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Found 5 papers, 2 papers with code
Balanced Semi-Supervised Generative Adversarial Network for Damage Assessment from Low-Data Imbalanced-Class Regime
In recent years, applying deep learning (DL) to assess structural damages has gained growing popularity in vision-based structural health monitoring (SHM).
Generative Adversarial Network
Structural Health Monitoring
+1
Sample-efficient Quantum Born Machine through Coding Rate Reduction
We use the information-theoretic \textit{Maximal Coding Rate Reduction} (MCR$^2$) metric as a second moment matching tool and study its effect on mode collapse in QCBMs.
Revisiting Sparse Convolutional Model for Visual Recognition
We show that such models have equally strong empirical performance on CIFAR-10, CIFAR-100, and ImageNet datasets when compared to conventional neural networks.
Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction
In particular, we propose to learn a closed-loop transcription between a multi-class multi-dimensional data distribution and a linear discriminative representation (LDR) in the feature space that consists of multiple independent multi-dimensional linear subspaces.
Text Analytics for Resilience-Enabled Extreme Events Reconnaissance
Post-hazard reconnaissance for natural disasters (e. g., earthquakes) is important for understanding the performance of the built environment, speeding up the recovery, enhancing resilience and making informed decisions related to current and future hazards.
