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Found 19 papers, 7 papers with code
Anomaly component analysis
At the crossway of machine learning and data analysis, anomaly detection aims at identifying observations that exhibit abnormal behaviour.
Fast kernel half-space depth for data with non-convex supports
Data depth is a statistical function that generalizes order and quantiles to the multivariate setting and beyond, with applications spanning over descriptive and visual statistics, anomaly detection, testing, etc.
Towards On-device Learning on the Edge: Ways to Select Neurons to Update under a Budget Constraint
In the realm of efficient on-device learning under extreme memory and computation constraints, a significant gap in successful approaches persists.
Tailoring Mixup to Data using Kernel Warping functions
Data augmentation is an essential building block for learning efficient deep learning models.
Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization
This paper tackles two major problem settings for interpretability of audio processing networks, post-hoc and by-design interpretation.
Optimized preprocessing and Tiny ML for Attention State Classification
In this paper, we present a new approach to mental state classification from EEG signals by combining signal processing techniques and machine learning (ML) algorithms.
Anomaly detection using data depth: multivariate case
Anomaly detection is a branch of machine learning and data analysis which aims at identifying observations that exhibit abnormal behaviour.
Statistical process monitoring of artificial neural networks
The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs.
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF
This paper tackles post-hoc interpretability for audio processing networks.
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications
The concept of median/consensus has been widely investigated in order to provide a statistical summary of ranking data, i. e. realizations of a random permutation $\Sigma$ of a finite set, $\{1,\; \ldots,\; n\}$ with $n\geq 1$ say.
Functional Anomaly Detection: a Benchmark Study
After an overview of the state-of-the-art and a visual-descriptive study, a variety of anomaly detection methods are compared.
Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis
Because it determines a center-outward ordering of observations in $\mathbb{R}^d$ with $d\geq 2$, the concept of statistical depth permits to define quantiles and ranks for multivariate data and use them for various statistical tasks (e. g. inference, hypothesis testing).
A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions
a probability distribution or a data set.
A Framework to Learn with Interpretation
The interpreter provides both local and global interpretability about the predictive model in terms of human-understandable high level attribute functions, with minimal loss of accuracy.
When OT meets MoM: Robust estimation of Wasserstein Distance
Issued from Optimal Transport, the Wasserstein distance has gained importance in Machine Learning due to its appealing geometrical properties and the increasing availability of efficient approximations.
Choosing among notions of multivariate depth statistics
In the last few years, efficient exact algorithms as well as approximate ones have been constructed and made available in R-packages.
Methodology Primary 62H05, 62H30, secondary 62-07
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure
a statistical population may play a crucial role in this regard, anomalies corresponding to observations with 'small' depth.
Functional Isolation Forest
For the purpose of monitoring the behavior of complex infrastructures (e. g. aircrafts, transport or energy networks), high-rate sensors are deployed to capture multivariate data, generally unlabeled, in quasi continuous-time to detect quickly the occurrence of anomalies that may jeopardize the smooth operation of the system of interest.
Depth and depth-based classification with R-package ddalpha
Following the seminal idea of Tukey, data depth is a function that measures how close an arbitrary point of the space is located to an implicitly defined center of a data cloud.
