ABSTRACT A common need for artificial intelligence models in the broader geoscience is to
encode various types of spatial data, such as points, polylines, polygons, graphs, or rasters …

To solve the spatial problems of mapping, localization and navigation, the mammalian
lineage has developed striking spatial representations. One important spatial representation …

Generating learning-friendly representations for points in space is a fundamental and long-
standing problem in machine learning. Recently, multi-scale encoding schemes (such as …

Recent progress in deep learning is essentially based on a “big data for small tasks”
paradigm, under which massive amounts of data are used to train a classifier for a single …

Unsupervised text encoding models have recently fueled substantial progress in NLP. The
key idea is to use neural networks to convert words in texts to vector space representations …

Conventional imitation learning assumes access to the actions of demonstrators, but these
motor signals are often non-observable in naturalistic settings. Additionally, sequential …

Path integration is one of the functions that support the self-localization ability of animals.
Path integration outputs position information after an animal's movement when initial …

Spatial scene similarity plays a crucial role in spatial cognition, as it enables us to
understand and compare different spatial scenes and their relationships. However …

The activity of the grid cell population in the medial entorhinal cortex (MEC) of the
mammalian brain forms a vector representation of the self-position of the animal. Recurrent …

The brain is able to calculate the distance and direction to the desired position based on grid
cells. Extensive neurophysiological studies of rodent navigation have postulated the grid …