Cover Image

Investigating Mask-aware Prototype Learning for Tabular Anomaly Detection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lu, Ruiying, Liu, Jinhan, Du, Chuan, Guo, Dandan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Masked Diffusion Modeling for Anomaly Detection
by: Zhang, Lixing, et al.
Published: (2026)

Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection
by: Livernoche, Victor, et al.
Published: (2026)

Unsupervised Anomaly Detection for Tabular Data Using Noise Evaluation
by: Dai, Wei, et al.
Published: (2024)

Anomaly Detection of Tabular Data Using LLMs
by: Li, Aodong, et al.
Published: (2024)

ProtoNAM: Prototypical Neural Additive Models for Interpretable Deep Tabular Learning
by: Xiong, Guangzhi, et al.
Published: (2024)

Disentangling Tabular Data Towards Better One-Class Anomaly Detection
by: Ye, Jianan, et al.
Published: (2024)

CausalTAD: Injecting Causal Knowledge into Large Language Models for Tabular Anomaly Detection
by: Wang, Ruiqi, et al.
Published: (2026)

uLEAD-TabPFN: Uncertainty-aware Dependency-based Anomaly Detection with TabPFN
by: Lu, Sha, et al.
Published: (2026)

Interpretable Graph-Level Anomaly Detection via Contrast with Normal Prototypes
by: Zhao, Qiuran, et al.
Published: (2026)

Multi-Agent Debate: A Unified Agentic Framework for Tabular Anomaly Detection
by: Wang, Pinqiao, et al.
Published: (2026)

Scalable Heterogeneous Graph Learning via Heterogeneous-aware Orthogonal Prototype Experts
by: Zhou, Wei, et al.
Published: (2026)

ContraLog: Log File Anomaly Detection with Contrastive Learning and Masked Language Modeling
by: Dietz, Simon, et al.
Published: (2026)

Uncertainty-aware Human Mobility Modeling and Anomaly Detection
by: Wen, Haomin, et al.
Published: (2024)

ReTabAD: A Benchmark for Restoring Semantic Context in Tabular Anomaly Detection
by: Yoon, Sanghyu, et al.
Published: (2025)

Multi-scale Masked Autoencoder for Electrocardiogram Anomaly Detection
by: Zhou, Ya, et al.
Published: (2025)

MaskTab: Scalable Masked Tabular Pretraining with Scaling Laws and Distillation for Industrial Classification
by: Zheng, Bo, et al.
Published: (2026)

Why the Counterintuitive Phenomenon of Likelihood Rarely Appears in Tabular Anomaly Detection with Deep Generative Models?
by: Kim, Donghwan, et al.
Published: (2026)

FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data
by: Anwar, Ahmed, et al.
Published: (2024)

When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection
by: Huang, Wei, et al.
Published: (2026)

To Predict or Not To Predict? Proportionally Masked Autoencoders for Tabular Data Imputation
by: Kim, Jungkyu, et al.
Published: (2024)

SG-XDEAT: Sparsity-Guided Cross-Dimensional and Cross-Encoding Attention with Target-Aware Conditioning in Tabular Learning
by: Cheng, Chih-Chuan, et al.
Published: (2025)

Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior
by: Perini, Lorenzo, et al.
Published: (2024)

Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrast
by: Bao, Guangyin, et al.
Published: (2023)

TabularMath: Evaluating Computational Extrapolation in Tabular Learning via Program-Verified Synthesis
by: Cheng, Zerui, et al.
Published: (2026)

EAGLE: Contrastive Learning for Efficient Graph Anomaly Detection
by: Ren, Jing, et al.
Published: (2025)

Graph Evidential Learning for Anomaly Detection
by: Wei, Chunyu, et al.
Published: (2025)

A Survey on Self-Supervised Learning for Non-Sequential Tabular Data
by: Wang, Wei-Yao, et al.
Published: (2024)

Prototype-based Heterogeneous Federated Learning for Blade Icing Detection in Wind Turbines with Class Imbalanced Data
by: Qi, Lele, et al.
Published: (2025)

Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation
by: Lu, Sha, et al.
Published: (2020)

An Enhanced Federated Prototype Learning Method under Domain Shift
by: Kuang, Liang, et al.
Published: (2024)

A Methodological Report on Anomaly Detection on Dynamic Knowledge Graphs
by: Lu, Xiaohua, et al.
Published: (2024)

BatteryBERT for Realistic Battery Fault Detection Using Point-Masked Signal Modeling
by: Zhou, Songqi, et al.
Published: (2025)

Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPs
by: Wu, Taiqiang, et al.
Published: (2023)

DRTA: Dynamic Reward Scaling for Reinforcement Learning in Time Series Anomaly Detection
by: Golchin, Bahareh, et al.
Published: (2025)

A Survey on Deep Tabular Learning
by: Somvanshi, Shriyank, et al.
Published: (2024)

Tabular and Deep Learning for the Whittle Index
by: Relaño, Francisco Robledo, et al.
Published: (2024)

Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learning
by: Ni, Hang, et al.
Published: (2025)

Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor Signals
by: Chen, Xiangwei, et al.
Published: (2024)

CLIMB: Class-imbalanced Learning Benchmark on Tabular Data
by: Liu, Zhining, et al.
Published: (2025)

Active Learning and Transfer Learning for Anomaly Detection in Time-Series Data
by: Kelleher, John D., et al.
Published: (2025)