Exploring materials band structure space with unsupervised machine learning
Sign Up to like & getrecommendations! Published in 2019 at "Computational Materials Science"
DOI: 10.1016/j.commatsci.2018.11.002
Abstract: Abstract An unsupervised machine learning algorithm is applied for the first time to explore the space of materials electronic band structures. T-student stochastic neighbor embedding (t-SNE), a state of the art algorithm for visualization of… read more here.
Keywords: space; structure space; unsupervised machine; machine learning ... See more keywords
Channel head extraction based on fuzzy unsupervised machine learning method
Sign Up to like & getrecommendations! Published in 2021 at "Geomorphology"
DOI: 10.1016/j.geomorph.2021.107888
Abstract: Abstract Channel head extraction is fundamental to understand the catchment hydrological processes, catchment origin, runoff generation and landscape evolution. A spatially constant threshold value such as upslope area, slope-area, and curvature have been widely used… read more here.
Keywords: fuzzy unsupervised; method; channel head; head extraction ... See more keywords
UNSUPERVISED MACHINE LEARNING CLUSTERING FOR STRATIFICATION OF CARDIAC RISK IN PATIENTS WITH EXERCISE ECHOCARDIOGRAPHY NEGATIVE FOR ISCHEMIA
Sign Up to like & getrecommendations! Published in 2019 at "Journal of the American College of Cardiology"
DOI: 10.1016/s0735-1097(19)30718-1
Abstract: Exercise echocardiography (ESE) negative for ischemia is associated with an overall low risk of adverse events, but significant prognostic heterogeneity may exist within the referred population of patients. Unsupervised clustering can uncover latent clinical phenotypes… read more here.
Keywords: exercise echocardiography; negative ischemia; risk; unsupervised machine ... See more keywords
A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei.
Sign Up to like & getrecommendations! Published in 2021 at "Nature protocols"
DOI: 10.1038/s41596-020-00432-x
Abstract: Cell morphology encodes essential information on many underlying biological processes. It is commonly used by clinicians and researchers in the study, diagnosis, prognosis, and treatment of human diseases. Quantification of cell morphology has seen tremendous… read more here.
Keywords: machine learning; robust unsupervised; unsupervised machine; morphological heterogeneity ... See more keywords
Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance
Sign Up to like & getrecommendations! Published in 2019 at "Scientific Reports"
DOI: 10.1038/s41598-019-53925-5
Abstract: Identification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to classify active electrodes showing… read more here.
Keywords: classification; machine learning; task; active electrodes ... See more keywords
Unraveling the origin of reductive stability of super-concentrated electrolytes from first principles and unsupervised machine learning
Sign Up to like & getrecommendations! Published in 2022 at "Chemical Science"
DOI: 10.1039/d2sc04025e
Abstract: Developing electrolytes with excellent electrochemical stability is critical for next-generation rechargeable batteries. Super-concentrated electrolytes (SCEs) have attracted great interest due to their high electrochemical performances and stability. Previous studies have revealed changes in solvation structures… read more here.
Keywords: super concentrated; unsupervised machine; concentrated electrolytes; machine learning ... See more keywords
Identifying Outliers in Astronomical Images with Unsupervised Machine Learning
Sign Up to like & getrecommendations! Published in 2022 at "Research in Astronomy and Astrophysics"
DOI: 10.1088/1674-4527/ac7386
Abstract: Astronomical outliers, such as unusual, rare or unknown types of astronomical objects or phenomena, constantly lead to the discovery of genuinely unforeseen knowledge in astronomy. More unpredictable outliers will be uncovered in principle with the… read more here.
Keywords: unsupervised machine; machine learning; attcae knn; cae knn ... See more keywords
Identifying strong lenses with unsupervised machine learning using convolutional autoencoder
Sign Up to like & getrecommendations! Published in 2020 at "Monthly Notices of the Royal Astronomical Society"
DOI: 10.1093/mnras/staa1015
Abstract: In this paper, we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder, and a clustering algorithm consisting of a Bayesian Gaussian mixture model. We apply this technique to… read more here.
Keywords: technique; unsupervised machine; machine learning; convolutional autoencoder ... See more keywords
Unsupervised machine learning discovers classes in aluminium alloys
Sign Up to like & getrecommendations! Published in 2023 at "Royal Society Open Science"
DOI: 10.1098/rsos.220360
Abstract: Aluminium (Al) alloys are critical to many applications. Although Al alloys have been commercially widespread for over a century, their development has predominantly taken a trial-and-error approach. Furthermore, many discrete studies regarding Al alloys, often… read more here.
Keywords: aluminium alloys; learning discovers; unsupervised machine; machine learning ... See more keywords
Searching magnetic states using an unsupervised machine learning algorithm with the Heisenberg model
Sign Up to like & getrecommendations! Published in 2019 at "Physical Review B"
DOI: 10.1103/physrevb.99.024423
Abstract: Magnetism is a canonical example of a spontaneous symmetry breaking. The symmetry of a magnetic state below the Curie temperature is spontaneously broken even though the Hamiltonian is invariant under symmetry. Recently, machine learning algorithms… read more here.
Keywords: machine; heisenberg model; learning algorithm; unsupervised machine ... See more keywords
Unsupervised Machine Learning of Quantum Phase Transitions Using Diffusion Maps.
Sign Up to like & getrecommendations! Published in 2020 at "Physical review letters"
DOI: 10.1103/physrevlett.125.225701
Abstract: Experimental quantum simulators have become large and complex enough that discovering new physics from the huge amount of measurement data can be quite challenging, especially when little theoretical understanding of the simulated model is available.… read more here.
Keywords: phase; machine learning; phase transitions; unsupervised machine ... See more keywords