Crowdsourcing opens the door to solving a wide variety of problems that previously were unfeasible in the field of machine learning, allowing us to obtain relatively low cost labeled data in a small amount of time.
Results for "machine learning"
Machine Learning for Bioinformatics and Neuroimaging
Machine Learning (ML) is a well‐known paradigm that refers to the ability of systems to learn a specific task from the data and aims to develop computer algorithms that improve with experience.
Inspired by the butterfly, deep learning sheds new light on nanoscale colors
Manipulating light on the nanoscale allows scientists to create specific structural colors that do away with the need for potentially harmful dyes.
Wearable brain-machine interface turns intentions into actions
Accurately measuring brain signals is critical to determining what actions a user wants to perform.
Humans, machines, and reproducibility in materials chemistry
There is more to materials discovery than just discovery! Will robots outperform humans in reproducibility too?
A new deep-learning architecture for drug discovery
Convolutional neural networks provide stronger predictive performances for pharmacological assays compared to traditional machine learning models.
Thoughts on materials discovery at the human-machine interface
Imagine how these two planes – the world of machines and the world of human systems – will work synergistically to realize the potential of new materials and the systems into which they will be integrated.
The alliance of data, human experts, and machines to design better medicinal drugs
Machine learning significantly reduces the time and cost involved in drug screening.
Teaching Machines to Think Like Humans
An artificial neural network based entirely on memristors is developed.
Deep Learning for RNA-Protein Interaction Prediction
Hundreds of RNA‐binding proteins and their associated RNAs have been revealed, which enables the large‐scale prediction of RNA–protein interactions using machine learning methods.
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