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Machine learning for electrocatalyst and photocatalyst design and discovery
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …
reducing the impact of global warming, and providing solutions to environmental pollution …
Feature dimensionality reduction: a review
W Jia, M Sun, J Lian, S Hou - Complex & Intelligent Systems, 2022 - Springer
As basic research, it has also received increasing attention from people that the “curse of
dimensionality” will lead to increase the cost of data storage and computing; it also …
dimensionality” will lead to increase the cost of data storage and computing; it also …
A unifying perspective on neural manifolds and circuits for cognition
Two different perspectives have informed efforts to explain the link between the brain and
behaviour. One approach seeks to identify neural circuit elements that carry out specific …
behaviour. One approach seeks to identify neural circuit elements that carry out specific …
Reconstructing computational system dynamics from neural data with recurrent neural networks
Computational models in neuroscience usually take the form of systems of differential
equations. The behaviour of such systems is the subject of dynamical systems theory …
equations. The behaviour of such systems is the subject of dynamical systems theory …
[HTML][HTML] Review of artificial intelligence and machine learning technologies: Classification, restrictions, opportunities and challenges
Artificial intelligence (AI) is an evolving set of technologies used for solving a wide range of
applied issues. The core of AI is machine learning (ML)—a complex of algorithms and …
applied issues. The core of AI is machine learning (ML)—a complex of algorithms and …
Interpretable machine learning: Fundamental principles and 10 grand challenges
Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …
Machine learning in aerodynamic shape optimization
Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …
optimization (ASO), thanks to the availability of aerodynamic data and continued …
Conceptual and empirical comparison of dimensionality reduction algorithms (pca, kpca, lda, mds, svd, lle, isomap, le, ica, t-sne)
Abstract Feature Extraction Algorithms (FEAs) aim to address the curse of dimensionality
that makes machine learning algorithms incompetent. Our study conceptually and …
that makes machine learning algorithms incompetent. Our study conceptually and …
[HTML][HTML] Applications of machine learning to water resources management: A review of present status and future opportunities
Water is the most valuable natural resource on earth that plays a critical role in the socio-
economic development of humans worldwide. Water is used for various purposes, including …
economic development of humans worldwide. Water is used for various purposes, including …
Attractor and integrator networks in the brain
In this Review, we describe the singular success of attractor neural network models in
describing how the brain maintains persistent activity states for working memory, corrects …
describing how the brain maintains persistent activity states for working memory, corrects …