19 citations · 21 across the 6 of their papers we have counts for
8 papers
IO Factory: Simulating AI-Enabled Influence Campaigns at Scale
Lukasz Olejnik, Wenchao Dong, Jonas R. Kunst +4
We introduce IO Factory, an AI-driven framework for simulating information and influence campaigns as fully integrated, traceable processes. The threat of digital manipulation now…
Unreliable Uncertainty Estimates with Monte Carlo Dropout
Aslak Djupskås, Alexander Johannes Stasik, Signe Riemer-Sørensen
Reliable uncertainty estimation is crucial for machine learning models, especially in safety-critical domains. While exact Bayesian inference offers a principled approach, it is of…
Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems
Aleksandra Jekic, Afroditi Natsaridou, Signe Riemer-Sørensen +2
Approximating solutions to partial differential equations (PDEs) is fundamental for the modeling of dynamical systems in science and engineering. Physics-informed neural networks (…
Road Graph Generator: Mapping roads at construction sites from GPS data
Katarzyna Michałowska, Helga Margrete Bodahl Holmestad, Signe Riemer-Sørensen
We propose a new method for inferring roads from GPS trajectories to map construction sites. This task presents a unique challenge due to the erratic and non-standard movement patt…
Machine Learning based Pointing Models for Radio/Sub-millimeter Telescopes
Bendik Nyheim, Signe Riemer-Sørensen, Rodrigo Parra +1
Radio, sub-millimeter and millimeter ground-based telescopes are powerful instruments for studying the gas and dust-rich regions of the Universe that are invisible at optical wavel…
DON-LSTM: Multi-Resolution Learning with DeepONets and Long Short-Term Memory Neural Networks
Katarzyna Michałowska, Somdatta Goswami, George Em Karniadakis +1
Deep operator networks (DeepONets, DONs) offer a distinct advantage over traditional neural networks in their ability to be trained on multi-resolution data. This property becomes…