  We investigate the prospect of reconstructing the ''cosmic distance ladder''
of the Universe using a novel deep learning framework called LADDER - Learning
Algorithm for Deep Distance Estimation and Reconstruction. LADDER is trained on
the apparent magnitude data from the Pantheon Type Ia supernovae compilation,
incorporating the full covariance information among data points, to produce
predictions along with corresponding errors. After employing several validation
tests with a number of deep learning models, we pick LADDER as the best
performing one. We then demonstrate applications of our method in the
cosmological context, including serving as a model-independent tool for
consistency checks for other datasets like baryon acoustic oscillations,
calibration of high-redshift datasets such as gamma ray bursts, and use as a
model-independent mock catalog generator for future probes. Our analysis
advocates for careful consideration of machine learning techniques applied to
cosmological contexts.
