Their work demonstrates that quantum circuits for data encoding in quantum machine learning can be greatly simplified without compromising accuracy or robustness. The research was published Sept ...

Two new studies from the Department of Computational Biomedicine at Cedars-Sinai are advancing what we know about using ...

By 2030, it’s expected that the market for streaming data will eclipse $73 billion, growing nearly 20% each year until then. More impressively, the machine learning market—which brought in $15 ...

MIT researchers designed a computationally efficient algorithm for machine learning with symmetric data that also requires fewer data for training than conventional approaches. Their work could inform ...

EMBL-EBI scientists and collaborators at Heidelberg University have developed CORNETO, a new computational tool that uses ...

Overview: AI models may unintentionally learn from one another through shared datasets, especially in open-source communities ...

Her project, “From Dirty Data to Fair Prediction: Data Preparation Framework for End-to-End Equitable Machine Learning,” targets the data-preparation pipeline as a strategic opportunity for ...

One of the key obstacles to efficient quantum machine learning has been encoding classical data into quantum states, a computationally challenging task requiring deeply entangled circuits. To ...