arXiv Machine Learning

Leakage-Robust Evaluation and Data-Scale Sensitivity of Attention-Enhanced Multi-Task Learning for Joint Fault Diagnosis and Remaining Useful Life Estimation

arXiv:2607. 16493v1 Announce Type: new Abstract: Multi-task deep learning models that jointly perform fault classification and remaining useful life (RUL) regression are increasingly used in predictive maintenance, yet reported performance can be strongly affected by how sliding-window sequences are split into training and test sets.

arXiv Machine Learning

Jul 7

Towards a more realistic evaluation of machine learning models for bearing fault diagnosis

arXiv:2509. 22267v5 Announce Type: replace Abstract: Reliable detection of bearing faults is essential for maintaining the safety and operational efficiency of rotating machinery.

By Jo\~ao Paulo Vieira, Victor Afonso Bauler, Rodrigo Kobashikawa Rosa, Danilo Silva

safety

arXiv AI

Jul 28

Physically Verifiable Evidence and LLM-Based Reporting for Bearing Fault Diagnosis

arXiv:2607. 22797v1 Announce Type: cross Abstract: Trustworthy deployment of AI-based diagnosis in safety-critical mechanical systems hinges on validation: whether a prediction can be checked against physical reality before it is acted upon.

By Yuntong Chen, Jianyu Liu, Guobin Zhao, Ziang Wang, Chao Chen, Ju Huang, Xitian Tian, Lijiang Huang

llms fine-tuning safety

arXiv Machine Learning

Jun 19

PaAno+: Multiscale Encoding and Cross-Variable Attention for Time Series Anomaly Detection

arXiv:2606. 20055v1 Announce Type: new Abstract: Time-series anomaly detection has significant practical value for industrial and medical monitoring, as well as other critical domains.

By Youji Zhu, Hongbing Wang, Wenchao Liu, Xiaodong Liu, Xiangguang Xiong

llms rag benchmarks

arXiv Machine Learning

Jun 11

DeMix: Debugging Training Data with Mixed Data Error Types by Investigating Influence Vectors

arXiv:2606. 11616v1 Announce Type: new Abstract: High-quality training data is essential for the success of machine learning models.

By Jiale Deng, Yanyan Shen, Xiaogang Shi, Chai Junjun

llms benchmarks safety

arXiv AI

Jun 26

Evaluation-Strategy Gap in Fault Diagnosis of Deep Learning Programs

arXiv:2606. 26492v1 Announce Type: cross Abstract: Deep Learning (DL) programs can fail during training for many reasons, and diagnosing the cause is a costly and time-consuming maintenance task.

By Sigma Jahan

arXiv Machine Learning

Jul 14

Vertical Fusion: Condensing Internal Representations for Robust ViT Classification

arXiv:2607. 10391v1 Announce Type: cross Abstract: Despite exposing rich intermediate representations, Vision Transformers (ViTs) are almost exclusively utilized as black-box feature extractors, where only the last layer is considered for downstream tasks.

By Francesco Di Salvo, Shyam Nandan Rai, Hamed Damirchi, Ignacio Meza De la Jara, Sebastian Doerrich, Marco Lents, Christian Ledig

llms diffusion

arXiv Machine Learning

Jun 11

CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting

arXiv:2511. 09789v2 Announce Type: replace Abstract: Recent advances in deep forecasting models have achieved remarkable performance, yet most approaches still struggle to provide both accurate predictions and interpretable insights into temporal dynamics.

By Fulong Yao, Wanqing Zhao, Chao Zheng, Xiaofei Han

llms benchmarks

arXiv AI

Jul 17

Pretraining Multiple Instance Learning Networks with Multi-Teacher Distillation from Pathology Slide Foundation Models

arXiv:2607. 14703v1 Announce Type: cross Abstract: Multiple instance learning (MIL) has become the main paradigm for whole-slide image (WSI) analysis in computational pathology.

By Mingxi Fu, Jiawen Li, Renao Yan, Jiali Hu, Qiehe Sun, Tian Guan, Yonghong He

llms computer-vision fine-tuning efficiency benchmarks

arXiv AI

Jun 19

Calibration Without Comprehension: Diagnosing the Limits of Fine-Tuning LLMs for Vulnerability Detection in Systems Software

arXiv:2606. 20502v1 Announce Type: cross Abstract: Whether LLMs scoring well on vulnerability benchmarks genuinely reason about security or merely pattern-match on contaminated data remains unresolved.

By Arastoo Zibaeirad, Marco Vieira

llms fine-tuning benchmarks

arXiv AI

2d ago

PEFT-MuTS: A Multivariate Parameter-Efficient Fine-Tuning Framework for Remaining Useful Life Prediction based on Cross-domain Time Series Representation Model

arXiv:2601. 22631v2 Announce Type: replace-cross Abstract: The application of data-driven remaining useful life (RUL) prediction has long been constrained by the availability of large amount of degradation data.

By En Fu, Yanyan Hu, Zengwang Jin, Kaixiang Peng

fine-tuning

arXiv Machine Learning

Jul 27

A Leakage-Free Stacked Ensemble Method for Multiclass Classification

arXiv:2607. 22081v1 Announce Type: new Abstract: Multiclass classification is a fundamental problem across a wide range of domains.

By S. P. Sharmila, Aruna Tiwari

arXiv AI

Jun 24

Lightweight Transformer Models for On-Device Fault Detection: A Benchmark Study on Resource-Constrained Deployment

arXiv:2606. 24173v1 Announce Type: cross Abstract: On-device fault detection enables real-time diagnostics without cloud dependency, but deploying machine learning models on resource-constrained hardware demands careful tradeoffs between accuracy, latency, and model size.

By Disha Patel

llms efficiency benchmarks

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