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Found 8 papers, 2 papers with code
A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially Correlated Faults with Application to Multistation Assembly Systems
This article proposes a novel fault diagnosis method: clustering spatially correlated sparse Bayesian learning (CSSBL), and explicitly demonstrates its applicability in a multistation assembly system that is vulnerable to the above challenges.
On Model Compression for Neural Networks: Framework, Algorithm, and Convergence Guarantee
This paper focuses on two model compression techniques: low-rank approximation and weight pruning in neural networks, which are very popular nowadays.
A Novel Sparse Bayesian Learning and Its Application to Fault Diagnosis for Multistation Assembly Systems
Fault diagnosis is to identify process faults that cause the excessive dimensional variation of the product using dimensional measurements.
Anomaly Detection in Additive Manufacturing Processes using Supervised Classification with Imbalanced Sensor Data based on Generative Adversarial Network
It is beneficial to generate effective artificial sample data for the abnormal states to make a more balanced training set.
Reinforcement Learning-based Defect Mitigation for Quality Assurance of Additive Manufacturing
Since AM typically fabricates a small number of customized products, this paper aims to create an online learning-based strategy to mitigate the new defects in AM process while minimizing the number of samples needed.
Self-scalable Tanh (Stan): Faster Convergence and Better Generalization in Physics-informed Neural Networks
To address this, a Self-scalable tanh (Stan) activation function is proposed for the PINNs.
HAA500: Human-Centric Atomic Action Dataset with Curated Videos
We contribute HAA500, a manually annotated human-centric atomic action dataset for action recognition on 500 classes with over 591K labeled frames.
Ranked #1 on
Action Recognition
on HAA500
CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement
In this paper, we propose a novel approach to address the high-resolution segmentation problem without using any high-resolution training data.
Ranked #1 on
Semantic Segmentation
on BIG
(using extra training data)
