  We introduce a machine-learning-based approach to enhance the sensitivity of
optical-extreme ultraviolet (XUV) transient absorption spectroscopy. A
reference spectrum is used as input to a three-layer feed-forward neural
network, allowing for an efficient elimination of source noise from measurement
data. In pump-probe experiments using high-harmonic radiation, we show a more
than tenfold improvement in noise suppression in XUV transient absorption
spectra compared to conventional referencing. Utilizing strong spectral
correlations in the source fluctuations, the network facilitates a pixel-wise
noise reduction without the need for wavelength calibration of the reference
spectrum. The presented method can be adapted to a wide range of beam lines and
enables the investigation of subtle electron and lattice dynamics in the weak
excitation regime, relevant for the study of photovoltaics and photoinduced
phase transitions of strongly correlated materials.
