Recent advances and applications of machine learning in solid-state materials science
One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …
is machine learning. This collection of statistical methods has already proved to be capable …
Convolutional neural networks for inverse problems in imaging: A review
In this article, we review recent uses of convolutional neural networks (CNNs) to solve
inverse problems in imaging. It has recently become feasible to train deep CNNs on large …
inverse problems in imaging. It has recently become feasible to train deep CNNs on large …
Deep fluids: A generative network for parameterized fluid simulations
This paper presents a novel generative model to synthesize fluid simulations from a set of
reduced parameters. A convolutional neural network is trained on a collection of discrete …
reduced parameters. A convolutional neural network is trained on a collection of discrete …
Imbalanced tabular data modelization using CTGAN and machine learning to improve IoT Botnet attacks detection
Abstract The Internet of Things (IoT) has been trending in the past few years, posing so
many security problems. IoT Botnets are one of the most serious attacks that threaten the …
many security problems. IoT Botnets are one of the most serious attacks that threaten the …
Reconstruction of three-dimensional porous media using generative adversarial neural networks
To evaluate the variability of multiphase flow properties of porous media at the pore scale, it
is necessary to acquire a number of representative samples of the void-solid structure. While …
is necessary to acquire a number of representative samples of the void-solid structure. While …
Generative adversarial nets
I Goodfellow, J Pouget-Abadie… - Advances in neural …, 2014 - proceedings.neurips.cc
We propose a new framework for estimating generative models via adversarial nets, in
which we simultaneously train two models: a generative model G that captures the data …
which we simultaneously train two models: a generative model G that captures the data …
CaloGAN: Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks
The precise modeling of subatomic particle interactions and propagation through matter is
paramount for the advancement of nuclear and particle physics searches and precision …
paramount for the advancement of nuclear and particle physics searches and precision …
Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey
N Asiri, M Hussain, F Al Adel, N Alzaidi - Artificial intelligence in medicine, 2019 - Elsevier
Diabetic retinopathy (DR) results in vision loss if not treated early. A computer-aided
diagnosis (CAD) system based on retinal fundus images is an efficient and effective method …
diagnosis (CAD) system based on retinal fundus images is an efficient and effective method …
Accelerating science with generative adversarial networks: an application to 3D particle showers in multilayer calorimeters
Physicists at the Large Hadron Collider (LHC) rely on detailed simulations of particle
collisions to build expectations of what experimental data may look like under different …
collisions to build expectations of what experimental data may look like under different …
DL2: training and querying neural networks with logic
M Fischer, M Balunovic… - International …, 2019 - proceedings.mlr.press
We present DL2, a system for training and querying neural networks with logical constraints.
Using DL2, one can declaratively specify domain knowledge constraints to be enforced …
Using DL2, one can declaratively specify domain knowledge constraints to be enforced …