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PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data.
Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine.
#2 best model for Time Series Classification on Physionet 2017 Atrial Fibrillation
We will first outline the motivation for this release, the plans for the future, and then give a brief overview over the new functionality in this version.
We propose the augmentation of fully convolutional networks with long short term memory recurrent neural network (LSTM RNN) sub-modules for time series classification.
#2 best model for Outlier Detection on ECG5000
Our architecture is composed of two deep networks, each of which trained by competing with each other while collaborating to understand the underlying concept in the target class, and then classify the testing samples.
It can accelerate outlier model building and scoring when a large number of base models are used.
In this study, we propose a three-module acceleration framework called SUOD to expedite the training and prediction with a large number of unsupervised detection models.
Self-supervision provides effective representations for downstream tasks without requiring labels.
#2 best model for Anomaly Detection on CIFAR-10 model detecting CIFAR-10
Robust machine learning relies on access to data that can be used with standardized frameworks in important tasks and the ability to develop models whose performance can be reasonably reproduced.