A review of deep learning approaches for inverse scattering problems (invited review)
In recent years, deep learning (DL) is becoming an increasingly important tool for solving
inverse scattering problems (ISPs). This paper reviews methods, promises, and pitfalls of …
inverse scattering problems (ISPs). This paper reviews methods, promises, and pitfalls of …
[HTML][HTML] A review on deep learning MRI reconstruction without fully sampled k-space
Background Magnetic resonance imaging (MRI) is an effective auxiliary diagnostic method
in clinical medicine, but it has always suffered from the problem of long acquisition time …
in clinical medicine, but it has always suffered from the problem of long acquisition time …
On neural differential equations
P Kidger - arXiv preprint arXiv:2202.02435, 2022 - arxiv.org
The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …
Learning deep transformer models for machine translation
Transformer is the state-of-the-art model in recent machine translation evaluations. Two
strands of research are promising to improve models of this kind: the first uses wide …
strands of research are promising to improve models of this kind: the first uses wide …
Investigating bi-level optimization for learning and vision from a unified perspective: A survey and beyond
Bi-Level Optimization (BLO) is originated from the area of economic game theory and then
introduced into the optimization community. BLO is able to handle problems with a …
introduced into the optimization community. BLO is able to handle problems with a …
Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network
A Sherstinsky - Physica D: Nonlinear Phenomena, 2020 - Elsevier
Because of their effectiveness in broad practical applications, LSTM networks have received
a wealth of coverage in scientific journals, technical blogs, and implementation guides …
a wealth of coverage in scientific journals, technical blogs, and implementation guides …
Augmented neural odes
Abstract We show that Neural Ordinary Differential Equations (ODEs) learn representations
that preserve the topology of the input space and prove that this implies the existence of …
that preserve the topology of the input space and prove that this implies the existence of …
Graph neural ordinary differential equations
We introduce the framework of continuous--depth graph neural networks (GNNs). Graph
neural ordinary differential equations (GDEs) are formalized as the counterpart to GNNs …
neural ordinary differential equations (GDEs) are formalized as the counterpart to GNNs …
Neural ordinary differential equations
RTQ Chen, Y Rubanova… - Advances in neural …, 2018 - proceedings.neurips.cc
We introduce a new family of deep neural network models. Instead of specifying a discrete
sequence of hidden layers, we parameterize the derivative of the hidden state using a …
sequence of hidden layers, we parameterize the derivative of the hidden state using a …
On interpretability of artificial neural networks: A survey
Deep learning as performed by artificial deep neural networks (DNNs) has achieved great
successes recently in many important areas that deal with text, images, videos, graphs, and …
successes recently in many important areas that deal with text, images, videos, graphs, and …