Machine learning, deep learning, and active learning, on the other hand, are approaches used to implement AI. If AI is when a computer can carry out a set of tasks based on instruction, ML is a ...

Active learning typically treats the human as a passive, but always correct, participant. Machine teaching provides a more proactive role for the human, but generally assumes that the human is ...

Our work demonstrates that learning machines can offer dramatic advances in how experiments are generated. How useful can machine learning be in a quantum laboratory? Here we raise the question of the ...

Led by Margaux Masson-Forsythe, a seasoned ML engineer and advocate for surgical data science and climate AI advancements, this hands-on guide to active machine learning demonstrates how to train ...

Led by Margaux Masson-Forsythe, a seasoned ML engineer and advocate for surgical data science and climate AI advancements, this hands-on guide to active machine learning demonstrates how to train ...

In particular, machine-vision methods to extract detailed information from imaging assays and active-learning methods to guide experimentation will be required to overcome the dimensionality ...

Fortunately, as more and more ML engineers have embraced a data-centric approach to AI development, the implementation of active learning strategies have been on the rise. The most sophisticated ...

Machine learning has proven to be very useful for the study of active matter, a collective term referring to things like cells and microorganisms. The field is quite new and growing fast. In an ...

The book covers how to apply them indepedently, in combination, and for different use cases in Computer Vision and Natural Language Processing. It also covers strategies for sampling for real-world ...