Results 151 to 160 of about 14,691 (177)

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

Advanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi, Yu Kumagai, Arata Takamatsu, Fumiyasu Oba   +3 more
wiley   +1 more source

Learning and inference with correlated neural variability. [PDF]

PNAS Nexus
Qi Y, Zhu Z, Wei Y, Cao L, Wang Z, Zhang J, Lu W, Feng J.   +7 more
europepmc   +1 more source

Memory efficient training for 3D brain image registration networks using PatchMorph. [PDF]

Sci Rep
Skibbe H, Byra M, Watakabe A, Yamamori T, Reisert M.   +4 more
europepmc   +1 more source

Handling Multi-Source Uncertainty in Accelerated Degradation Through a Wiener-Based Robust Modeling Scheme. [PDF]

Sensors (Basel)
Tu H, Wang X, Li Y.
europepmc   +1 more source

Diffusion models for brain imaging computing: a survey of frameworks and applications. [PDF]

Brain Inform
Osman YBM, Elsanosi AHM, Jing C, Shen Y, Kuai H, Wang S, Ng MK, Lei B, Wang S.   +8 more
europepmc   +1 more source

Epidemic Forecasting via Hybrid Deep Learning With Unified Visibility and Temporal Graphs Under Stochastic Noise Modelling. [PDF]

Healthc Technol Lett
Ghafi AK, Pirkhedri A, Akhbarifar S, Shafiabadi MH.   +3 more
europepmc   +1 more source

Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes

Journal of Computational Physics, 2022
Matthew Simpson, Ruth Baker, David Warne   +2 more
exaly  

Inference of dominant modes for linear stochastic processes

Royal Society Open Science, 2021
Robert Mackay
exaly