Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

20 Mar 20161 code implementation

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.

AUTOMATED FEATURE ENGINEERING HYPERPARAMETER OPTIMIZATION NEURAL ARCHITECTURE SEARCH

Automating biomedical data science through tree-based pipeline optimization

28 Jan 20161 code implementation

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.

HYPERPARAMETER OPTIMIZATION

Deep Feature Synthesis: Towards Automating Data Science Endeavors

DSAA 2015 2015 1 code implementation

In this paper, we develop the Data Science Machine, which is able to derive predictive models from raw data automatically.

AUTOMATED FEATURE ENGINEERING

Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science

13 Nov 20173 code implementations

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.

Real numbers, data science and chaos: How to fit any dataset with a single parameter

28 Apr 20191 code implementation

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.

TIME SERIES

Evaluating recommender systems for AI-driven data science

22 May 20191 code implementation

The recommender system learns online as results are generated.

COLLABORATIVE FILTERING

Fashion-Gen: The Generative Fashion Dataset and Challenge

21 Jun 20181 code implementation

We introduce a new dataset of 293, 008 high definition (1360 x 1360 pixels) fashion images paired with item descriptions provided by professional stylists.

IMAGE GENERATION

Crystal Graph Neural Networks for Data Mining in Materials Science

Technical report, RIMCS LLC 2019 1 code implementation

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.

BAND GAP FORMATION ENERGY MATERIALS SCREENING TOTAL MAGNETIZATION

Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding

13 Feb 20183 code implementations

As spacecraft send back increasing amounts of telemetry data, improved anomaly detection systems are needed to lessen the monitoring burden placed on operations engineers and reduce operational risk.

ANOMALY DETECTION