It's a commonplace of artificial intelligence to say that machine learning, which depends on vast amounts of data, functions by finding patterns in data. The phrase, "finding patterns in data," in ...

Stanford University researchers developed a machine learning-based method capable of diagnosing multiple diseases using B cell and T cell receptor sequences. The model, called Machine learning for ...

Machine learning workloads require large datasets, while machine learning workflows require high data throughput. We can optimize the data pipeline to achieve both.

Machine learning methods are becoming increasingly important in the analysis of large-scale genomic, epigenomic, proteomic and metabolic data sets. In this Review, the authors consider the ...

Machine learning is particularly good at digesting large amounts of data very quickly and identifying patterns or finding anomalies or outliers in that data.

Lost amongst the hype and hyperbole surrounding machine learning today, especially deep learning, is the critical distinction between correlation and causation.

Conclusion This machine learning approach enabled the identification of a typology of four representative treatment sequences observed in long-term survival. It was noted that most long-term survivors ...

Q: What technologies underlie Uber’s machine learning? Lange: We offer about 10 different algorithms, including boosted trees, linear learners and neural networks.

Machine learning, in which an algorithm detects patterns in large amounts of data with minimal direction from programmers, has been put to use in a variety of pattern-finding applications, from ...