For the past year, the HEP.TrkX project has been investigating machine
learning solutions to LHC particle track reconstruction problems. A variety of
models were studied that drew inspiration from computer vision applications and
operated on an image-like representation of tracking detector data. While these
approaches have shown some promise, image-based methods face challenges in
scaling up to realistic HL-LHC data due to high dimensionality and sparsity. In
contrast, models that can operate on the spacepoint representation of track
measurements ("hits") can exploit the structure of the data to solve tasks
efficiently. In this paper we will show two sets of new deep learning models
for reconstructing tracks using space-point data arranged as sequences or
connected graphs. In the first set of models, Recurrent Neural Networks (RNNs)
are used to extrapolate, build, and evaluate track candidates akin to Kalman
Filter algorithms. Such models can express their own uncertainty when trained
with an appropriate likelihood loss function. The second set of models use
Graph Neural Networks (GNNs) for the tasks of hit classification and segment
classification. These models read a graph of connected hits and compute
features on the nodes and edges. They adaptively learn which hit connections
are important and which are spurious. The models are scaleable with simple
architecture and relatively few parameters. Results for all models will be
presented on ACTS generic detector simulated data.Comment: CTD 2018 proceeding