Improving State-of-Art on Sparse Random Projections

Random projections are widely used to reduce data dimension in various analyses. Provable guarantees were developed first in the important result of Johnson and Lindenstrauss on Lipschitz maps, but more recently there has been a lot of follow-up work in the context of machine-learning. Particularly attractive are sparse random projections, which share similar guarantees as […]

Performance drawbacks of Tensorflow Datasets

Tensorflow, the popular framework for machine-learning, recommends its new dataset API for preprocessing and serving data. It supports useful tricks, such as caching data in memory, prefetching in parallel threads and others described in tutorials. Still, Tensorflow has issues with slow data slicing, so the dataset API may actually do harm in setups where computations […]