  We present a novel neural network (NN) method for the detection and removal
of Radio Frequency Interference (RFI) from the raw digitized signal in the
signal processing chain of a typical radio astronomy experiment. The main
advantage of our method is that it does not require a training set. Instead,
our method relies on the fact that the true signal of interest coming from
astronomical sources is thermal and therefore described as a Gaussian random
process, which cannot be compressed. We employ a variational encoder/decoder
network to find the compressible information in the datastream that can explain
the most variance with the fewest degrees of freedom. We demonstrate it on a
set of toy problems and stored ringbuffers from the Baryon Mapping eXperiment
(BMX) prototype. We find that the RFI subtraction is effective at cleaning
simulated timestreams: while we find that the power spectra of the RFI-cleaned
timestreams output by the NN suffer from extra signal consistent with additive
noise, we find that it is generally around percent level across the band and
sub 10 percent in contaminated spectral channels even when RFI power is an
order of magnitude larger than the signal. We discuss advantages and
limitations of this method and possible implementation in the front-end of
future radio experiments.
