1 citations · 3 across the 9 of their papers we have counts for
9 papers
Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance
Pei-Hsuan Hsia, Lars H. Heyen, Arvid Weyrauch +4
In Bayesian neural networks (BNNs), variational inference is a widely adopted framework for modeling uncertainty in a distributional way, with the evidence lower bound (ELBO) servi…
Exploring Federated Learning for Thermal Urban Feature Segmentation -- A Comparison of Centralized and Decentralized Approaches
Leonhard Duda, Khadijeh Alibabaei, Elena Vollmer +11
Federated Learning (FL) is an approach for training a shared Machine Learning (ML) model with distributed training data and multiple participants. FL allows bypassing limitations o…
pyGinkgo: A Sparse Linear Algebra Operator Framework for Python
Keshvi Tuteja, Gregor Olenik, Roman Mishchuk +5
Sparse linear algebra is a cornerstone of many scientific computing and machine learning applications. Python has become a popular choice for these applications due to its simplici…
Energy Consumption in Parallel Neural Network Training
Philipp Huber, David Li, Juan Pedro Gutiérrez Hermosillo Muriedas +4
The increasing demand for computational resources of training neural networks leads to a concerning growth in energy consumption. While parallelization has enabled upscaling model…
Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism
Deifilia Kieckhefen, Markus Götz, Lars H. Heyen +2
AI-based methods have revolutionized atmospheric forecasting, with recent successes in medium-range forecasting spurring the development of climate foundation models. Accurate mode…
Model Fusion via Neuron Transplantation
Muhammed Öz, Nicholas Kiefer, Charlotte Debus +3
Ensemble learning is a widespread technique to improve the prediction performance of neural networks. However, it comes at the price of increased memory and inference time. In this…
A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting
Nicholas Kiefer, Arvid Weyrauch, Muhammed Öz +3
The current landscape in time-series forecasting is dominated by Transformer-based models. Their high parameter count and corresponding demand in computational resources pose a cha…
ReCycle: Fast and Efficient Long Time Series Forecasting with Residual Cyclic Transformers
Arvid Weyrauch, Thomas Steens, Oskar Taubert +6
Transformers have recently gained prominence in long time series forecasting by elevating accuracies in a variety of use cases. Regrettably, in the race for better predictive perfo…
Harnessing Orthogonality to Train Low-Rank Neural Networks
Daniel Coquelin, Katharina Flügel, Marie Weiel +4
This study explores the learning dynamics of neural networks by analyzing the singular value decomposition (SVD) of their weights throughout training. Our investigation reveals tha…