A review

F Stahlberg - Journal of Artificial Intelligence Research, 2020 - jair.org
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 …

Complete dictionary recovery over the sphere I: Overview and the geometric picture

J Sun, Q Qu, J Wright - IEEE Transactions on Information …, 2016 - ieeexplore.ieee.org
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 …

A survey on vision transformer

K Han, Y Wang, H Chen, X Chen, J Guo… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
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 …

A survey on visual transformer

K Han, Y Wang, H Chen, X Chen, J Guo, Z Liu… - arXiv preprint arXiv …, 2020 - arxiv.org
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 …

Optimizing federated learning in distributed industrial IoT: A multi-agent approach

W Zhang, D Yang, W Wu, H Peng… - IEEE Journal on …, 2021 - ieeexplore.ieee.org
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 …

How neural networks extrapolate: From feedforward to graph neural networks

K Xu, M Zhang, J Li, SS Du, K Kawarabayashi… - arXiv preprint arXiv …, 2020 - arxiv.org
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

Z Allen-Zhu, Y Li, Y Liang - Advances in neural information …, 2019 - proceedings.neurips.cc
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers
Page 1 Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two …

PPINN: Parareal physics-informed neural network for time-dependent PDEs

X Meng, Z Li, D Zhang, GE Karniadakis - Computer Methods in Applied …, 2020 - Elsevier
Physics-informed neural networks (PINNs) encode physical conservation laws and prior
physical knowledge into the neural networks, ensuring the correct physics is represented …

Learning overparameterized neural networks via stochastic gradient descent on structured data

Y Li, Y Liang - Advances in neural information processing …, 2018 - proceedings.neurips.cc
Neural networks have many successful applications, while much less theoretical
understanding has been gained. Towards bridging this gap, we study the problem of …

Secureml: A system for scalable privacy-preserving machine learning

P Mohassel, Y Zhang - 2017 IEEE symposium on security and …, 2017 - ieeexplore.ieee.org
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 …