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Tag: detection

  • Locally Private Parametric Methods for Change-Point Detection

    Locally Private Parametric Methods for Change-Point Detection arXiv:2602.13619v1 Announce Type: new Abstract: We study parametric change-point detection, where the goal is to identify distributional changes in time series, under local differential privacy. In the non-private setting, we derive improved finite-sample accuracy guarantees for a change-point detection algorithm based on the generalized log-likelihood ratio test, via…

  • Feature Detection, Part 3: Harris Corner Detection

    Feature Detection, Part 3: Harris Corner Detection Finding the most informative points in images The post Feature Detection, Part 3: Harris Corner Detection appeared first on Towards Data Science. Vyacheslav Efimov Go to original source

  • Novelty detection on path space

    Novelty detection on path space arXiv:2512.03243v1 Announce Type: new Abstract: We frame novelty detection on path space as a hypothesis testing problem with signature-based test statistics. Using transportation-cost inequalities of Gasteratos and Jacquier (2023), we obtain tail bounds for false positive rates that extend beyond Gaussian measures to laws of RDE solutions with smooth bounded…

  • Feature Detection, Part 2: Laplace & Gaussian Operators

    Feature Detection, Part 2: Laplace & Gaussian Operators Laplace meets Gaussian — the story of two operators in edge detection The post Feature Detection, Part 2: Laplace & Gaussian Operators appeared first on Towards Data Science. Vyacheslav Efimov Go to original source

  • Perturbations in the Orthogonal Complement Subspace for Efficient Out-of-Distribution Detection

    Perturbations in the Orthogonal Complement Subspace for Efficient Out-of-Distribution Detection arXiv:2511.00849v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is essential for deploying deep learning models in open-world environments. Existing approaches, such as energy-based scoring and gradient-projection methods, typically rely on high-dimensional representations to separate in-distribution (ID) and OOD samples. We introduce P-OCS (Perturbations in the…

  • RF-DETR Under the Hood: The Insights of a Real-Time Transformer Detection

    RF-DETR Under the Hood: The Insights of a Real-Time Transformer Detection From rigid grids to adaptive attention, this is the evolutionary path that made detection transformers fast, flexible, and formidable. The post RF-DETR Under the Hood: The Insights of a Real-Time Transformer Detection appeared first on Towards Data Science. David Redó Nieto Go to original…

  • Feature Detection, Part 1: Image Derivatives, Gradients, and Sobel Operator

    Feature Detection, Part 1: Image Derivatives, Gradients, and Sobel Operator Applying calculus fundamentals to computer vision for edge detection The post Feature Detection, Part 1: Image Derivatives, Gradients, and Sobel Operator appeared first on Towards Data Science. Vyacheslav Efimov Go to original source

  • On the Adversarial Robustness of Learning-based Conformal Novelty Detection

    On the Adversarial Robustness of Learning-based Conformal Novelty Detection arXiv:2510.00463v1 Announce Type: new Abstract: This paper studies the adversarial robustness of conformal novelty detection. In particular, we focus on AdaDetect, a powerful learning-based framework for novelty detection with finite-sample false discovery rate (FDR) control. While AdaDetect provides rigorous statistical guarantees under benign conditions, its behavior…

  • No Peeking Ahead: Time-Aware Graph Fraud Detection

    No Peeking Ahead: Time-Aware Graph Fraud Detection How to implement leak-free graph fraud detection The post No Peeking Ahead: Time-Aware Graph Fraud Detection appeared first on Towards Data Science. Erika G. Gonçalves Go to original source

  • Noise Robust One-Class Intrusion Detection on Dynamic Graphs

    Noise Robust One-Class Intrusion Detection on Dynamic Graphs arXiv:2508.14192v1 Announce Type: cross Abstract: In the domain of network intrusion detection, robustness against contaminated and noisy data inputs remains a critical challenge. This study introduces a probabilistic version of the Temporal Graph Network Support Vector Data Description (TGN-SVDD) model, designed to enhance detection accuracy in the…

  • Anomoly detection with only categorical variables

    Anomoly detection with only categorical variables Hello everyone, I have an anomoly detection project but all of my data is categorical. I suppose I could try and ask them to change it prediction but does anyone have any advice. The goal is to there are groups within the data and and do an analysis to…

  • Don’t Waste Your Labeled Anomalies: 3 Practical Strategies to Boost Anomaly Detection Performance

    Don’t Waste Your Labeled Anomalies: 3 Practical Strategies to Boost Anomaly Detection Performance A few labels go a long way in anomaly detection The post Don’t Waste Your Labeled Anomalies: 3 Practical Strategies to Boost Anomaly Detection Performance appeared first on Towards Data Science. Shuai Guo Go to original source

  • Explainable Anomaly Detection with RuleFit: An Intuitive Guide

    Explainable Anomaly Detection with RuleFit: An Intuitive Guide Creating interpretable rules to characterize the identified anomalies The post Explainable Anomaly Detection with RuleFit: An Intuitive Guide appeared first on Towards Data Science. Shuai Guo Go to original source

  • SubSearch: Robust Estimation and Outlier Detection for Stochastic Block Models via Subgraph Search

    SubSearch: Robust Estimation and Outlier Detection for Stochastic Block Models via Subgraph Search arXiv:2506.03657v1 Announce Type: new Abstract: Community detection is a fundamental task in graph analysis, with methods often relying on fitting models like the Stochastic Block Model (SBM) to observed networks. While many algorithms can accurately estimate SBM parameters when the input graph…

  • Conformal Object Detection by Sequential Risk Control

    Conformal Object Detection by Sequential Risk Control arXiv:2505.24038v1 Announce Type: new Abstract: Recent advances in object detectors have led to their adoption for industrial uses. However, their deployment in critical applications is hindered by the inherent lack of reliability of neural networks and the complex structure of object detection models. To address these challenges, we…

  • Statistical Inference for Clustering-based Anomaly Detection

    Statistical Inference for Clustering-based Anomaly Detection arXiv:2504.18633v1 Announce Type: new Abstract: Unsupervised anomaly detection (AD) is a fundamental problem in machine learning and statistics. A popular approach to unsupervised AD is clustering-based detection. However, this method lacks the ability to guarantee the reliability of the detected anomalies. In this paper, we propose SI-CLAD (Statistical Inference…

  • (Im)possibility of Automated Hallucination Detection in Large Language Models

    (Im)possibility of Automated Hallucination Detection in Large Language Models arXiv:2504.17004v1 Announce Type: cross Abstract: Is automated hallucination detection possible? In this work, we introduce a theoretical framework to analyze the feasibility of automatically detecting hallucinations produced by large language models (LLMs). Inspired by the classical Gold-Angluin framework for language identification and its recent adaptation to…

  • Custom Training Pipeline for Object Detection Models

    Custom Training Pipeline for Object Detection Models What if you want to write the whole object detection training pipeline from scratch, so you can understand each step and be able to customize it? That’s what I set out to do. I examined several well-known object detection pipelines and designed one that best suits my needs…

  • A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection

    A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection arXiv:2502.08695v1 Announce Type: new Abstract: Bayesian nonparametric methods are naturally suited to the problem of out-of-distribution (OOD) detection. However, these techniques have largely been eschewed in favor of simpler methods based on distances between pre-trained or learned embeddings of data points. Here we…

  • Sequential Change Point Detection via Denoising Score Matching

    Sequential Change Point Detection via Denoising Score Matching arXiv:2501.12667v1 Announce Type: new Abstract: Sequential change-point detection plays a critical role in numerous real-world applications, where timely identification of distributional shifts can greatly mitigate adverse outcomes. Classical methods commonly rely on parametric density assumptions of pre- and post-change distributions, limiting their effectiveness for high-dimensional, complex data…

  • Mastering Sensor Fusion: Color Image Obstacle Detection with KITTI Data — Part 2

    Mastering Sensor Fusion: Color Image Obstacle Detection with KITTI Data — Part 2 Mastering Sensor Fusion: Color Image Obstacle Detection with KITTI Data — Part 2 How to use color image data for object detection in the context of obstacle detection The concept of sensor fusion is a decision-making mechanism that can be applied to different problems and using different…

  • Sensor Fusion — KITTI — ‘Lidar-based Obstacle Detection’ — Part-1

    Sensor Fusion — KITTI — ‘Lidar-based Obstacle Detection’ — Part-1 Mastering Sensor Fusion: LiDAR Obstacle Detection with KITTI Data — Part 1 How to use Lidar data for obstacle detection with unsupervised learning Sensor fusion, multi-modal perception, autonomous vehicles — if these keywords pique your interest, this Medium blog is for you. Join me as I explore the fascinating world of LiDAR and color image-based environment…

  • datadriftR: An R Package for Concept Drift Detection in Predictive Models

    datadriftR: An R Package for Concept Drift Detection in Predictive Models arXiv:2412.11308v1 Announce Type: new Abstract: Predictive models often face performance degradation due to evolving data distributions, a phenomenon known as data drift. Among its forms, concept drift, where the relationship between explanatory variables and the response variable changes, is particularly challenging to detect and…

  • Community Detection with Heterogeneous Block Covariance Model

    Community Detection with Heterogeneous Block Covariance Model arXiv:2412.03780v1 Announce Type: new Abstract: Community detection is the task of clustering objects based on their pairwise relationships. Most of the model-based community detection methods, such as the stochastic block model and its variants, are designed for networks with binary (yes/no) edges. In many practical scenarios, edges often…