Zobrazeno 1 - 10
of 164
pro vyhledávání: '"Geißler, Daniel."'
Autor:
Geissler, Daniel, Nshimyimana, Dominique, Rey, Vitor Fortes, Suh, Sungho, Zhou, Bo, Lukowicz, Paul
The research of machine learning (ML) algorithms for human activity recognition (HAR) has made significant progress with publicly available datasets. However, most research prioritizes statistical metrics over examining negative sample details. While
Externí odkaz:
http://arxiv.org/abs/2412.09037
A fundamental step in the development of machine learning models commonly involves the tuning of hyperparameters, often leading to multiple model training runs to work out the best-performing configuration. As machine learning tasks and models grow i
Externí odkaz:
http://arxiv.org/abs/2412.08526
Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces
Supervised machine learning often operates on the data-driven paradigm, wherein internal model parameters are autonomously optimized to converge predicted outputs with the ground truth, devoid of explicitly programming rules or a priori assumptions.
Externí odkaz:
http://arxiv.org/abs/2412.08515
Human Activity Recognition using time-series data from wearable sensors poses unique challenges due to complex temporal dependencies, sensor noise, placement variability, and diverse human behaviors. These factors, combined with the nontransparent na
Externí odkaz:
http://arxiv.org/abs/2412.08507
Autor:
Bello, Hymalai, Geißler, Daniel, Ray, Lala, Müller-Divéky, Stefan, Müller, Peter, Kittrell, Shannon, Liu, Mengxi, Zhou, Bo, Lukowicz, Paul
Artificial Intelligence (AI) methods are powerful tools for various domains, including critical fields such as avionics, where certification is required to achieve and maintain an acceptable level of safety. General solutions for safety-critical syst
Externí odkaz:
http://arxiv.org/abs/2409.08666
Smaller machine learning models, with less complex architectures and sensor inputs, can benefit wearable sensor-based human activity recognition (HAR) systems in many ways, from complexity and cost to battery life. In the specific case of smart facto
Externí odkaz:
http://arxiv.org/abs/2408.14146
Despite the widespread integration of ambient light sensors (ALS) in smart devices commonly used for screen brightness adaptation, their application in human activity recognition (HAR), primarily through body-worn ALS, is largely unexplored. In this
Externí odkaz:
http://arxiv.org/abs/2408.09527
Large Language Models (LLMs) have made significant advances in natural language processing, but their underlying mechanisms are often misunderstood. Despite exhibiting coherent answers and apparent reasoning behaviors, LLMs rely on statistical patter
Externí odkaz:
http://arxiv.org/abs/2408.01168
Autor:
Geissler, Daniel, Lukowicz, Paul
Hybrid intelligence aims to enhance decision-making, problem-solving, and overall system performance by combining the strengths of both, human cognitive abilities and artificial intelligence. With the rise of Large Language Models (LLM), progressivel
Externí odkaz:
http://arxiv.org/abs/2407.10580
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 Activity Recognition (HAR). Where conventional networks perform a parameter
Externí odkaz:
http://arxiv.org/abs/2406.11914