Self-driving laboratories for chemistry and materials science
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …
Through the automation of experimental workflows, along with autonomous experimental …
[HTML][HTML] Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
J Waring, C Lindvall, R Umeton - Artificial intelligence in medicine, 2020 - Elsevier
Objective This work aims to provide a review of the existing literature in the field of
automated machine learning (AutoML) to help healthcare professionals better utilize …
automated machine learning (AutoML) to help healthcare professionals better utilize …
SMAC3: A versatile Bayesian optimization package for hyperparameter optimization
Algorithm parameters, in particular hyperparameters of machine learning algorithms, can
substantially impact their performance. To support users in determining well-performing …
substantially impact their performance. To support users in determining well-performing …
Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models
Graph neural networks (GNN) has been considered as an attractive modelling method for
molecular property prediction, and numerous studies have shown that GNN could yield …
molecular property prediction, and numerous studies have shown that GNN could yield …
Speech emotion recognition using deep 1D & 2D CNN LSTM networks
We aimed at learning deep emotion features to recognize speech emotion. Two
convolutional neural network and long short-term memory (CNN LSTM) networks, one 1D …
convolutional neural network and long short-term memory (CNN LSTM) networks, one 1D …
Auto-keras: An efficient neural architecture search system
H **, Q Song, X Hu - Proceedings of the 25th ACM SIGKDD …, 2019 - dl.acm.org
Neural architecture search (NAS) has been proposed to automatically tune deep neural
networks, but existing search algorithms, eg, NASNet, PNAS, usually suffer from expensive …
networks, but existing search algorithms, eg, NASNet, PNAS, usually suffer from expensive …
[HTML][HTML] Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization
Accurate assessment of undrained shear strength (USS) for soft sensitive clays is a great
concern in geotechnical engineering practice. This study applies novel data-driven extreme …
concern in geotechnical engineering practice. This study applies novel data-driven extreme …
Higgs physics at the HL-LHC and HE-LHC
The discovery of the Higgs boson in 2012, by the ATLAS and CMS experiments, was a
success achieved with only a percent of the entire dataset foreseen for the LHC. It opened a …
success achieved with only a percent of the entire dataset foreseen for the LHC. It opened a …
Tunability: Importance of hyperparameters of machine learning algorithms
Modern supervised machine learning algorithms involve hyperparameters that have to be
set before running them. Options for setting hyperparameters are default values from the …
set before running them. Options for setting hyperparameters are default values from the …
Computational discovery of transition-metal complexes: from high-throughput screening to machine learning
Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …
materials. The behavior of the metal–organic bond, while very tunable for achieving target …