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 …
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 …
Neural ordinary differential equations
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 …
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 …
Learning structured sparsity in deep neural networks
High demand for computation resources severely hinders deployment of large-scale Deep
Neural Networks (DNN) in resource constrained devices. In this work, we propose a …
Neural Networks (DNN) in resource constrained devices. In this work, we propose a …
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 …
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 …
Pde-net: Learning pdes from data
Partial differential equations (PDEs) play a prominent role in many disciplines of science
and engineering. PDEs are commonly derived based on empirical observations. However …
and engineering. PDEs are commonly derived based on empirical observations. However …
PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network
Partial differential equations (PDEs) are commonly derived based on empirical
observations. However, recent advances of technology enable us to collect and store …
observations. However, recent advances of technology enable us to collect and store …