[D] why did kernel methods become less popular than neural networks?

• falkon

  3 173 8.0 Python

  Large-scale, multi-GPU capable, kernel solver (by FalkonML)

  

On this note, you may be interested by the KeOps library (which is to kernels/geometric ML what cuDNN is to CNNs) and the Falkon solvers: the software bottlenecks that were holding back kernel methods are progressively being lifted. Million-scale datasets are now routinely processed in minutes/hours and billion-scale problems are starting to become tractable. This opens up quite a few possibilities :-)

• keops

  5 1,000 9.5 Python

  KErnel OPerationS, on CPUs and GPUs, with autodiff and without memory overflows

  

You're very welcome! As of today it is still mostly useful when you have less than 50-100 features per point (as detailed here or there), but it's very versatile. We are actively working on making it as useful as possible for the community: if you encounter any issue with it, feel free to let us know!

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

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