We interact with digital images every single day, snapping photos, applying filters, and rendering 3D visualizations. But while the human eye sees colors, shapes, and depth, a computer sees something ...
Three-dimensional (3D) cultured neural networks that emulate the structures and computational principles of the brain could be of use in the development of brain-inspired computing and artificial ...
CIANNA is a general-purpose deep learning framework primarily developed and used for astronomical data analysis. Functionalities and optimizations are added based on relevance for astrophysical ...
CryoDRGN is a neural network based algorithm for heterogeneous cryo-EM reconstruction. In particular, the method models a continuous distribution over 3D structures by using a neural network based ...
A distinguishing feature of the neural network models used in Physics and Chemistry is that they must obey basic underlying symmetries, such as symmetry to translations, rotations, and the exchange of ...
This important work provides evidence that artificial recurrent neural networks can be used to investigate neural mechanisms underlying reversible remapping of spatial representations. Authors perform ...
Machine learning with neural networks is sometimes said to be part art and part science. Dr. James McCaffrey of Microsoft Research teaches both with a full-code, step-by-step tutorial. A binary ...
Location of earthquakes is a primary task in seismology and microseismic monitoring, essential for almost any further analysis. Earthquake hypocenters can be determined by the inversion of arrival ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. The modeling of flow and transport in porous media is of the utmost importance in many ...
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