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cs.LG2024

The Power of Training: How Different Neural Network Setups Influence the Energy Demand

Daniel Geißler, Bo Zhou, Mengxi Liu +2

This work offers a heuristic evaluation of the effects of variations in machine learning training regimes and learning paradigms on the energy consumption of computing, especially…

cs.LG2024

Initial Investigation of Kolmogorov-Arnold Networks (KANs) as Feature Extractors for IMU Based Human Activity Recognition

Mengxi Liu, Daniel Geißler, Dominique Nshimyimana +3

In this work, we explore the use of a novel neural network architecture, the Kolmogorov-Arnold Networks (KANs) as feature extractors for sensor-based (specifically IMU) Human Activ…

cs.LG2024

Remaining useful life prediction of Lithium-ion batteries using spatio-temporal multimodal attention networks

Sungho Suh, Dhruv Aditya Mittal, Hymalai Bello +3

Lithium-ion batteries are widely used in various applications, including electric vehicles and renewable energy storage. The prediction of the remaining useful life (RUL) of batter…

cs.LG2024

iKAN: Global Incremental Learning with KAN for Human Activity Recognition Across Heterogeneous Datasets

Mengxi Liu, Sizhen Bian, Bo Zhou +1

This work proposes an incremental learning (IL) framework for wearable sensor human activity recognition (HAR) that tackles two challenges simultaneously: catastrophic forgetting a…

cs.LG2024

BeSound: Bluetooth-Based Position Estimation Enhancing with Cross-Modality Distillation

Hymalai Bello, Sungho Suh, Bo Zhou +1

Smart factories leverage advanced technologies to optimize manufacturing processes and enhance efficiency. Implementing worker tracking systems, primarily through camera-based meth…