As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts.
Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government.
In this paper, we develop the Data Science Machine, which is able to derive predictive models from raw data automatically.
If we pick $n$ random points uniformly in $[0, 1]^d$ and connect each point to its $k-$nearest neighbors, then it is well known that there exists a giant connected component with high probability.
We show how any dataset of any modality (time-series, images, sound...) can be approximated by a well-behaved (continuous, differentiable...) scalar function with a single real-valued parameter.
The recommender system learns online as results are generated.
To achieve this, we model various exploratory inspection and diagnostic tasks for deep learning training processes as specifications for streams using a map-reduce paradigm with which many data scientists are already familiar.
We introduce a new dataset of 293, 008 high definition (1360 x 1360 pixels) fashion images paired with item descriptions provided by professional stylists.
This paper proposes crystal graph neural networks (CGNNs) that use no bond distances, and introduces a scale-invariant graph coordinator that makes up crystal graphs for the CGNN models to be trained on the dataset based on a theoretical materials database.
SOTA for Formation Energy on OQMD v1.2