2 citations · 2 across the 3 of their papers we have counts for
4 papers
The Real Cost of Context: Predicting Compute Time and Energy Consumption in LLM Prefill and Decode Phases
Lars Krupp, Daniel Geißler, Francisco M. Calatrava-Nicolas +3
The integration of Large Language Models (LLMs) into agentic architectures has substantially increased token consumption, raising concerns about their energy footprint. Yet, estima…
This Is Taking Too Long -- Investigating Time as a Proxy for Energy Consumption of LLMs
Lars Krupp, Daniel Geißler, Francisco M. Calatrava-Nicolas +3
The energy consumption of Large Language Models (LLMs) is raising growing concerns due to their adverse effects on environmental stability and resource use. Yet, these energy costs…
Embedded Inter-Subject Variability in Adversarial Learning for Inertial Sensor-Based Human Activity Recognition
Francisco M. Calatrava-Nicolás, Shoko Miyauchi, Vitor Fortes Rey +3
This paper addresses the problem of Human Activity Recognition (HAR) using data from wearable inertial sensors. An important challenge in HAR is the model's generalization capabili…
Deep Adversarial Learning with Activity-Based User Discrimination Task for Human Activity Recognition
Francisco M. Calatrava-Nicolás, Shoko Miyauchi, Oscar Martinez Mozos
We present a new adversarial deep learning framework for the problem of human activity recognition (HAR) using inertial sensors worn by people. Our framework incorporates a novel a…