7 papers · 1 filter
Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure Detection
Yousuf Moiz Ali, Jaroslaw E. Prilepsky, João Pedro +4
We propose a hybrid active-online learning framework for label-efficient concept drift adaptation in optical network failure detection. Using margin-based selective labeling, our m…
Hardware-Oriented Inference Complexity of Kolmogorov-Arnold Networks
Bilal Khalid, Pedro Freire, Sergei K. Turitsyn +1
Kolmogorov-Arnold Networks (KANs) have recently emerged as a powerful architecture for various machine learning applications. However, their unique structure raises significant con…
Experimental Demonstration of Online Learning-Based Concept Drift Adaptation for Failure Detection in Optical Networks
Yousuf Moiz Ali, Jaroslaw E. Prilepsky, João Pedro +4
We present a novel online learning-based approach for concept drift adaptation in optical network failure detection, achieving up to a 70% improvement in performance over conventio…
From Data to Decision: A Multi-Stage Framework for Class Imbalance Mitigation in Optical Network Failure Analysis
Yousuf Moiz Ali, Jaroslaw E. Prilepsky, Nicola Sambo +5
Machine learning-based failure management in optical networks has gained significant attention in recent years. However, severe class imbalance, where normal instances vastly outnu…
Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks
Yousuf Moiz Ali, Jaroslaw E. Prilepsky, Nicola Sambo +5
We compare pre-, in-, and post-processing techniques for class imbalance mitigation in optical network failure detection. Threshold Adjustment achieves the highest F1 gain (15.3%),…
Improving Analog Neural Network Robustness: A Noise-Agnostic Approach with Explainable Regularizations
Alice Duque, Pedro Freire, Egor Manuylovich +3
This work tackles the critical challenge of mitigating "hardware noise" in deep analog neural networks, a major obstacle in advancing analog signal processing devices. We propose a…