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Found 19 papers, 10 papers with code
Deep One-Class Classification
Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection.
Ranked #32 on
Anomaly Detection
on One-class CIFAR-10
Deep Semi-Supervised Anomaly Detection
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.
VERSE: Versatile Graph Embeddings from Similarity Measures
Embedding a web-scale information network into a low-dimensional vector space facilitates tasks such as link prediction, classification, and visualization.
NetLSD: Hearing the Shape of a Graph
However, it is a hard task in terms of the expressiveness of the employed similarity measure and the efficiency of its computation.
Social and Information Networks
The Shape of Data: Intrinsic Distance for Data Distributions
The ability to represent and compare machine learning models is crucial in order to quantify subtle model changes, evaluate generative models, and gather insights on neural network architectures.
Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data
Unsupervised feature selection aims to reduce the number of features, often using feature importance scores to quantify the relevancy of single features to the task at hand.
Differentiable Segmentation of Sequences
We build on recent advances in learning continuous warping functions and propose a novel family of warping functions based on the two-sided power (TSP) distribution.
Does Terrorism Trigger Online Hate Speech? On the Association of Events and Time Series
We propose a novel statistical methodology to measure, test and visualize the systematic association between rare events and peaks in a time series.
Two-Sample Testing for Event Impacts in Time Series
Unfortunately, it is often non-trivial to select both a time series that is informative about events and a powerful detection algorithm: detection may fail because the detection algorithm is not suitable, or because there is no shared information between the time series and the events of interest.
Statistical Evaluation of Anomaly Detectors for Sequences
Although precision and recall are standard performance measures for anomaly detection, their statistical properties in sequential detection settings are poorly understood.
SGR: Self-Supervised Spectral Graph Representation Learning
Representing a graph as a vector is a challenging task; ideally, the representation should be easily computable and conducive to efficient comparisons among graphs, tailored to the particular data and analytical task at hand.
FREDE: Anytime Graph Embeddings
Low-dimensional representations, or embeddings, of a graph's nodes facilitate several practical data science and data engineering tasks.
Graph Clustering with Graph Neural Networks
Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction.
RODD: Robust Outlier Detection in Data Cubes
We propose a general type of test data and examine all methods in a simulation study.
Interpretable Anomaly Detection via Discrete Optimization
Anomaly detection is essential in many application domains, such as cyber security, law enforcement, medicine, and fraud protection.
Prototypes as Explanation for Time Series Anomaly Detection
Detecting abnormal patterns that deviate from a certain regular repeating pattern in time series is essential in many big data applications.
Redundancy-aware unsupervised rankings for collections of gene sets
The rankings can be used to reduce the dimension of collections of gene sets, such that they show lower redundancy and still a high coverage of the genes.
On the Effectiveness of Heterogeneous Ensemble Methods for Re-identification
In this contribution, we introduce a novel ensemble method for the re-identification of industrial entities, using images of chipwood pallets and galvanized metal plates as dataset examples.
About Test-time training for outlier detection
In this paper, we introduce DOUST, our method applying test-time training for outlier detection, significantly improving the detection performance.
