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Found 12 papers, 6 papers with code
Is AUC the best measure for practical comparison of anomaly detectors?
The area under receiver operating characteristics (AUC) is the standard measure for comparison of anomaly detectors.
Fitting large mixture models using stochastic component selection
The performance of our method is illustrated in a variety of synthetic and real-data contexts, considering deep models, such as mixtures of normalizing flows and sum-product (transform) networks.
Comparison of Anomaly Detectors: Context Matters
The objective of this comparison is twofold: to compare anomaly detection methods of various paradigms with focus on deep generative models, and identification of sources of variability that can yield different results.
DeepTopPush: Simple and Scalable Method for Accuracy at the Top
Accuracy at the top is a special class of binary classification problems where the performance is evaluated only on a small number of relevant (top) samples.
Expectation-Maximization Algorithm for Identification of Mesh-based Compartment Thermal Model of Power Modules
Recently, we have proposed a linear time invariant state-space thermal model based on a compartment representation and its identification procedure that is based on the Expectation-Maximization algorithm from incomplete temperature data.
Neural Power Units
Conventional Neural Networks can approximate simple arithmetic operations, but fail to generalize beyond the range of numbers that were seen during training.
Nonlinear classifiers for ranking problems based on kernelized SVM
Many classification problems focus on maximizing the performance only on the samples with the highest relevance instead of all samples.
General Framework for Binary Classification on Top Samples
Many binary classification problems minimize misclassification above (or below) a threshold.
Rodent: Relevance determination in differential equations
We aim to identify the generating, ordinary differential equation (ODE) from a set of trajectories of a partially observed system.
Anomaly scores for generative models
Reconstruction error is a prevalent score used to identify anomalous samples when data are modeled by generative models, such as (variational) auto-encoders or generative adversarial networks.
Are generative deep models for novelty detection truly better?
Many deep models have been recently proposed for anomaly detection.
Non-parametric Bayesian Models of Response Function in Dynamic Image Sequences
Comparison of the resulting algorithms with these priors is performed on synthetic dataset as well as on real datasets from dynamic renal scintigraphy.
