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Found 11 papers, 2 papers with code
Self-evolving Autoencoder Embedded Q-Network
During the evolution of the autoencoder architecture, a bias-variance regulatory strategy is employed to elicit the optimal response from the RL agent.
Evolving Restricted Boltzmann Machine-Kohonen Network for Online Clustering
A novel online clustering algorithm is presented where an Evolving Restricted Boltzmann Machine (ERBM) is embedded with a Kohonen Network called ERBM-KNet.
Bayesian optimized physics-informed neural network for estimating wave propagation velocities
The inverse estimation capability of the proposed approach is tested in three different isotropic media with different wave velocities.
Quantile Online Learning for Semiconductor Failure Analysis
Our proposed method achieved an overall accuracy of 86. 66% and compared with the second-best existing method it improves 15. 50% on the GAA-FET dislocation defect dataset.
Towards deep generation of guided wave representations for composite materials
The forward physics-based models are utilized to map from elastic properties space to wave propagation behavior in a laminated composite material.
Tackling Data Scarcity with Transfer Learning: A Case Study of Thickness Characterization from Optical Spectra of Perovskite Thin Films
Transfer learning increasingly becomes an important tool in handling data scarcity often encountered in machine learning.
DRBM-ClustNet: A Deep Restricted Boltzmann-Kohonen Architecture for Data Clustering
A Bayesian Deep Restricted Boltzmann-Kohonen architecture for data clustering termed as DRBM-ClustNet is proposed.
Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion
In this work, ultrasonic guided waves and a dual-branch version of convolutional neural networks are used to solve two different but related inverse problems, i. e., finding layup sequence type and identifying material properties.
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations
In this paper, we have proposed two different unsupervised-feature learning approaches where the algorithms are trained only on the baseline scenarios to learn the distribution of baseline signals.
Robust Consensus of Higher-Order Multi-Agent Systems With Attrition and Inclusion of Agents and Switching Topologies
To solve this RAI consensus problem, first, the sufficient condition for the existence of the RAIDD protocol is obtained using the $\nu$-gap metric-based simultaneous stabilization approach.
Attention over Self-attention:Intention-aware Re-ranking with Dynamic Transformer Encoders for Recommendation
We then introduce a dynamic transformer encoder (DTE) to capture user-specific inter-item relationships among item candidates by seamlessly accommodating the learned latent user intentions via IDM.
