Hugging Face Trending Papers August 5, 2026

MGSB: Manifold Gated Signature Branch Pressure-Domain Baseline Architecture for Two-Phase Pipeline Flows Under Distributional Shift

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Leak detection models for multiphase pipelines often degrade when deployed under flow regimes that differ from training. Existing evaluations typically assess performance under in-distribution operating conditions, masking failures caused by regime transitions such as bubble-to-slug flow.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

safety

arXiv Machine Learning

Aug 6

MGSB: Manifold Gated Signature Branch Pressure-Domain Baseline Architecture for Two-Phase Pipeline Flows Under Distributional Shift

arXiv:2608. 04805v1 Announce Type: new Abstract: Leak detection models for multiphase pipelines often degrade when deployed under flow regimes that differ from training.

By Issah Suleiman, Sormeh Serpoosh, Nadine Elkholy, Hicham Ferroudji, Mohammad Azizur Rahman, Matthew Hamilton

safety

Hugging Face Trending Papers

Jun 29

Exploiting Local Flatness for Efficient Out-of-Distribution Detection

Detecting out-of-distribution (OOD) data is crucial for reliable machine learning deployment. Among detection strategies, post-hoc methods are particularly attractive due to their efficiency, as they operate directly on pre-trained networks without requiring retraining.

benchmarks

arXiv AI

Jun 30

Exploiting Local Flatness for Efficient Out-of-Distribution Detection

arXiv:2606. 29952v1 Announce Type: cross Abstract: Detecting out-of-distribution (OOD) data is crucial for reliable machine learning deployment.

By Seonghwan Park, Hyunji Jung, Dongyeop Lee, Namhoon Lee

benchmarks

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 23

SynSur: An end-to-end generative pipeline for synthetic industrial surface defect generation and detection

arXiv:2604. 26633v2 Announce Type: replace-cross Abstract: Industrial surface defect inspection suffers from a fundamental data bottleneck: defects are rare, annotations require expert knowledge, and collecting balanced training sets is slow and costly.

By Paul Julius K\"uhn, Mika Pommeranz, Arjan Kuijper, Saptarshi Neil Sinha

diffusion fine-tuning multimodal

arXiv Machine Learning

Jun 30

Two kinds of robustness are not the same: disentangling fault tolerance and low-SNR robustness in multi-domain event detection on real data

arXiv:2606. 29339v1 Announce Type: cross Abstract: Reliable event detection underpins induced-seismicity monitoring for Carbon dioxide Capture and Storage (CCS) and geothermal operations, distributed acoustic sensing (DAS), and industrial condition monitoring.

By Isao Kurosawa

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MGSB: Manifold Gated Signature Branch Pressure-Domain Baseline Architecture for Two-Phase Pipeline Flows Under Distributional Shift

Exploiting Local Flatness for Efficient Out-of-Distribution Detection

Exploiting Local Flatness for Efficient Out-of-Distribution Detection

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

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