6 papers · 1 filter
KAN-MLP-Mixer: A comprehensive investigation of the usage of Kolmogorov-Arnold Networks (KANs) for improving IMU-based Human Activity Recognition
Mengxi Liu, Sizhen Bian, Vitor Fortes +5
Kolmogorov-Arnold Networks (KANs) have demonstrated an exceptional ability to learn complex functions on clean, low-dimensional data but struggle to maintain performance on noisy a…
Promoting Sustainable Web Agents: Benchmarking and Estimating Energy Consumption through Empirical and Theoretical Analysis
Lars Krupp, Daniel GeiÃler, Vishal Banwari +2
Web agents, like OpenAI's Operator and Google's Project Mariner, are powerful agentic systems pushing the boundaries of Large Language Models (LLM). They can autonomously interact…
TxP: Reciprocal Generation of Ground Pressure Dynamics and Activity Descriptions for Improving Human Activity Recognition
Lala Shakti Swarup Ray, Lars Krupp, Vitor Fortes Rey +3
Sensor-based human activity recognition (HAR) has predominantly focused on Inertial Measurement Units and vision data, often overlooking the capabilities unique to pressure sensors…
Talk2X -- An Open-Source Toolkit Facilitating Deployment of LLM-Powered Chatbots on the Web
Lars Krupp, Daniel GeiÃler, Peter Hevesi +3
Integrated into websites, LLM-powered chatbots offer alternative means of navigation and information retrieval, leading to a shift in how users access information on the web. Yet,…
Towards Sustainable Web Agents: A Plea for Transparency and Dedicated Metrics for Energy Consumption
Lars Krupp, Daniel GeiÃler, Paul Lukowicz +1
Improvements in the area of large language models have shifted towards the construction of models capable of using external tools and interpreting their outputs. These so-called we…
Initial Findings on Sensor based Open Vocabulary Activity Recognition via Text Embedding Inversion
Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh +1
Conventional human activity recognition (HAR) relies on classifiers trained to predict discrete activity classes, inherently limiting recognition to activities explicitly present i…